Slope deformation monitoring method and device, flight equipment and storage medium

Abstract

The application discloses a kind of side slope deformation monitoring method, side slope deformation monitoring device, flight equipment and storage medium, belong to environmental monitoring technical field.Wherein, the side slope deformation monitoring method includes: obtaining first point cloud data and second point cloud data, the first point cloud data and the second point cloud data are generated for monitoring target side slope at different time;The first point cloud data and the second point cloud data are registered and handled, obtain the first target point cloud data corresponding to the first point cloud data in target coordinate system and the second target point cloud data corresponding to the second point cloud data;According to the first target point cloud data and the second target point cloud data, determine the deformation of the target side slope.This application has realized the efficiency of improving side slope monitoring.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to slope deformation monitoring methods, slope deformation monitoring devices, flight equipment, and storage media. Background Technology

[0002] With the development of land resources, it is necessary to monitor slope deformation. Currently, the common method is to set up reference points on the slope. Reference points are set up on the ground, and slope data, including that at the reference points, is collected multiple times. The slope is then monitored by comparing the collected slope data with the reference points. However, this method requires setting up markers such as flags on the target slope before slope data can be collected, which reduces the efficiency of slope monitoring.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a slope deformation monitoring method, a slope deformation monitoring device, a flight device, and a storage medium, aiming to improve the efficiency of slope monitoring.

[0005] To achieve the above objectives, the present invention provides a slope deformation monitoring method, which includes the following steps:

[0006] Acquire first point cloud data and second point cloud data, which are generated from the target slope being monitored at different times;

[0007] The first point cloud data and the second point cloud data are registered to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system.

[0008] The deformation of the target slope is determined based on the first target point cloud data and the second target point cloud data.

[0009] Optionally, the step of determining the deformation of the target slope based on the first target point cloud data and the second target point cloud data includes:

[0010] Determine the common area between the first region corresponding to the first target point cloud data in the target slope and the second region corresponding to the second target point cloud data in the target slope;

[0011] Based on the registration relationship between the first target point cloud data and the second target point cloud data, the first target point cloud data, and the second target point cloud data, a change descriptor corresponding to each location point in the public area is calculated;

[0012] The changed areas in the public region are determined based on the change descriptor;

[0013] The deformation of the target slope is determined based on the data point information corresponding to the changed area in the first target point cloud data and the second target point cloud data.

[0014] Optionally, the step of determining the deformation of the target slope based on the data point information corresponding to the changed area in the first target point cloud data and the second target point cloud data includes:

[0015] The changed region is segmented according to the segmentation algorithm, and the ground region within the segmented changed region is determined;

[0016] Among all the change descriptors corresponding to the ground area, the change descriptors that are greater than a preset threshold are identified as target descriptors, and the areas corresponding to all the target descriptors in the ground area are divided to obtain multiple sub-change areas;

[0017] The deformation of the target slope is determined based on the data point information corresponding to the multiple sub-variable regions in the first target point cloud data and the second target point cloud data.

[0018] Optionally, the step of determining the deformation of the target slope based on the data point information corresponding to the plurality of sub-variable regions in the first target point cloud data and the second target point cloud data includes:

[0019] Each of the aforementioned sub-variable regions is divided into multiple grids;

[0020] The grids that meet the preset conditions are identified as grids to be calculated, and a plurality of grids to be calculated are obtained; wherein, the preset conditions include the existence of data points corresponding to the grids in the first target point cloud data and / or the second target point cloud data;

[0021] The deformation of the target slope is determined based on the size parameters of the grid to be calculated and / or the position information of the corresponding data points in the first target point cloud data and the second target point cloud data.

[0022] Optionally, the step of registering the first point cloud data and the second point cloud data to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system includes:

[0023] Calculate the first feature of each point in the first point cloud data and the second feature of each point in the second point cloud data;

[0024] The first point cloud data and the second point cloud data are matched based on the first feature and the second feature to obtain at least three matching pairs;

[0025] Calculate coordinate transformation parameters based on the at least three matching pairs;

[0026] The first target point cloud data and the second target point cloud data are determined based on the coordinate transformation parameters, the first point cloud data, and the second point cloud data.

[0027] Optionally, the step of calculating the coordinate transformation parameters based on the at least three matching pairs includes:

[0028] Calculate the rotation matrix and translation matrix based on at least three matching pairs and the pairing weights corresponding to the matching pairs;

[0029] Calculate the registration error between the first target point cloud data and the second target point cloud data based on the rotation matrix and the translation matrix;

[0030] When the registration error is greater than a preset error threshold, the pairing weights are determined based on the rotation matrix and the translation matrix, and the process returns to the step of calculating the rotation matrix and translation matrix based on at least three matching pairs and the pairing weights corresponding to the matching pairs.

[0031] When the registration error is less than or equal to a preset error threshold, the rotation matrix and the translation matrix are determined as the coordinate transformation parameters.

[0032] Optionally, the step of acquiring the first point cloud data and the second point cloud data includes:

[0033] The flight equipment is controlled to fly along a preset route according to a preset time interval, and the first point cloud data and the second point cloud data are collected.

[0034] Furthermore, to achieve the above objectives, the present invention also provides a slope deformation monitoring device, the slope deformation monitoring device comprising:

[0035] The acquisition module is used to acquire first point cloud data and second point cloud data, which are generated at different times for monitoring target slopes;

[0036] The registration module is used to perform registration processing on the first point cloud data and the second point cloud data to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system.

[0037] The output module is used to determine the deformation of the target slope based on the first target point cloud data and the second target point cloud data.

[0038] In addition, to achieve the above objectives, the present invention also provides a flight device, the flight device comprising: a memory, a processor, and a slope deformation monitoring program stored in the memory and executable on the processor, the slope deformation monitoring program being configured to implement the steps of the slope deformation monitoring method described in any of the above claims.

[0039] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a slope deformation monitoring program, wherein the slope deformation monitoring program, when executed by a processor, implements the steps of the slope deformation monitoring method described in any of the above claims.

[0040] This invention proposes a slope deformation monitoring method. This method performs registration processing on first and second point cloud data generated at different times for monitoring a target slope, obtaining first target point cloud data corresponding to the first point cloud data and second target point cloud data corresponding to the second point cloud data in the target coordinate system, thereby determining the correspondence between the two point cloud data. The deformation of the target slope is determined based on the first and second target point cloud data. Compared to current methods that set reference points on the slope, the first and second target point cloud data obtained through registration processing in this application can accurately reflect the changes in the same area on the slope at different times, enabling slope deformation monitoring without setting reference points on the slope, thus improving the efficiency of slope monitoring. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of the flight equipment in the hardware operating environment involved in the embodiments of the present invention;

[0042] Figure 2 This is a flowchart illustrating the first embodiment of the slope deformation monitoring method of the present invention;

[0043] Figure 3 This is a flowchart illustrating the second embodiment of the slope deformation monitoring method of the present invention;

[0044] Figure 4 This is a flowchart illustrating the third embodiment of the slope deformation monitoring method of the present invention;

[0045] Figure 5 This is a flowchart illustrating the fourth embodiment of the slope deformation monitoring method of the present invention;

[0046] Figure 6 This is a flowchart illustrating the fifth embodiment of the slope deformation monitoring method of the present invention.

[0047] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0049] Reference Figure 1 , Figure 1 This is a schematic diagram of the flight equipment structure of the hardware operating environment involved in the embodiments of the present invention.

[0050] like Figure 1 As shown, the flight device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interaction device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interaction device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interaction device 1003 may also be connected to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0051] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the flight equipment and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0052] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a slope deformation monitoring program.

[0053] exist Figure 1 In the flight device shown, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the flight device of the present invention can be set in the flight device. The flight device calls the slope deformation monitoring program stored in the memory 1005 through the processor 1001 and executes the slope deformation monitoring method provided in the embodiment of the present invention.

[0054] This invention provides a method for monitoring slope deformation, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a slope deformation monitoring method according to the present invention.

[0055] In this embodiment, the slope deformation monitoring method includes:

[0056] Step S10: Obtain first point cloud data and second point cloud data, wherein the first point cloud data and the second point cloud data are generated at different times for monitoring target slopes;

[0057] In this embodiment, the geodetic data, digital elevation model, orthographic map, topographic map, and vegetation cover of the monitoring area and adjacent areas are typically determined first. After airspace is applied for, flight path planning is performed, and a lidar is installed on the flight equipment. The flight equipment flies over the target slope, using the lidar to acquire data from the target slope, forming point cloud data. This allows the acquisition of point cloud data generated at different times for monitoring the target slope, namely the first and second point cloud data of this application.

[0058] Step S20: Perform registration processing on the first point cloud data and the second point cloud data to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system.

[0059] In this embodiment, the coordinate systems of the first target point cloud data and the second target point cloud data are both the target coordinate system. In this application, either the first point cloud data or the second point cloud data can be selected as the target coordinate system, and coordinate transformation parameters can be used to place the first point cloud data and the second point cloud data within the target coordinate system, thus obtaining their corresponding first target point cloud data and second target point cloud data.

[0060] Step S30: Determine the deformation of the target slope based on the first target point cloud data and the second target point cloud data.

[0061] In this embodiment, the changing areas of the slope in different time periods are determined by the first target point cloud data and the second target point cloud data, and the deformation of the target slope is determined by calculating parameters such as the elevation difference, changing area, and changing volume of the changing areas.

[0062] In this embodiment, compared with the current method of setting reference points on the slope, this application uses registration processing to determine the correspondence between the first point cloud data and the second point cloud data without setting reference points on the slope, thereby avoiding the need to set reference points on the slope and improving the efficiency of slope monitoring.

[0063] Furthermore, based on the first embodiment, a second embodiment of the slope deformation monitoring invention is proposed. In this embodiment, reference is made to... Figure 3 The step of determining the deformation of the target slope based on the first target point cloud data and the second target point cloud data includes:

[0064] Step S31: Determine the common area between the first region corresponding to the first target point cloud data in the target slope and the second region corresponding to the second target point cloud data in the target slope;

[0065] Since it's impossible to guarantee a perfect one-to-one correspondence between the data collected by the lidar during flight, it's necessary to determine the first and second regions. By comparing the first and second regions, the overlapping area between them is identified as the common region. Specifically, coordinate transformation parameters are used to place the two point cloud data sets in the target coordinate system. These parameters can include rotation and translation matrices. An octree is used to determine if each point in the first target point cloud data exists at the same position in the second target point cloud data, thus extracting all overlapping points containing corresponding points. However, this method can lead to the removal of change points in change areas, requiring the re-addition of these change points. All extracted change points are then re-added to the common region. By dividing all extracted points into grids, if a point's corresponding grid has grids in all four directions (up, down, left, and right), it's considered a mistakenly deleted point. Finally, the mistakenly deleted change points are re-added to obtain the points in the common region.

[0066] Step S32: Calculate the change descriptor corresponding to each location point in the common area based on the registration relationship between the first target point cloud data and the second target point cloud data, the first target point cloud data, and the second target point cloud data;

[0067] The change descriptor here can be the distance between the matching pairs in the first target point cloud data and the second target point cloud data determined by the registration relationship.

[0068] Step S33: Determine the changed areas in the public area based on the change descriptor;

[0069] Since the change descriptor reflects the changes in the information of its corresponding data points, in this embodiment, the neighborhood of the data points whose change descriptors are non-zero is determined as the change region. The range of this neighborhood can be determined based on the point cloud density. In other embodiments, a closed region is formed by connecting the data points whose change descriptor values ​​are non-zero, which is then used as the defined change region.

[0070] Step S34: Determine the deformation of the target slope based on the data point information corresponding to the changed area in the first target point cloud data and the second target point cloud data.

[0071] By identifying the changed area, and based on the corresponding data points in the first and second target point cloud data, the area, elevation, and volume of the change are determined. This allows for the assessment of the deformation of the target slope.

[0072] In this embodiment, by determining the common area, the accuracy of the location analysis of the first target point cloud data and the second target point cloud data is determined, and by calculating the change descriptor, the degree of change can be determined, thereby achieving accurate analysis of the target slope and improving the accuracy of determining the deformation of the target slope.

[0073] Furthermore, based on any of the above embodiments, a third embodiment of the slope deformation monitoring invention is proposed. In this embodiment, reference is made to... Figure 4 The step of determining the deformation of the target slope based on the data point information corresponding to the changed area in the first target point cloud data and the second target point cloud data includes:

[0074] Step S341: Segment the changed region according to the segmentation algorithm, and determine the ground region in the segmented changed region;

[0075] Since the point cloud data obtained from scanning often contains many objects that do not require attention, such as tree points, water points, power lines, and temporary debris, this solution uses a segmentation algorithm to segment the changing region. In this embodiment, the point cloud is segmented into different categories, and the category with the largest number of segments is taken as the ground point. In other embodiments, the ground region is obtained by taking the derivative of adjacent data points in the first target point cloud data and the second target point cloud data respectively. When the derivative result is greater than the derivative threshold, the data point corresponding to the derivative result is removed.

[0076] Step S342: Determine the change descriptors greater than a preset threshold among all the change descriptors corresponding to the ground area as target descriptors, and divide the area corresponding to all the target descriptors in the ground area to obtain multiple sub-change areas;

[0077] The preset threshold here can be determined based on geodetic data of the monitoring area and adjacent areas of the target slope, digital elevation model of the monitoring area, orthophoto map, topographic map, vegetation cover of the monitoring area, etc. In other embodiments, the ground area can be divided into multiple sub-change areas according to the direction of change, i.e., the positive or negative sign of the change descriptor. When the change descriptor in a sub-change area is positive, the sub-change area is determined to be an accumulation area. When the change descriptor in a sub-change area is negative, the sub-change area is determined to be a sliding area.

[0078] Step S343: Determine the deformation of the target slope based on the data point information corresponding to the multiple sub-variable regions in the first target point cloud data and the second target point cloud data.

[0079] The deformation of each sub-variable region is determined by identifying the corresponding data point information in the first target point cloud data and the second target point cloud data, thereby determining the deformation of the target slope.

[0080] In this embodiment, by determining the ground area within the change area, the analysis of slope deformation is avoided due to the influence of the height of other objects, thereby improving the accuracy of the target slope deformation.

[0081] Furthermore, based on any of the above embodiments, a fourth embodiment of the slope deformation monitoring invention is proposed. In this embodiment, reference is made to... Figure 5 The step of determining the deformation of the target slope based on the data point information corresponding to the multiple sub-variable regions in the first target point cloud data and the second target point cloud data includes:

[0082] Step S3431: Divide each of the sub-variable regions into multiple grids;

[0083] The grids here are all the same size, and the size parameters of the grids can be determined based on the distance between data points in the point cloud data of adjacent points.

[0084] Step S3432: Determine the grids that meet the preset conditions as grids to be calculated, and obtain a plurality of grids to be calculated; wherein, the preset conditions include the existence of data points corresponding to the grids in the first target point cloud data and / or the second target point cloud data;

[0085] When the grid contains data points corresponding to the grid, it is determined that the grid can be used to calculate the deformation of the target slope.

[0086] Step S3433: Determine the deformation of the target slope based on the size parameters of the grid to be calculated and / or the position information of the corresponding data points in the first target point cloud data and the second target point cloud data of the grid to be calculated.

[0087] In this embodiment, the changed area of ​​the target slope can be determined by using grids. The changed area of ​​the target slope is calculated by multiplying the size parameters by the number of grids to be calculated. The changed volume of the grid is determined by multiplying the size parameters of the grids by the change descriptor of the corresponding data points of the grids. The changed volume of each grid in the sub-change area is statistically analyzed to obtain the changed volume of the target slope. Specifically, the change descriptor can be the elevation change of the corresponding data points of the grids. The changed volume of the implemented grid is obtained by multiplying the elevation change and the size parameters of the grids. In other embodiments, all grids to be calculated can be projected onto a horizontal plane, the change descriptor of the corresponding data points of the grids can be used as grayscale values, the grayscale image can be rendered, and the information of the changed area can be added to the display image. Finally, a color scale bar and a scale bar can be added to determine the deformation of the target slope at various locations.

[0088] In this embodiment, the specific change value of slope deformation can be calculated by using the size parameters of the target grid to be calculated and / or the position information of the corresponding data points in the first target point cloud data and the second target point cloud data of the grid to be calculated, thereby improving the accuracy of determining the slope deformation.

[0089] Furthermore, based on any of the above embodiments, a fifth embodiment of the slope deformation monitoring invention is proposed. In this embodiment, reference is made to... Figure 6 The step of registering the first point cloud data and the second point cloud data to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system includes:

[0090] Step S21: Calculate the first feature of each point in the first point cloud data and the second feature of each point in the second point cloud data;

[0091] The first and second features here are Fast Point Feature Histograms (FPFHs). These FPFHs obtain the spatial differences between points in a neighborhood and form a multi-histogram to describe the geometric properties of a point within its neighborhood. In this embodiment, point features are selected. In other embodiments, geometric primitives such as lines, surfaces, and volumes can also be selected as feature points.

[0092] Step S22: Match the first point cloud data and the second point cloud data according to the first feature and the second feature to obtain at least three matching pairs;

[0093] By comparing the feature distances of the first feature and the second feature in the feature space, a matching pair of points in the first point cloud data and the second point cloud data is determined. In this embodiment, the two points in the matching pair are the closest points to each other in the feature space.

[0094] Step S23: Calculate coordinate transformation parameters based on the at least three matching pairs;

[0095] The coordinate transformation parameters are calculated based on the position information of at least three matching pairs.

[0096] Step S24: Determine the first target point cloud data and the second target point cloud data based on the coordinate transformation parameters, the first point cloud data, and the second point cloud data.

[0097] According to the coordinate transformation parameters, the first point cloud data and the second point cloud data are converted into first target point cloud data and second target point cloud data in the target coordinate system. Specifically, for example: when the coordinate system of the first point cloud data is selected as the target coordinate system, the coordinate transformation parameters for converting the second point cloud data to target coordinates are calculated, the second target point cloud data corresponding to the second point cloud data is calculated according to the coordinate transformation parameters, and the first point cloud data is used as the first target point cloud data. When the coordinate system of the second point cloud data is selected as the target coordinate system, the coordinate transformation parameters for converting the first point cloud data to target coordinates are calculated, the first target point cloud data corresponding to the first point cloud data is calculated according to the coordinate transformation parameters, and the second point cloud data is used as the second target point cloud data. Further, to further reduce the registration error, iterative closest point (ICP) is used to optimize the registration result. When the point cloud scale is 300m × 300m × 100m, the point cloud registration error is less than 0.5m. Furthermore, when performing accuracy testing on the registration, traditional surveying instruments can be used, a public dataset of shared registration reference values ​​can be used, or a manual target method can be used for evaluation and checkpoints can be set.

[0098] In this embodiment, compared to setting up markers on the actual slope, the matching pairs can be determined by the FPFH feature without setting up markers, thereby improving the detection efficiency of slope monitoring.

[0099] Furthermore, based on any of the above embodiments, a sixth embodiment of the present invention for slope deformation monitoring is proposed. In this embodiment, the step of calculating the coordinate transformation parameters based on the at least three matching pairs includes:

[0100] Calculate the rotation matrix and translation matrix based on at least three matching pairs and the pairing weights corresponding to the matching pairs;

[0101] Calculate the registration error between the first target point cloud data and the second target point cloud data based on the rotation matrix and the translation matrix;

[0102] When the registration error is greater than a preset error threshold, the pairing weights are updated according to the rotation matrix, and the process returns to the step of calculating the rotation matrix and translation matrix based on at least three matching pairs and the pairing weights corresponding to the matching pairs.

[0103] When the registration error is less than or equal to a preset error threshold, the rotation matrix and the translation matrix are determined as the coordinate transformation parameters.

[0104] In this embodiment, the rotation matrix and the translation matrix are calculated using weighted singular value decomposition (SVD) based on at least three matching pairs and their corresponding pairing weights, with each matching pair having equal initial pairing weights. The registration error between the first target point cloud data and the second target point cloud data is calculated based on the rotation matrix and the translation matrix. The pairing weights of the matching pairs are adjusted according to the magnitude of the registration error. When the registration error is less than or equal to a preset error threshold, the rotation matrix and the translation matrix are determined as the coordinate transformation parameters. In other embodiments, a fixed number of iterations for the rotation and translation matrices can be preset. When this number of iterations is reached, the calculation stops, and the rotation and translation matrices are used as the coordinate transformation parameters. Furthermore, the rotation matrix and the translation matrix are iteratively optimized using the least squares method based on the iterated coordinate transformation parameters. The optimized rotation matrix and the translation matrix are then updated to the coordinate transformation parameters; specifically, the optimized rotation matrix and the translation matrix can replace the unoptimized rotation matrix and the translation matrix.

[0105] In this embodiment, the numerical values ​​of the pairing weights corresponding to the matching pairs are adjusted by repeatedly iterating the rotation matrix and translation matrix, thereby improving the accuracy of the rotation matrix, which in turn improves the accuracy of point cloud registration and the accuracy of determining the deformation of the target slope.

[0106] Furthermore, based on any of the above embodiments, a seventh embodiment of the present invention for slope deformation monitoring is proposed. In this embodiment, the step of acquiring the first point cloud data and the second point cloud data includes:

[0107] The flight equipment is controlled to fly along a preset route according to a preset time interval, and the first point cloud data and the second point cloud data are collected.

[0108] The preset time interval here is determined based on geodetic data of the monitoring area and adjacent areas of the slope. The first point cloud data and the second point cloud data are collected during flight.

[0109] In this embodiment, by controlling the flight equipment to fly along a preset route through the preset time interval and collecting the first point cloud data and the second point cloud data, the rate of change of slope deformation can be determined, thereby improving the accuracy of slope deformation.

[0110] Furthermore, this invention also proposes a slope deformation monitoring device, which includes:

[0111] The acquisition module is used to acquire first point cloud data and second point cloud data, which are generated at different times for monitoring target slopes;

[0112] The registration module is used to perform registration processing on the first point cloud data and the second point cloud data to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system.

[0113] The output module is used to determine the deformation of the target slope based on the first target point cloud data and the second target point cloud data.

[0114] Furthermore, this embodiment of the invention also proposes a storage medium storing a slope deformation monitoring program, which, when executed by a processor, implements the steps of any of the above-described slope deformation monitoring method embodiments.

[0115] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0116] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0118] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for monitoring slope deformation, characterized in that, The slope deformation monitoring method includes the following steps: Acquire first point cloud data and second point cloud data, which are generated from the target slope being monitored at different times; The first point cloud data and the second point cloud data are registered to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system. The deformation of the target slope is determined based on the first target point cloud data and the second target point cloud data; The step of registering the first point cloud data and the second point cloud data to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system includes: Calculate the first feature of each point in the first point cloud data and the second feature of each point in the second point cloud data, where the first feature and the second feature are a fast feature histogram of points; The first point cloud data and the second point cloud data are matched based on the first feature and the second feature to obtain at least three matching pairs; Calculate the rotation matrix and translation matrix based on at least three matching pairs and the pairing weights corresponding to the matching pairs; Calculate the registration error between the first target point cloud data and the second target point cloud data based on the rotation matrix and translation matrix; When the registration error is greater than a preset error threshold, the pairing weights are updated according to the rotation matrix and the translation matrix, and the process returns to the step of calculating the rotation matrix and translation matrix based on at least three matching pairs and the pairing weights corresponding to the matching pairs. When the registration error is less than or equal to a preset error threshold, the rotation matrix and the translation matrix are determined as the coordinate transformation parameters. The first target point cloud data and the second target point cloud data are determined based on the coordinate transformation parameters, the first point cloud data, and the second point cloud data. The step of determining the deformation of the target slope based on the first target point cloud data and the second target point cloud data includes: Determine the common area between the first region corresponding to the first target point cloud data in the target slope and the second region corresponding to the second target point cloud data in the target slope; Based on the registration relationship between the first target point cloud data and the second target point cloud data, the first target point cloud data, and the second target point cloud data, a change descriptor corresponding to each location point in the public area is calculated; The changed areas in the public region are determined based on the change descriptor; The changed region is segmented according to the segmentation algorithm, and the ground region within the segmented changed region is determined; Among all the change descriptors corresponding to the ground area, the change descriptors that are greater than a preset threshold are identified as target descriptors, and the areas corresponding to all the target descriptors in the ground area are divided to obtain multiple sub-change areas; Each of the aforementioned sub-variable regions is divided into multiple grids; The grids that meet the preset conditions are identified as grids to be calculated, and a plurality of grids to be calculated are obtained; wherein, the preset conditions include the existence of data points corresponding to the grids in the first target point cloud data and / or the second target point cloud data; The deformation of the target slope is determined based on the size parameters of the grid to be calculated and / or the position information of the corresponding data points in the first target point cloud data and the second target point cloud data of the grid to be calculated. The step of determining the deformation of the target slope based on the size parameters of the grid to be calculated and / or the position information of the corresponding data points in the first target point cloud data and the second target point cloud data of the grid to be calculated includes: The change volume of the grid is determined by multiplying the size parameters of the grid by the change descriptor of the corresponding data point of the grid. The change volume of each grid in the sub-change region is counted to obtain the change volume of the target slope. or, Project all grids to be calculated onto a horizontal plane, use the change descriptors of the corresponding data points of the grids as grayscale values, render the grayscale image, add the information of the change area to the display image, and add a color scale bar and scale bar to determine the deformation of each location of the target slope.

2. The slope deformation monitoring method as described in claim 1, characterized in that, The steps for obtaining the first point cloud data and the second point cloud data include: The flight equipment is controlled to fly along a preset route according to a preset time interval, and the first point cloud data and the second point cloud data are collected.

3. A slope deformation monitoring device, characterized in that, The slope deformation monitoring device includes: The acquisition module is used to acquire first point cloud data and second point cloud data, which are generated at different times for monitoring target slopes; The registration module is used to perform registration processing on the first point cloud data and the second point cloud data to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system. The output module is used to determine the deformation of the target slope based on the first target point cloud data and the second target point cloud data; The steps of registering the first point cloud data and the second point cloud data to obtain the first target point cloud data corresponding to the first point cloud data and the second target point cloud data corresponding to the second point cloud data in the target coordinate system include: Calculate the first feature of each point in the first point cloud data and the second feature of each point in the second point cloud data, where the first feature and the second feature are a fast feature histogram of points; The first point cloud data and the second point cloud data are matched based on the first feature and the second feature to obtain at least three matching pairs; Calculate the rotation matrix and translation matrix based on at least three matching pairs and the pairing weights corresponding to the matching pairs; Calculate the registration error between the first target point cloud data and the second target point cloud data based on the rotation matrix and translation matrix; When the registration error is greater than a preset error threshold, the pairing weights are updated according to the rotation matrix and the translation matrix, and the process returns to the step of calculating the rotation matrix and translation matrix based on at least three matching pairs and the pairing weights corresponding to the matching pairs; When the registration error is less than or equal to a preset error threshold, the rotation matrix and the translation matrix are determined as the coordinate transformation parameters. The first target point cloud data and the second target point cloud data are determined based on the coordinate transformation parameters, the first point cloud data, and the second point cloud data. The step of determining the deformation of the target slope based on the first target point cloud data and the second target point cloud data includes: Determine the common area between the first region corresponding to the first target point cloud data in the target slope and the second region corresponding to the second target point cloud data in the target slope; Based on the registration relationship between the first target point cloud data and the second target point cloud data, the first target point cloud data, and the second target point cloud data, a change descriptor corresponding to each location point in the public area is calculated; The changed areas in the public region are determined based on the change descriptor; The changed region is segmented according to the segmentation algorithm, and the ground region within the segmented changed region is determined; Among all the change descriptors corresponding to the ground area, the change descriptors that are greater than a preset threshold are identified as target descriptors, and the areas corresponding to all the target descriptors in the ground area are divided to obtain multiple sub-change areas; Each of the aforementioned sub-variable regions is divided into multiple grids; The grids that meet the preset conditions are identified as grids to be calculated, and a plurality of grids to be calculated are obtained; wherein, the preset conditions include the existence of data points corresponding to the grids in the first target point cloud data and / or the second target point cloud data; The deformation of the target slope is determined based on the size parameters of the grid to be calculated and / or the position information of the corresponding data points in the first target point cloud data and the second target point cloud data of the grid to be calculated. The step of determining the deformation of the target slope based on the size parameters of the grid to be calculated and / or the position information of the corresponding data points in the first target point cloud data and the second target point cloud data of the grid to be calculated includes: The change volume of the grid is determined by multiplying the size parameters of the grid by the change descriptor of the corresponding data point of the grid. The change volume of each grid in the sub-change region is counted to obtain the change volume of the target slope. or, Project all grids to be calculated onto a horizontal plane, use the change descriptors of the corresponding data points of the grids as grayscale values, render the grayscale image, add the information of the change area to the display image, and add a color scale bar and scale bar to determine the deformation of each location of the target slope.

4. A flight device, characterized in that, The flight device includes: a memory, a processor, and a slope deformation monitoring program stored in the memory and executable on the processor, the slope deformation monitoring program being configured to implement the steps of the slope deformation monitoring method as described in any one of claims 1 to 2.

5. A storage medium, characterized in that, The storage medium stores a slope deformation monitoring program, which, when executed by a processor, implements the steps of the slope deformation monitoring method as described in any one of claims 1 to 2.

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