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

Abstract

The invention provides a multi-source landslide monitoring data coordinate unification method, device and equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining the actual monitoring data of a landslide region and the coordinates of a target key point under a CGCS2000 coordinate reference through GNSS equipment, a laser radar measurement unmanned plane, a synthetic aperture radar and an engineering measurement instrument; constructing a key point coordinate mapping relation through coordinates under the original coordinate reference and the CGCS2000 coordinate reference; based on neighborhood density normalization, obtaining a seven-parameter coordinate conversion model through a key point coordinate mapping relation and a geographic weighting coefficient; based on a mask rule, filtering pixels outside the landslide area and pixels with low signal-to-noise ratios in all the remaining point coordinates; and inputting all filtered remaining point coordinates into a seven-parameter coordinate conversion model to obtain target CGCS2000 coordinate data. According to the invention, the coordinate conversion precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, device, equipment and storage medium for unifying coordinates of multi-source landslide monitoring data. Background Art

[0002] Landslide is a common geological disaster that poses a serious threat to human life and property. In order to effectively prevent and respond to landslide disasters, landslide monitoring technology has become particularly important. At present, the displacement changes in the landslide area are mainly monitored to detect early signs of landslides in time and take corresponding preventive measures.

[0003] At present, there are many types of monitoring equipment used in landslide displacement field monitoring methods. Each device has its own coordinate reference, and different monitoring devices use different coordinate references. Since each monitoring method has its own coordinate system and there is no unified coordinate reference, it is difficult to directly superimpose and compare data from different coordinate systems, which affects the accuracy of comprehensive analysis. Manual coordinate conversion may have errors, and multiple conversions may lead to error accumulation, reducing the reliability of the data. Summary of the invention

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

[0005] In order to solve the above problems, the present invention provides a method, device, equipment and storage medium for unifying coordinates of multi-source landslide monitoring data.

[0006] In a first aspect, the present invention provides a method for unifying coordinates of multi-source landslide monitoring data, comprising:

[0007] The actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference are obtained by GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering measurement instruments, wherein the actual monitoring data includes the coordinates of the target key points in the original coordinate reference and the coordinates of all remaining points;

[0008] Constructing a key point coordinate mapping relationship through the coordinates of the target key point in the original coordinate reference and the coordinates of the target key point in the CGCS2000 coordinate reference;

[0009] Based on neighborhood density normalization, a seven-parameter coordinate transformation model is obtained through the key point coordinate mapping relationship and geographic weighting coefficients;

[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 refer to the pixels with a 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, the seven-parameter coordinate transformation model is obtained through the key point coordinate mapping relationship and the geographic weighting coefficient, including:

[0013] The geographic weighting coefficient is obtained by determining the degree of geographic distribution sparseness of key points;

[0014] Obtaining the seven-parameter coordinate transformation model according to the key point coordinate mapping relationship and the geographic weighting coefficient;

[0015] Wherein, the seven-parameter coordinate transformation model is:

[0016]

[0017] Among them, X i , Y i , Z i is the three-dimensional coordinate value of the i-th key point in the CGCS2000 coordinate system, w i is the geographic weighting coefficient of the i-th key point, ΔX0, ΔY0, ΔZ0 are the translation coefficients for converting the coordinates from the original coordinate system to the CGCS2000 coordinate system, m is the scale coefficient, ω X ,ω Y ,ω Z The rotation coefficient for converting coordinates from the original coordinate system to the CGCS2000 coordinate system, X′ i , Y′ i , Z′ i is the three-dimensional coordinate value of the i-th key point in the original coordinate system.

[0018] Optionally, obtaining the geographic weighting coefficient according to the sparseness of geographical distribution of key points includes:

[0019] Obtaining the neighborhood density of key points by the sparsity of geographical distribution of the key points;

[0020] Among them, the neighborhood density of the key point is:

[0021]

[0022] Among them, D i is the key point neighborhood density of the i-th key point, N i is the spatial Euclidean distance between the i-th key point and the nearest key point;

[0023] Obtaining the geographic weighting coefficient according to the neighborhood density of the key point;

[0024] Wherein, the geographical weighting coefficient is:

[0025]

[0026] Among them, w i is the geographic weighting coefficient of the ith key point, D min is the minimum value of the neighborhood density of all key points, D max is the maximum value of the key point neighborhood density of all key points.

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

[0028] Multiplying the matrix composed of all the remaining point coordinates with the regional mask matrix correspondingly to obtain regional mask processing data, wherein the regional mask matrix is ​​used to mark the pixels located in the landslide area;

[0029] The regional mask processing data is filtered through a signal-to-noise ratio mask rule to obtain the filtered coordinates of all remaining points, wherein the signal-to-noise ratio mask rule is used to filter pixels with a low signal-to-noise ratio according to the signal-to-noise ratio.

[0030] Optionally, the actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference are obtained by using GNSS equipment, a laser radar measurement drone, a synthetic aperture radar and an engineering measurement instrument, including:

[0031] The landslide area is monitored by the laser radar measurement drone, the synthetic aperture radar and the engineering measurement instrument to obtain the actual monitoring data;

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

[0033] Optionally, the GNSS device includes an RTK device connected to a BeiDou ground-based augmentation system, and the step of obtaining the coordinates of the target key points of the landslide area in the CGCS2000 coordinate reference by using the GNSS device, the laser radar measurement drone, the synthetic aperture radar and the engineering measurement instrument includes:

[0034] Obtaining the key point coordinates of the laser radar surveying drone CGCS2000 and the key point coordinates of the synthetic aperture radar CGCS2000 of the landslide area through the RTK device connected to the Beidou ground-based augmentation system, the laser radar surveying drone and the synthetic aperture radar;

[0035] The key point coordinates of the engineering surveying instrument CGCS2000 in the landslide area are obtained by deploying a forced centering observation pier and performing joint measurement with the engineering surveying instrument.

[0036] Optionally, the engineering surveying instrument includes a total station and a level, the key point coordinates of the engineering surveying instrument CGCS2000 include key point plane coordinates and key point elevation coordinates, and the key point coordinates of the engineering surveying instrument CGCS2000 in the landslide area are obtained by arranging a forced centering observation pier and performing joint measurement with the engineering surveying instrument, including:

[0037] The plane coordinates of the key points are obtained by placing the forced centering observation pier and the total station at the entrance of the landslide tunnel to perform combined traverse measurement;

[0038] The elevation coordinates of the key points are obtained by arranging the forced centering observation pier and the level at the entrance of the landslide tunnel to perform closed-route leveling measurement.

[0039] In a second aspect, the present invention provides a device for unifying coordinates of multi-source landslide monitoring data, comprising:

[0040] A data acquisition module is used to acquire the actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference through GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering measurement instruments, wherein the actual monitoring data includes the coordinates of the target key points in the original coordinate reference and the coordinates of all remaining points;

[0041] A mapping relationship acquisition module, used for constructing a key point coordinate mapping relationship through the coordinates of the target key point in the original coordinate reference and the coordinates of the target key point in the CGCS2000 coordinate reference;

[0042] A coordinate transformation model acquisition module, used to obtain a seven-parameter coordinate transformation model through the key point coordinate mapping relationship and geographic weighting coefficient based on neighborhood density normalization;

[0043] A data filtering module, for filtering pixels outside the landslide area and pixels with low signal-to-noise ratio in the remaining point coordinates based on mask rules to obtain the filtered remaining point coordinates, wherein the pixels with low signal-to-noise ratio represent 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 conversion model to obtain target CGCS2000 coordinate data.

[0045] In a third aspect, 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 used to implement the multi-source landslide monitoring data unified coordinate method as described in the first aspect when executing the computer program.

[0048] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for unifying coordinates of multi-source landslide monitoring data as described in the first aspect is implemented.

[0049] The beneficial effects of the multi-source landslide monitoring data unified coordinate method, device, equipment and storage medium of the present invention are: the actual monitoring data of the landslide area and the coordinates of the target key points under the CGCS2000 coordinate reference are obtained through GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering measurement instruments, and the actual monitoring data include the coordinates of the target key points under the original coordinate reference and the coordinates of all remaining points. The key point coordinate mapping relationship is established through the correspondence between 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. Based on neighborhood density normalization, considering that the geographic distribution density will cause model deviation, the geographic weighting coefficient is introduced to obtain a seven-parameter coordinate transformation model, so that the model is more in line with the landslide coordinate transformation scenario. Based on the mask rule, the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining point coordinates are filtered to obtain the filtered coordinates of all remaining points, thereby improving data quality and reducing subsequent data processing costs. The coordinates of all remaining points after filtering are input into the seven-parameter coordinate conversion model to obtain the target CGCS2000 coordinate data, and the coordinate reference of multi-source data landslide monitoring is converted to the CGCS2000 coordinate reference to improve the accuracy of coordinate conversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of a flow chart of a method for unifying coordinates of multi-source landslide monitoring data according to an embodiment of the present invention;

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

[0052] Figure 3 A schematic diagram of geographical weighting coefficients according to an embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of a process of filtering all remaining point coordinates according to an embodiment of the present invention;

[0054] Figure 5 A schematic diagram of a landslide displacement field monitoring scene and instrument equipment according to an embodiment of the present invention;

[0055] Figure 6 This is a plan view of the image control mark of the laser radar measurement drone according to an embodiment of the present invention;

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

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

[0058] Fig. 9 A side view of a ground-based synthetic aperture radar according to an embodiment of the present invention;

[0059] Fig.10 A schematic diagram of the vicinity of a landslide tunnel entrance according to an embodiment of the present invention;

[0060] Fig.11 It is a structural schematic diagram of a device for unifying coordinates of multi-source landslide monitoring data according to an embodiment of the present invention;

[0061] Fig.12 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes 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. In addition, 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 "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0065] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

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

[0067] In the related technologies, the existing landslide displacement field monitoring by surveying and mapping means is mostly limited to one of the surface or deep layers, and cannot make 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] In view of the problems existing in the above-mentioned related technologies, this embodiment provides a method, device, equipment and storage medium for unifying coordinates of multi-source landslide monitoring data.

[0069] like Figure 1 As shown, a method for unifying coordinates of multi-source landslide monitoring data provided by an embodiment of the present invention includes:

[0070] Step 110, obtaining the actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference through GNSS equipment, lidar measurement drones, synthetic aperture radars and engineering surveying instruments, wherein the actual monitoring data includes the coordinates of the target key points in the original coordinate reference 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, speed and time. A LiDAR surveying drone is an unmanned aerial vehicle that integrates a LiDAR (laser radar) sensor for efficient and accurate collection of three-dimensional geographic information. Synthetic aperture radar is an active remote sensing technology that forms images by emitting microwaves and receiving signals reflected from ground objects. Engineering surveying instruments are professional equipment used to measure and determine parameters such as the geometric position, distance, angle, and elevation of ground points. The original coordinate reference includes but is not limited to the regional independent coordinate reference, the engineering independent coordinate reference, and the radar coordinate reference. The number of target key points is at least four, and the coordinates of all remaining points represent the coordinates of other key points in the landslide area except the target key points under the original coordinate reference.

[0072] Step 120, constructing a key point coordinate mapping relationship through the coordinates of the target key point in the original coordinate reference and the coordinates of the target key point in the CGCS2000 coordinate reference.

[0073] Specifically, based on the key point coordinates of the LiDAR measurement UAV, synthetic aperture radar and engineering measurement monitoring means in their respective original coordinate references, as well as the key point coordinates of these three monitoring means in the CGCS2000 coordinate reference, the 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 the geographic weighting coefficient.

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

[0076] Step 140, based on the mask rule, filtering the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining all point coordinates to obtain the filtered remaining all point coordinates, wherein the pixels with low signal-to-noise ratio refer to the pixels with a signal-to-noise ratio less than a preset threshold.

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

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

[0079] Specifically, the remaining coordinates of all the points after filtering are input into the seven-parameter coordinate conversion model, and the remaining coordinates of the points under different original coordinate references are uniformly converted to the coordinates under the CGCS2000 coordinate reference. The spatial rectangular coordinates corresponding to the original coordinate reference of the multi-source monitoring means are converted one by one into the spatial rectangular coordinates under the CGCS2000 coordinate reference.

[0080] In this embodiment, the actual monitoring data of the landslide area and the coordinates of the target key points under the CGCS2000 coordinate reference are obtained through GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering surveying instruments. The actual monitoring data includes the coordinates of the target key points under the original coordinate reference and the coordinates of all remaining points. The key point coordinate mapping relationship is established through the correspondence between 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. Based on neighborhood density normalization, considering that the geographic distribution density will cause model deviation, the geographic weighting coefficient is introduced to obtain a seven-parameter coordinate transformation model, so that the model is more in line with the landslide coordinate transformation scenario. Based on the mask rule, the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining point coordinates are filtered to obtain the filtered coordinates of all the remaining points, thereby improving data quality and reducing subsequent data processing costs. The coordinates of all remaining points after filtering are input into the seven-parameter coordinate conversion model to obtain the target CGCS2000 coordinate data, and the coordinate reference of multi-source data landslide monitoring is converted to the CGCS2000 coordinate reference to improve the accuracy of coordinate conversion.

[0081] Optionally, the seven-parameter coordinate transformation model is obtained through the key point coordinate mapping relationship and the geographic weighting coefficient, including:

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

[0083] Obtaining the seven-parameter coordinate transformation model according to the key point coordinate mapping relationship and the geographic weighting coefficient;

[0084] Wherein, the seven-parameter coordinate transformation model is:

[0085]

[0086] Among them, X i , Y i , Z i is the three-dimensional coordinate value of the i-th key point in the CGCS2000 coordinate system, wi is the geographic weighting coefficient of the i-th key point, ΔX0, ΔY0, ΔZ0 are the translation coefficients for converting the coordinates from the original coordinate system to the CGCS2000 coordinate system, m is the scale coefficient, ω X ,ω Y ,ω Z The rotation coefficient for converting coordinates from the original coordinate system to the CGCS2000 coordinate system, X′ i , Y′ i , Z′ i is the three-dimensional coordinate value of the i-th key point in the original coordinate system.

[0087] Specifically, in the actual application scenario of landslide displacement field monitoring, due to the uneven geographical distribution of key points on the surface or underground, points in different regions may have different conversion errors due to factors such as data accuracy, terrain complexity, and acquisition methods. Therefore, the idea of ​​geographic weighting is introduced to assign a geographic weighting coefficient to each key point to reflect its influence on the overall conversion model during the conversion process.

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

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

[0090] Optionally, obtaining the geographic weighting coefficient according to the sparseness of geographical distribution of key points includes:

[0091] Obtaining the neighborhood density of key points by the sparsity of geographical distribution of the key points;

[0092] Among them, the neighborhood density of the key point is:

[0093]

[0094] Among them, D i is the key point neighborhood density of the i-th key point, N i is the spatial Euclidean distance between the i-th key point and the nearest key point;

[0095] Obtaining the geographic weighting coefficient according to the neighborhood density of the key point;

[0096] Wherein, the geographical weighting coefficient is:

[0097]

[0098] Among them, w i is the geographic weighting coefficient of the ith key point, D min is the minimum value of the neighborhood density of all key points, D max is the maximum value of the key point neighborhood density of all key points.

[0099] Specifically, to quantify the sparseness of the geographical distribution of key points, the neighborhood density of each point is calculated. Assuming that a certain monitoring method has N key points, each key point P i The distance to the nearest keypoint is used as a density measure. i The key point neighborhood density D i for:

[0100]

[0101] Among them, D i is the key point neighborhood density of the i-th key point, N i is the spatial Euclidean distance between the ith key point and the nearest key point, and the nearest key point is the key point closest to the ith key point.

[0102] The neighborhood density normalization formula is used to perform geographic weighting for key points in the seven-parameter coordinate transformation, and the geographic weighting of all key points is standardized in the interval [0,1]. The geographic weighting coefficient is obtained according to the neighborhood density of the key points, where the geographic weighting coefficient is:

[0103]

[0104] Among them, w i is the geographic weighting coefficient of the ith key point, D min is the minimum value of the neighborhood density of the key points among all key points, Dmax It is the maximum value of the neighborhood density of the key point among all key points.

[0105] Assume that the spatial Euclidean distance between the sparsest key point and the nearest key point in the landslide area is N a =300, the spatial Euclidean distance between the densest key point and the nearest key point is N b =50, we can get w a =1,w b =0, and the geographic weighted coefficients of the other key points are all in the interval [0,1]. This formula can meet the actual needs of the landslide displacement field monitoring application scenario.

[0106] After solving the geographical weighting principle and the corresponding algorithm formula, in order to solve the seven parameters of coordinate transformation, it is necessary to use the least squares method to minimize the transformed CGCS2000 coordinates (X i Y i Z i ) T With the original coordinates (X′ i Y′ i Z′ i ) T The error between them needs to minimize the following objective function:

[0107]

[0108] in, It means to minimize the sum of the objective functions from the 1st to the nth key points, w i is the key point P i The geographic weighting coefficient, m is the scale factor 1.00000, X i , Y i , Z i It is the key point P i The three-dimensional coordinate value in the original coordinate system, X′ i , Y′ i , Z′ i is the three-dimensional coordinate value of the key point Pi in the CGCS2000 coordinate system, m is the scale factor, and the prior value is 1.00000.

[0109] Since the scale coefficient has a priori value, in fact, only the three translation coefficients and three rotation coefficients in the formula need to be solved, and the necessary number of observations is 6, corresponding to the three-dimensional coordinate pairs of two key points. At least 4 key points are required for each monitoring method, and 6 redundant observations can be formed in the solution, which is helpful to improve the accuracy of the coordinate transformation parameters and eliminate the gross errors of individual coordinate data.

[0110] By taking the derivative of the objective function through the corresponding interface of Matlab software, the coordinate transformation parameter ω can be established.X ω Y ω Z The linear equation system of ΔX0 ΔY0 ΔZ0 is finally used to obtain the key parameters of the seven-parameter transformation model taking into account geographical weighting.

[0111] In some more specific embodiments, in combination Figure 3 As shown in the figure, the parallelogram area is the landslide area, and the circular points are the key points. Their geographical distribution is uneven, some are sparse and some are dense. The elliptical area is a key point P i Neighborhood, radius N i The arc above the point represents the geographic weighted function, and the height w of the arc directly above the point i is the geographic weighting coefficient. As can be seen from the figure, the larger the neighborhood (i.e., the sparser the points), the larger the geographic weighting coefficient; and the smaller the neighborhood (i.e., the denser the points), the smaller the geographic weighting coefficient.

[0112] In this optional embodiment, based on non-ideal conditions such as landslide landforms and vegetation coverage, the geographical distribution of key points for various monitoring means of landslide displacement field is extremely uneven. The plane distribution and elevation distribution uniformity of surface monitoring means in multi-source data are acceptable, but the overall number is low. The underground monitoring means are mainly concentrated in landslide tunnels. The uniformity of elevation distribution of key points is extremely weak, and the uniformity of plane distribution is relatively weak. There are large blank areas in the landslide body for surface monitoring means and underground monitoring means. For key points with large geographical distribution density, the spatial information redundancy is large due to the small point spacing. If all key points are equally included in the coordinate transformation model, the model may pay too much attention to the area. Therefore, it is necessary to balance the influence of high-density data by reducing the weights of these points to avoid model deviation in the data fusion process. Relatively speaking, from the perspective of landslide displacement field monitoring, areas with low density of key points in the landslide area are often greatly affected by unstable factors such as vegetation coverage and shallow surface soil conservation. These factors are closely related to landslide disasters and need to be fully considered. Therefore, for key points with low distribution density, their geographical weighting coefficients should be increased accordingly. By reasonably adjusting the geographical weighted coefficients of different key points, the distribution differences of key points in various regions within the landslide area are fully considered, and the seven unknown parameters in the coordinate transformation model are reasonably involved in the solution.

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

[0114] Multiplying the matrix composed of all the remaining point coordinates with the regional mask matrix correspondingly to obtain regional mask processing data, wherein the regional mask matrix is ​​used to mark the pixels located in the landslide area;

[0115] The regional mask processing data is filtered through a signal-to-noise ratio mask rule to obtain the filtered coordinates of all remaining points, wherein the signal-to-noise ratio mask rule is used to filter pixels with a low signal-to-noise ratio according to the signal-to-noise ratio.

[0116] Specifically, the remaining all point coordinates include the remaining point coordinates of the laser radar measurement drone, the remaining point coordinates of the synthetic aperture radar, and the remaining point coordinates of the engineering measurement instrument. The filtering of the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining all point coordinates to obtain the filtered remaining all point coordinates includes:

[0117] Filtering the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining point coordinates of the laser radar surveying drone and the remaining point coordinates of the synthetic aperture radar to obtain the filtered remaining point coordinates of the laser radar surveying drone and the remaining point coordinates of the synthetic aperture radar;

[0118] The remaining point coordinates of the engineering surveying instrument, the filtered remaining point coordinates of the laser radar surveying drone and the remaining point coordinates of the synthetic aperture radar are taken as the remaining all point coordinates after filtering.

[0119] Different from the "point-to-point" monitoring of GNSS and engineering surveying, the original observation data of LiDAR survey drones and synthetic aperture radars will inevitably include some areas outside the landslide monitoring area, such as water areas, towns, farmland, etc. The observation data in these areas are useless for monitoring the landslide displacement field, which increases the computing time and computing power resources in the subsequent data solution; in addition, in the subsequent data solution, the software program may mistakenly select reference positioning points in these areas for atmospheric model correction, affecting the atmospheric model correction effect and reducing the overall solution accuracy. Therefore, it is necessary to filter and eliminate the pixels in other areas outside the landslide area in the monitoring data obtained by LiDAR survey drones and synthetic aperture radars. Since each pixel in the original observation data of LiDAR and synthetic aperture radar contains the coordinate field and radar echo signal-to-noise ratio field, it is feasible to filter and eliminate pixels outside the landslide and low signal-to-noise ratio pixels by formulating mask rules. Through the interface provided by Matlab software, or the corresponding monitoring means data post-processing software provides relevant functional operations, the interactive operation interface formulates the regional mask matrix and signal-to-noise ratio mask rules composed of 0 and 1. Each unit of the original observation data matrix is ​​multiplied one by one with the corresponding unit of the regional mask matrix, and then the signal-to-noise ratio mask rule is used to output the data filtered by the mask matrix.

[0120] In some more specific embodiments, in combination Figure 4As shown, the matrix of all remaining point coordinates is a 4*4 plane matrix, and each value in the matrix represents the signal-to-noise ratio of the pixel. The matrix A of 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 ​​derived by software, which is composed of 0 and 1, where the cell is 0 represents the non-landslide area, and the cell is 1 represents the landslide area. The landslide area mask matrix B is (0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0). The matrix of all remaining point coordinates and the landslide area mask matrix are multiplied one by one, and the non-landslide pixels are filtered out. The pixels in the non-landslide area have become 0 values. The signal-to-noise ratio mask rule is that if the signal-to-noise ratio value is less than 20, it is modified to 0, otherwise, the original signal-to-noise ratio value is output. After filtering out non-landslide pixels, the matrix with low signal-to-noise ratio is eliminated again through the signal-to-noise ratio mask rule. At this time, the observation data represented by the matrix has completed the filtering and elimination of non-landslide area pixels and low signal-to-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 mask rules for obtaining monitoring data from laser radar measurement drones and synthetic aperture radars include two filtering dimensions: signal-to-noise ratio and monitoring area. This improves the clarity and accuracy of the data, avoids the detection and measurement of surface deformation due to inaccurate phase solution, and reduces computing time and computing resource investment.

[0122] Optionally, the actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference are obtained by using GNSS equipment, a laser radar measurement drone, a synthetic aperture radar and an engineering measurement instrument, including:

[0123] The landslide area is monitored by the laser radar measurement drone, the synthetic aperture radar and the engineering measurement instrument to obtain the actual monitoring data;

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

[0125] Optionally, the GNSS device includes an RTK device connected to a BeiDou ground-based augmentation system, and the step of obtaining the coordinates of the target key points of the landslide area in the CGCS2000 coordinate reference by using the GNSS device, the laser radar measurement drone, the synthetic aperture radar and the engineering measurement instrument includes:

[0126] Obtaining the key point coordinates of the laser radar surveying drone CGCS2000 and the key point coordinates of the synthetic aperture radar CGCS2000 of the landslide area through the RTK device connected to the Beidou ground-based augmentation system, the laser radar surveying drone and the synthetic aperture radar;

[0127] The key point coordinates of the engineering surveying instrument CGCS2000 in the landslide area are obtained by deploying a forced centering observation pier and performing joint measurement with the engineering surveying instrument.

[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 receiver that can continuously receive signals from multiple satellite systems for a long time to provide high-precision location information. GNSS static observation equipment refers to long-term observation of a specific location within a certain period of time to obtain high-precision location data. RTK equipment mainly consists of two parts: a base station and a mobile station. Synthetic aperture radar includes but is not limited to satellite-based synthetic aperture radar deployed on a satellite platform and ground-based synthetic aperture radar deployed on the ground. Engineering surveying instruments include but are not limited to total stations, levels, and three-dimensional laser scanners.

[0129] Access to the BeiDou Ground-Based Augmentation System is defined as an action that simultaneously meets the following three conditions: (1) using mobile communication signals or self-provided radio stations for data transmission; (2) passing account and password verification; and (3) receiving the real-time differential signal of the BeiDou Ground-Based Augmentation System's base station fixed solution. The key points of the drone measured by the lidar are the key points of the image control mark. 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, in combination Figure 5As shown in the figure, the shaded area is the landslide profile, the landslide tunnel passes under the landslide, the gray dotted line is the landslide tunnel path, the tunnel key points and engineering measurement instruments are located in the landslide tunnel, and the white square slope area of ​​the triangular prism represents the surface area of ​​the landslide. The GNSS continuous tracking station, synthetic aperture radar corner reflector, and drone image control mark are located on the landslide surface, the lidar measurement drone is located at low altitude, the GNSS satellite and synthetic aperture radar satellite are located in space, and the ground-based synthetic aperture radar is located in an area far away from the landslide and with line of sight. GNSS equipment, lidar measurement drones, synthetic aperture radars, and engineering surveying instruments were used to monitor the displacement field of the landslide, and monitoring data including the coordinates of key points and all remaining points based on different original coordinate benchmarks were obtained for each monitoring method; the coordinates of the GNSS continuous tracking station CGCS2000 on the landslide surface were solved using the data provided by the Beidou Ground-Based Augmentation System base station and obtained; the coordinates of the key points of the lidar measurement drone on the landslide surface and the key points of the synthetic aperture radar on the landslide surface CGCS2000 were obtained using RTK equipment connected to the Beidou Ground-Based Augmentation System; forced centering observation piers were set up near the entrance of the landslide tunnel and GNSS static observations were carried out, and the GNSS static observation data were solved using the data provided by the Beidou Ground-Based Augmentation System base station to obtain the CGCS2000 coordinates of the forced centering observation piers; engineering surveying instruments were used to conduct joint measurements of the monitoring points inside the landslide tunnel and the forced centering observation piers at the entrance of the landslide tunnel to obtain the CGCS2000 coordinates of the key points of the landslide tunnel.

[0131] In some more specific embodiments, in combination Figure 6-9 As shown, Figure 6 This is the plan view of the LiDAR measurement drone image control mark. The gray area is the LiDAR measurement drone image control mark, which is red paint sprayed on the surface, 1m long at the bottom and 0.3m long at the top. The points pointed by the circles are the key points that need to use RTK equipment to obtain CGCS2000 coordinates. Figure 7 This is the side view of the ground-based synthetic aperture radar corner reflector, which is made of stainless steel and has a single-sided hollowed-out regular triangular prism shape, with a side length of 1.5m. The triangular area to the left of the dotted line is the hollowed-out surface facing away, and the point pointed by the circle is the key point where the CGCS2000 coordinates need to be obtained using RTK equipment. Figure 8 This is the side view of the satellite-based synthetic aperture radar corner reflector. The whole is made of stainless steel, with a single-sided hollow regular triangular prism shape, and the side length is 1.5m. The white triangle area is the hollow surface facing away, and the other two gray triangles are shaded areas to enhance the three-dimensional effect of the picture. The points pointed by the circles are the key points that need to use RTK equipment to obtain CGCS2000 coordinates. Fig. 9This is a side view of the ground-based synthetic aperture radar. The lower prism is the slide rail of the equipment, which is 3.7 meters long. The slide rail carries a square host. To the right (front) of the host is a horn-shaped radar wave transmitting and receiving device. The points pointed by the circles on both sides of the slide rail are the key points where the 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 engineering surveying instrument CGCS2000 include key point plane coordinates and key point elevation coordinates, and the key point coordinates of the engineering surveying instrument CGCS2000 in the landslide area are obtained by arranging a forced centering observation pier and performing joint measurement with the engineering surveying instrument, including:

[0133] The plane coordinates of the key points are obtained by placing the forced centering observation pier and the total station at the entrance of the landslide tunnel to perform combined traverse measurement;

[0134] The elevation coordinates of the key points are obtained by arranging the forced centering observation pier and the level instrument at the entrance of the landslide tunnel to perform closed-route leveling measurement.

[0135] Specifically, combined Fig.10 As shown in the figure, the dotted area is the tunnel, and the area near the landslide tunnel entrance is defined as the area that meets the following three conditions at the same time: (1) the straight-line distance from the landslide tunnel entrance is no more than 200 meters; (2) the key point closest to the landslide tunnel entrance is in the visible area, ensuring that it is in the visible area, not the blind area; (3) there is no mountain or vegetation blocking the sky. The plane coordinates (x, y coordinates) of the key point, the elevation coordinates (z coordinates) of the key point.

[0136] like Fig.11 As shown, an embodiment of the present invention provides a device for unifying coordinates of multi-source landslide monitoring data, comprising:

[0137] The data acquisition module 10 is used to acquire the actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference through GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering measurement instruments, wherein the actual monitoring data includes the coordinates of the target key points in the original coordinate reference and the coordinates of all remaining points;

[0138] A mapping relationship acquisition module 20, used to construct a key point coordinate mapping relationship through the coordinates of the target key point in the original coordinate reference and the coordinates of the target key point in the CGCS2000 coordinate reference;

[0139] A coordinate transformation model acquisition module 30, for obtaining a seven-parameter coordinate transformation model through the key point coordinate mapping relationship and geographic weighting coefficients based on neighborhood density normalization;

[0140] A 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 all point coordinates based on a mask rule to obtain the filtered remaining all point coordinates, wherein the pixels with low signal-to-noise ratio refer to 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 conversion model to obtain target CGCS2000 coordinate data.

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

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

[0144] Obtaining the seven-parameter coordinate transformation model according to the key point coordinate mapping relationship and the geographic weighting coefficient;

[0145] Wherein, the seven-parameter coordinate transformation model is:

[0146]

[0147] Among them, X i , Y i , Z i is the three-dimensional coordinate value of the i-th key point in the CGCS2000 coordinate system, w i is the geographic weighting coefficient of the i-th key point, ΔX0, ΔY0, ΔZ0 are the translation coefficients for converting the coordinates from the original coordinate system to the CGCS2000 coordinate system, m is the scale coefficient, ω X ,ω Y ,ω Z The rotation coefficient for converting coordinates from the original coordinate system to the CGCS2000 coordinate system, X′ i , Y′ i , Z′ i is the three-dimensional coordinate value 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 the key points according to the sparseness of the geographical distribution of the key points;

[0149] Among them, the neighborhood density of the key point is:

[0150]

[0151] Among them, D i is the key point neighborhood density of the i-th key point, N i is the spatial Euclidean distance between the i-th key point and the nearest key point;

[0152] Obtaining the geographic weighting coefficient according to the neighborhood density of the key point;

[0153] Wherein, the geographical weighting coefficient is:

[0154]

[0155] Among them, w i is the geographic weighting coefficient of the ith key point, D min is the minimum value of the neighborhood density of all key points, D max is the maximum value of the key point neighborhood density of all key points.

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

[0157] The regional mask processing data is filtered through a signal-to-noise ratio mask rule to obtain the filtered coordinates of all remaining points, wherein the signal-to-noise ratio mask rule is used to filter pixels with a low signal-to-noise ratio according to the signal-to-noise ratio.

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

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

[0160] Optionally, the data acquisition module 10 is specifically used to: obtain the key point coordinates of the laser radar surveying drone CGCS2000 and the key point coordinates of the synthetic aperture radar CGCS2000 of the landslide area through the RTK device connected to the Beidou ground-based augmentation system, the laser radar surveying drone and the synthetic aperture radar;

[0161] The key point coordinates of the engineering surveying instrument CGCS2000 in the landslide area are obtained by deploying a forced centering observation pier and performing joint measurement with the engineering surveying instrument.

[0162] Optionally, the data acquisition module 10 is specifically used to: obtain the plane coordinates of the key points by arranging the forced centering observation pier at the entrance of the landslide tunnel and performing a combined traverse measurement with the total station;

[0163] The elevation coordinates of the key points are obtained by arranging the forced centering observation pier and the level at the entrance of the landslide tunnel to perform closed-route leveling measurement.

[0164] like Fig.12 As shown, 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 above-mentioned multi-source landslide monitoring data unified coordinate method when executing the computer program.

[0165] In other words, 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 executing the computer program:

[0166] The actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference are obtained by GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering measurement instruments, wherein the actual monitoring data includes the coordinates of the target key points in the original coordinate reference and the coordinates of all remaining points;

[0167] Constructing a key point coordinate mapping relationship through the coordinates of the target key point in the original coordinate reference and the coordinates of the target key point in the CGCS2000 coordinate reference;

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

[0169] 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 refer to the pixels 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] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for unifying coordinates of multi-source landslide monitoring data is implemented.

[0172] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0173] The actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference are obtained by GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering measurement instruments, wherein the actual monitoring data includes the coordinates of the target key points in the original coordinate reference and the coordinates of all remaining points;

[0174] Constructing a key point coordinate mapping relationship through the coordinates of the target key point in the original coordinate reference and the coordinates of the target key point in the CGCS2000 coordinate reference;

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

[0176] 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 refer to the pixels 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] An electronic device that can be used as a server or client of the present invention will now be described, which is an example of a hardware device 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, workbenches, 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 processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present invention described and / or required herein.

[0179] The electronic device includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0180] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0181] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for unifying coordinates of multi-source landslide monitoring data, characterized in that: include: The actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference are obtained by GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering measurement instruments, wherein the actual monitoring data includes the coordinates of the target key points in the original coordinate reference and the coordinates of all remaining points; Constructing a key point coordinate mapping relationship through the coordinates of the target key point in the original coordinate reference and the coordinates of the target key point in the CGCS2000 coordinate reference; Based on neighborhood density normalization, a seven-parameter coordinate transformation model is obtained through the key point coordinate mapping relationship and geographic weighted coefficients; Based on the mask rule, the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining point coordinates are filtered to obtain the filtered remaining point coordinates, wherein the pixels with low signal-to-noise ratio refer to the pixels with a signal-to-noise ratio less than a preset threshold; The filtered coordinates of all remaining points are input into the seven-parameter coordinate transformation model to obtain the target CGCS2000 coordinate data.

2. The method for unifying coordinates of multi-source landslide monitoring data according to claim 1, characterized in that: The seven-parameter coordinate transformation model is obtained through the key point coordinate mapping relationship and the geographic weighting coefficient, including: The geographic weighting coefficient is obtained by determining the degree of geographic distribution sparseness of key points; Obtaining the seven-parameter coordinate transformation model according to the key point coordinate mapping relationship and the geographic weighting coefficient; Among them, the seven-parameter coordinate transformation model is: Among them, X i , Y i , Z i is the three-dimensional coordinate value of the i-th key point in the CGCS2000 coordinate system, w i is the geographic weighting coefficient of the i-th key point, ΔX0, ΔY0, ΔZ0 are the translation coefficients for converting the coordinates from the original coordinate system to the CGCS2000 coordinate system, m is the scale coefficient, ω X ,ω Y ,ω Z The rotation coefficient for converting coordinates from the original coordinate system to the CGCS2000 coordinate system, X′ i , Y′ i , Z′ i is the three-dimensional coordinate value of the i-th key point in the original coordinate system.

3. The method for unifying coordinates of multi-source landslide monitoring data according to claim 2 is characterized in that: The obtaining of the geographic weighting coefficient by the degree of geographic distribution sparseness of key points includes: Obtaining the neighborhood density of key points by the sparsity of geographical distribution of the key points; Among them, the neighborhood density of the key point is: Among them, D i is the key point neighborhood density of the i-th key point, N i is the spatial Euclidean distance between the i-th key point and the nearest key point; Obtaining the geographic weighting coefficient according to the neighborhood density of the key point; Wherein, the geographical weighting coefficient is: Among them, w i is the geographic weighting coefficient of the ith key point, D min is the minimum value of the neighborhood density of all key points, D max is the maximum value of the key point neighborhood density of all key points.

4. The method for unifying coordinates of multi-source landslide monitoring data according to claim 1, characterized in that: The filtering of the pixels outside the landslide area and the pixels with low signal-to-noise ratio in the remaining all point coordinates to obtain the filtered remaining all point coordinates includes: Multiplying the matrix composed of all the remaining point coordinates with the regional mask matrix correspondingly to obtain regional mask processing data, wherein the regional mask matrix is ​​used to mark the pixels located in the landslide area; The regional mask processing data is filtered through a signal-to-noise ratio mask rule to obtain the filtered coordinates of all remaining points, wherein the signal-to-noise ratio mask rule is used to filter pixels with a low signal-to-noise ratio according to the signal-to-noise ratio.

5. The method for unifying coordinates of multi-source landslide monitoring data according to claim 1, characterized in that: The actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference are obtained by using GNSS equipment, lidar measurement drones, synthetic aperture radars and engineering measurement instruments, including: The landslide area is monitored by the laser radar measurement drone, the synthetic aperture radar and the engineering measurement instrument to obtain the actual monitoring data; The coordinates of the target key points in the landslide area under the CGCS2000 coordinate reference are obtained through the GNSS equipment, the lidar surveying drone, the synthetic aperture radar and the engineering surveying instrument, wherein the coordinates of the target key points under the CGCS2000 coordinate reference include the key point coordinates of the lidar surveying drone CGCS2000, the key point coordinates of the synthetic aperture radar CGCS2000 and the key point coordinates of the engineering surveying instrument CGCS2000.

6. The method for unifying coordinates of multi-source landslide monitoring data according to claim 5, characterized in that: The GNSS device includes an RTK device connected to the Beidou Ground Based Augmentation System, and the coordinates of the target key points in the landslide area under the CGCS2000 coordinate reference are obtained by using the GNSS device, the laser radar measurement drone, the synthetic aperture radar and the engineering measurement instrument, including: Obtaining the key point coordinates of the laser radar surveying drone CGCS2000 and the key point coordinates of the synthetic aperture radar CGCS2000 of the landslide area through the RTK device connected to the Beidou ground-based augmentation system, the laser radar surveying drone and the synthetic aperture radar; The key point coordinates of the engineering surveying instrument CGCS2000 in the landslide area are obtained by deploying a forced centering observation pier and performing joint measurement with the engineering surveying instrument.

7. The method for unifying coordinates of multi-source landslide monitoring data according to claim 6, characterized in that: The engineering surveying instrument includes a total station and a level, the key point coordinates of the engineering surveying instrument CGCS2000 include the key point plane coordinates and the key point elevation coordinates, and the key point coordinates of the engineering surveying instrument CGCS2000 in the landslide area are obtained by arranging a forced centering observation pier and performing joint measurement with the engineering surveying instrument, including: The plane coordinates of the key points are obtained by placing the forced centering observation pier and the total station at the entrance of the landslide tunnel to perform combined traverse measurement; The elevation coordinates of the key points are obtained by arranging the forced centering observation pier and the level at the entrance of the landslide tunnel to perform closed-route leveling measurement.

8. A device for unifying coordinates of multi-source landslide monitoring data, characterized in that: include: A data acquisition module is used to acquire the actual monitoring data of the landslide area and the coordinates of the target key points in the CGCS2000 coordinate reference through GNSS equipment, laser radar measurement drones, synthetic aperture radars and engineering measurement instruments, wherein the actual monitoring data includes the coordinates of the target key points in the original coordinate reference and the coordinates of all remaining points; A mapping relationship acquisition module, used for constructing a key point coordinate mapping relationship through the coordinates of the target key point in the original coordinate reference and the coordinates of the target key point in the CGCS2000 coordinate reference; A coordinate transformation model acquisition module, used to obtain a seven-parameter coordinate transformation model through the key point coordinate mapping relationship and geographic weighting coefficient based on neighborhood density normalization; A data filtering module, for filtering pixels outside the landslide area and pixels with low signal-to-noise ratio in the remaining point coordinates based on mask rules to obtain the filtered remaining point coordinates, wherein the pixels with low signal-to-noise ratio represent pixels with a signal-to-noise ratio less than a preset threshold; The CGCS2000 coordinate acquisition module is used to input the filtered coordinates of all remaining points into the seven-parameter coordinate conversion model to obtain target CGCS2000 coordinate data.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the multi-source landslide monitoring data unified coordinate method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for unifying coordinates of multi-source landslide monitoring data as claimed in any one of claims 1 to 7 is implemented.

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