A site selection method and site selection device for a displacement monitoring apparatus

Abstract

This application provides a method and apparatus for selecting a location for a displacement monitoring device, comprising: acquiring point cloud data of the monitored area and point cloud data of candidate installation areas; establishing a voxel grid model of the candidate installation areas based on the point cloud data of the candidate installation areas; wherein each voxel grid in the voxel grid model corresponds to a candidate installation location of the displacement monitoring device; determining the relative angle information between each candidate installation location and the monitored area based on the monitored point cloud data, and determining the installation adaptation index of each candidate installation location for the monitored area based on the relative angle information; and selecting the target installation location of the displacement monitoring device from multiple candidate installation locations based on the installation adaptation index of each candidate installation location for the monitored area. In this way, by quantitatively analyzing the suitability of the installation location of the displacement monitoring device, an optimal installation location can be selected, thereby improving the accuracy and stability of the monitoring data of the monitoring device.

Description

Technical Field

[0001] This application relates to the field of displacement monitoring equipment technology, and in particular to a method and device for selecting a displacement monitoring device. Background Technology

[0002] Long-term automated displacement monitoring requires prior site selection for the installation of the displacement monitoring equipment, and the quality of the site selection directly affects the monitoring effect. Currently, existing technologies typically rely on operators to manually select sites based on their experience and the site environment. However, this manual site selection method heavily depends on the operator's experience, making it impossible to quantitatively assess the quality of the site selection. This can easily lead to unreasonable installation locations for the displacement monitoring equipment, thus affecting the monitoring effect. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method and device for selecting a displacement monitoring device. By using the relative angle information between the candidate installation location and the monitored area, the installation adaptability index of each candidate installation location for the monitored area is determined, so as to screen out the preferred target installation location for the displacement monitoring device. In this way, by quantitatively analyzing the suitability of the installation location of the displacement monitoring device, the preferred installation location can be selected, thereby improving the accuracy and stability of the monitoring data of the monitoring device.

[0004] This application provides a method for selecting a location for a displacement monitoring device, the method comprising:

[0005] Acquire point cloud data of the monitored area and point cloud data of the candidate installation areas for displacement monitoring equipment;

[0006] A voxel grid model of the candidate installation area is established based on the point cloud data of the candidate installation area; wherein, the voxel grid model includes multiple voxel grids, and each voxel grid corresponds to a candidate installation position of the displacement monitoring device;

[0007] Based on the monitored point cloud data, the relative angle information between each candidate installation location and the monitored area is determined, and the installation adaptation index of each candidate installation location for the monitored area is determined based on the relative angle information.

[0008] The target installation location for the displacement monitoring device is selected from multiple candidate installation locations based on the installation compatibility index of each candidate installation location for the monitored area.

[0009] Furthermore, based on the monitored point cloud data, the relative angle information between each candidate installation location and the monitored area is determined, and the installation adaptation index of each candidate installation location for the monitored area is determined based on the relative angle information, including:

[0010] Based on the spatial location information of the monitored point cloud data, the monitored area is divided into multiple local areas;

[0011] Based on the monitored point cloud data corresponding to each local area, determine the centroid incidence angle from each candidate installation location to the centroid of each local area;

[0012] Based on the centroid incidence angle from each candidate installation location to the centroid of each local region, an installation adaptation index is determined for each candidate installation location relative to each local region of the monitored area; wherein, the installation adaptation index is negatively correlated with the centroid incidence angle.

[0013] Furthermore, the spatial location information includes the spatial distance between monitored points in the monitored point cloud data and the curvature and / or normal at each monitored point; the division of the monitored area into multiple local regions based on the spatial location information of the monitored point cloud data includes:

[0014] Determine the curvature and / or normal at each of the multiple monitored points included in the monitored point cloud data;

[0015] Based on the curvature and / or normal at each monitored point, select multiple monitored points that belong to the flat region of the monitored area from the plurality of monitored points;

[0016] Based on the spatial distance between each monitored point belonging to a flat area within the monitored region, the multiple monitored points are clustered into multiple local regions.

[0017] Furthermore, determining the centroid incidence angle from each candidate installation location to the centroid of each local region based on the monitored point cloud data corresponding to each local region includes:

[0018] Based on the monitored point cloud data corresponding to each local region, a fitting plane corresponding to each local region is obtained;

[0019] Determine the centroid of each local region and the plane normal vector of the fitted plane corresponding to each local region;

[0020] For each candidate installation location, and for each local region, determine the direction vector between the centroid of the local region and the candidate installation location;

[0021] The angle between the direction vector from the local region to the centroid of the candidate installation position and the plane normal vector of the fitted plane corresponding to the local region is determined as the centroid incidence angle from the candidate installation position to the centroid of the local region.

[0022] Furthermore, determining the installation fit index of each candidate installation location for each local region within the monitored area, based on the centroid incidence angle from each candidate installation location to the centroid of each local region, includes:

[0023] For each candidate installation location and each local area, the installation compatibility index of the candidate installation location for that local area is determined by the following formula:

[0024]

[0025] In the formula, α represents the centroid incidence angle from the candidate installation position to the centroid of the local region; ad() represents the installation adaptation index of the candidate installation position for the local region; C1, C2 and C3 represent constants.

[0026] Furthermore, when the number of displacement monitoring devices is one, the step of selecting the target installation location of the displacement monitoring device from multiple candidate installation locations based on the installation compatibility index of each candidate installation location for the monitored area includes:

[0027] Determine the average installation fit index of each candidate installation location for each local area within the monitored area;

[0028] The candidate installation location with the highest average value is determined as the target installation location of the displacement monitoring device;

[0029] When the number of displacement monitoring devices is N, where N is a positive integer greater than 1, the step of selecting the target installation location of the displacement monitoring device from multiple candidate installation locations based on the installation adaptability index of each candidate installation location for the monitored area includes:

[0030] N candidate installation locations are selected multiple times from multiple candidate installation locations to obtain m sets of candidate installation locations, where m is a positive integer greater than 1;

[0031] For each group of candidate installation locations, for each local area in the monitored area, determine the maximum installation compatibility index between each candidate installation location in the group and that local area;

[0032] The sum of the maximum installation adaptation indices corresponding to each local area is determined as the installation adaptation index of the candidate installation locations in that group.

[0033] The candidate installation location with the highest installation adaptation index among the m groups of candidate installation locations is determined as the target installation location for the N displacement monitoring devices.

[0034] Furthermore, after selecting the target installation location for the displacement monitoring device from multiple candidate installation locations based on the installation adaptability index of each candidate installation location for the monitored area, the site selection method further includes:

[0035] The voxel mesh corresponding to the target installation location is further divided into voxel meshes to establish a refined voxel mesh model corresponding to the target installation location. The refined voxel mesh model includes multiple refined voxel meshes, and each refined voxel mesh corresponds to a refined installation location of the displacement monitoring device. The resolution of the refined voxel mesh model is smaller than that of the voxel mesh model.

[0036] Based on the adaptation index of each refined installation location for the monitored area, the precise installation location of the displacement monitoring device is further selected from multiple refined installation locations.

[0037] This application embodiment also provides a location selection device for a displacement monitoring device, the location selection device comprising:

[0038] The acquisition module is used to acquire the monitored point cloud data of the monitored area and the point cloud data of the candidate installation area of ​​the displacement monitoring equipment;

[0039] A module is established to build a voxel grid model of the candidate installation area based on the point cloud data of the candidate installation area; wherein, the voxel grid model includes multiple voxel grids, and each voxel grid corresponds to a candidate installation position of the displacement monitoring device;

[0040] The determination module is used to determine the relative angle information between each candidate installation location and the monitored area based on the monitored point cloud data, and to determine the installation adaptation index of each candidate installation location for the monitored area based on the relative angle information.

[0041] The filtering module is used to filter the target installation location of the displacement monitoring device from multiple candidate installation locations based on the installation adaptability index of each candidate installation location for the monitored area.

[0042] This application also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the addressing method for a displacement monitoring device described above are performed.

[0043] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described location selection method for a displacement monitoring device.

[0044] This application provides a method and apparatus for selecting a displacement monitoring device, comprising: acquiring point cloud data of a monitored area and point cloud data of a candidate installation area for the displacement monitoring device; establishing a voxel grid model of the candidate installation area based on the point cloud data of the candidate installation area; wherein the voxel grid model includes multiple voxel grids, each voxel grid corresponding to a candidate installation position of the displacement monitoring device; determining the relative angle information between each candidate installation position and the monitored area based on the monitored point cloud data, and determining the installation adaptation index of each candidate installation position for the monitored area based on the relative angle information; and selecting the target installation position of the displacement monitoring device from multiple candidate installation positions based on the installation adaptation index of each candidate installation position for the monitored area.

[0045] This application determines the installation compatibility index of each candidate installation location for the monitored area by using the relative angle information between the candidate installation location and the monitored area, so as to screen out the preferred target installation location for the displacement monitoring equipment. In this way, by quantitatively analyzing the suitability of the installation location of the displacement monitoring equipment, the preferred installation location can be selected, thereby improving the accuracy and stability of the monitoring data of the monitoring equipment.

[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 One of the flowcharts of a displacement monitoring device location method provided in this application embodiment is shown;

[0049] Figure 2 This invention provides a schematic diagram of the structure of a location selection device for a displacement monitoring equipment according to an embodiment of the present application.

[0050] Figure 3This is a second schematic diagram of the structure of a location selection device for a displacement monitoring equipment provided in an embodiment of this application;

[0051] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0053] Research has shown that long-term automated displacement monitoring requires prior site selection for the installation of the displacement monitoring equipment, and the quality of the site selection directly affects the monitoring effectiveness. Currently, existing technologies typically rely on operators to manually select sites based on their experience and the site environment. However, this manual site selection method heavily depends on the operator's experience, making it impossible to quantitatively assess the quality of the site selection. This can easily lead to unreasonable installation locations for the displacement monitoring equipment, thus affecting its monitoring performance.

[0054] Based on this, embodiments of this application provide a method and apparatus for selecting a displacement monitoring device. By using the relative angle information between candidate installation locations and the monitored area, the installation adaptability index of each candidate installation location for the monitored area is determined, so as to screen out the preferred target installation location for the displacement monitoring device. In this way, by quantitatively analyzing the suitability of the installation location of the displacement monitoring device, the preferred installation location can be selected, thereby improving the accuracy and stability of the monitoring data of the monitoring device.

[0055] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for selecting a location for a displacement monitoring device, as provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the location selection method includes:

[0056] S101. Obtain the point cloud data of the monitored area and the point cloud data of the candidate installation area of ​​the displacement monitoring equipment.

[0057] Here, the displacement monitoring device includes a device that determines whether displacement or deformation has occurred in the monitored area by measuring the distance between two points and analyzing the change in distance between the two measurements. For example, the displacement monitoring device in this application can be a laser rangefinder. A laser rangefinder measures distance by emitting and receiving laser light. For instance, the monitored area can be a mountain, and the laser rangefinder can monitor whether landslides or other deformations have occurred by monitoring whether the distance from the rangefinder to the mountain has changed. The installation location affects the effectiveness of the laser rangefinder in emitting and receiving laser light towards the monitored area, thus affecting the accuracy and stability of the measurement data.

[0058] In this step, point cloud data of the monitored area and point cloud data of the candidate installation area of ​​the displacement monitoring equipment can be obtained by LiDAR scanning or UAV aerial photography modeling. The point cloud data includes the three-dimensional spatial coordinates of multiple points.

[0059] S102. Establish a voxel mesh model of the candidate installation area based on the point cloud data of the candidate installation area.

[0060] The voxel grid model includes multiple voxel grids, and each voxel grid corresponds to a candidate installation location of the displacement monitoring device.

[0061] In a specific implementation, step S102 may include: determining the bounding box of the point cloud data of the candidate installation area based on the range of change of the three-dimensional spatial coordinates of each monitoring point in the point cloud data of the candidate installation area; dividing the bounding box into voxel meshes according to a preset resolution to obtain the voxel mesh model.

[0062] Bounding box is an algorithm for finding the optimal bounding space of a discrete point set. The basic idea is to approximate a complex geometric object using a slightly larger, simpler geometric shape (called a bounding box). For example, a bounding box can be a regular spatial cube, and the coordinates of each vertex of the spatial cube can be determined based on the range of variation of the three-dimensional spatial coordinates of each monitoring point.

[0063] Furthermore, after determining the bounding box of the point cloud data for the candidate installation area, considering the allowable elevation for erection or excavation when installing the monitoring equipment, the determined bounding box is expanded vertically along the elevation direction to obtain the preferred bounding box of the point cloud data for the candidate installation area, which is then used for voxel mesh generation, thereby improving the calculation accuracy for subsequently determining the preferred installation location.

[0064] S103. Based on the monitored point cloud data, determine the relative angle information between each candidate installation location and the monitored area, and determine the installation adaptation index of each candidate installation location for the monitored area based on the relative angle information.

[0065] Here, research has revealed that the accuracy of laser point cloud acquisition is directly related to the incident angle of the laser, as the angle at which the laser strikes the target surface affects the size of the laser spot and the intensity of the reflected signal. Specifically, as the incident angle of the laser increases, the point cloud density decreases, while the point position deviation increases. The density and accuracy of the point cloud directly affect the accuracy and stability of displacement monitoring.

[0066] Therefore, based on the relative angle information between the candidate installation location and the monitored point cloud data of the monitored area, the installation adaptation index of each candidate installation location for the monitored area can be determined. The installation adaptation index is used to characterize the suitability of installing the monitoring equipment at the candidate installation location. The larger the installation adaptation index, the more suitable the candidate installation location is for installing displacement monitoring equipment.

[0067] In one possible implementation, step S103 may include:

[0068] S1031. Based on the spatial location information of the monitored point cloud data, the monitored area is divided into multiple local areas. The spatial location information includes the spatial distance between monitored points in the monitored point cloud data and the curvature and / or normal at each monitored point.

[0069] In one possible implementation, step S1031 may include:

[0070] Step 1: Determine the curvature and / or normal at each of the multiple monitored points included in the monitored point cloud data.

[0071] In this step, for any monitored point, the surface corresponding to the monitored point can be obtained by fitting the monitored point and a predetermined number of monitored points around the monitored point, and then the surface curvature and / or normal at the monitored point can be obtained.

[0072] Step 2: Based on the curvature and / or normal at each monitored point, select multiple monitored points that belong to the flat area of ​​the monitored region from the multiple monitored points.

[0073] Here, for areas with sharp shapes in the monitored area, such as piles of wild grass on a mountain or particularly protruding rocks, the laser of the displacement monitoring device may be deflected when it is directed at such sharp-shaped areas, resulting in inaccurate measurement results.

[0074] Normal and curvature are both geometric properties of point clouds. In this step, based on the curvature at each monitored point and a preset curvature threshold, multiple monitored points with lower curvature and higher point cloud flatness (i.e., belonging to flat regions) can be selected from multiple monitored points, thus excluding sharp features. Similarly, based on the normal at each monitored point, the angle between normal vectors of the point clouds can be determined, and then based on a preset angle threshold, multiple monitored points with smaller normal vector angles and higher point cloud flatness (i.e., belonging to flat regions) can be selected from multiple monitored points, thus excluding sharp features. These two methods can be applied individually or in combination, that is, monitored points whose normal and curvature simultaneously meet the requirements are identified as monitored points belonging to flat regions.

[0075] Step 3: Based on the spatial distance between each monitored point belonging to a flat area within the monitored region, the multiple monitored points are clustered into multiple local regions.

[0076] In this step, for each monitored point belonging to a flat area within the monitored region, a search is performed outwards from that monitored point. The monitored point and other monitored points whose spatial distance to it is less than a preset spatial distance threshold are grouped into one class, until the number of monitored points in the class to which the monitored point belongs reaches a preset threshold, or until all other monitored points whose spatial distance to the monitored point is less than the preset spatial distance threshold have been grouped into one class. In this way, multiple sets of monitored points obtained from clustering multiple monitored points can be determined, and each set of monitored points constitutes a local region, thus determining multiple local regions obtained from clustering multiple monitored points.

[0077] S1032. Based on the monitored point cloud data corresponding to each local area, determine the centroid incidence angle from each candidate installation location to the centroid of each local area.

[0078] The centroid of a local region can be determined based on the three-dimensional spatial coordinates of each monitored point in the local region.

[0079] In one possible implementation, step S1032 may include:

[0080] Step 1: Based on the monitored point cloud data corresponding to each local region, fit the fitting plane corresponding to each local region.

[0081] Step 2: Determine the centroid of each local region and the plane normal vector of the fitting plane corresponding to each local region.

[0082] In steps 1 and 2, the centroid of each local region, the plane equation of the corresponding fitted plane, and the plane normal vector can be obtained by fitting the three-dimensional spatial coordinates of each monitored point in the monitored point cloud data corresponding to each local region.

[0083] Step 3: For each candidate installation location, for each local region, determine the direction vector between the centroid of the local region and the candidate installation location.

[0084] In this step, the three-dimensional spatial coordinates of each candidate installation location can be subtracted from the three-dimensional spatial coordinates of the centroid of each local region to determine the direction vector between the centroid of the local region and the candidate installation location.

[0085] Step 4: Determine the angle between the direction vector from the local region to the centroid of the candidate installation position and the plane normal vector of the fitting plane corresponding to the local region as the centroid incidence angle from the candidate installation position to the centroid of the local region.

[0086] S1033. Based on the centroid incidence angle from each candidate installation location to the centroid of each local area, determine the installation adaptation index of each candidate installation location for each local area in the monitored area.

[0087] The installation compatibility index is negatively correlated with the centroid incident angle; negative correlation means that the two variables change in opposite directions. Specifically, the larger the centroid incident angle, the lower the installation compatibility index should be. This is because as the laser incident angle increases, the point cloud density decreases continuously, and the point position deviation increases continuously. The density and accuracy of the point cloud further affect the accuracy and stability of displacement monitoring. Accordingly, the installation compatibility index of the candidate installation location should be relatively low.

[0088] In one possible implementation, step S1033 may include: for each candidate installation location and each local area, determining the installation compatibility index of the candidate installation location for the local area using the following formula:

[0089]

[0090] In the formula, α represents the centroid incidence angle from the candidate installation position to the centroid of the local region; ad() represents the installation adaptation index of the candidate installation position for the local region; C1, C2, and C3 represent constants that can be determined experimentally. Here, the angle threshold of 60 degrees can also be adjusted according to the actual effect.

[0091] For example, the formula for determining the installation compatibility index of the candidate installation location for the local area, obtained through experiments in this application embodiment, is as follows:

[0092]

[0093] S104. Based on the installation compatibility index of each candidate installation location for the monitored area, select the target installation location for the displacement monitoring device from multiple candidate installation locations.

[0094] When the number of displacement monitoring devices is one, step S104 may include: determining the average value of the installation adaptation index of each candidate installation location for each local area in the monitored area; and determining the candidate installation location with the highest average value as the target installation location of the displacement monitoring device.

[0095] Where the number of displacement monitoring devices is N, where N is a positive integer greater than 1, step S104 may include: selecting N candidate installation locations multiple times from multiple candidate installation locations to obtain m groups of candidate installation locations, where m is a positive integer greater than 1; for each group of candidate installation locations, determining the maximum installation compatibility index between each candidate installation location in the group and each local area in the monitored area; determining the sum of the maximum installation compatibility indices corresponding to each local area as the installation compatibility index of the group of candidate installation locations; and determining the group of candidate installation locations with the highest installation compatibility index among the m groups of candidate installation locations as the target installation locations for the N displacement monitoring devices.

[0096] Here, when selecting m groups of candidate installation locations, a certain strategy can be adopted, such as exhaustive search, to ensure the field of view coverage of N displacement monitoring devices. That is, if a certain local area is not covered, the maximum installation adaptation index of that local area is low or zero, so that the candidate installation location with the highest installation adaptation index can take into account the field of view.

[0097] Furthermore, after selecting the target installation location for the displacement monitoring device from multiple candidate installation locations based on the installation compatibility index of each candidate installation location for the monitored area as described in S104, the site selection method further includes:

[0098] S105. The voxel mesh corresponding to the target installation location is further divided into voxel meshes to establish a refined voxel mesh model corresponding to the target installation location.

[0099] S106. Based on the adaptation index of each refined installation position for the monitored area, the precise installation position of the displacement monitoring device is selected again from multiple refined installation positions.

[0100] The refined voxel mesh model includes multiple refined voxel meshes, each of which corresponds to a refined installation position of the displacement monitoring device; the resolution of the refined voxel mesh model is smaller than that of the voxel mesh model.

[0101] By dividing the voxel mesh containing the target installation location into a smaller resolution, a finer-grained set of candidate installation locations can be obtained. Then, using the site selection methods in S103 and S104, a more precise installation location for the displacement monitoring device can be selected from this finer-grained set of candidate installation locations. In this way, a rough optimal installation location can be calculated first using a larger resolution, and then a more precise optimal installation location can be obtained using a smaller resolution. This achieves both higher precision in the optimal installation location and reduced computational load.

[0102] This application provides a method for selecting a location for a displacement monitoring device, comprising: acquiring point cloud data of a monitored area and point cloud data of a candidate installation area for the displacement monitoring device; establishing a voxel grid model of the candidate installation area based on the point cloud data of the candidate installation area; wherein the voxel grid model includes multiple voxel grids, each voxel grid corresponding to a candidate installation location of the displacement monitoring device; determining the relative angle information between each candidate installation location and the monitored area based on the monitored point cloud data, and determining the installation adaptation index of each candidate installation location for the monitored area based on the relative angle information; and selecting the target installation location of the displacement monitoring device from multiple candidate installation locations based on the installation adaptation index of each candidate installation location for the monitored area.

[0103] This application determines the installation compatibility index of each candidate installation location for the monitored area by using the relative angle information between the candidate installation location and the monitored area, so as to screen out the preferred target installation location for the displacement monitoring equipment. In this way, by quantitatively analyzing the suitability of the installation location of the displacement monitoring equipment, the preferred installation location can be selected, thereby improving the accuracy and stability of the monitoring data of the monitoring equipment.

[0104] Please see Figure 2 , Figure 3 , Figure 2 This is one of the structural schematic diagrams of a location selection device for a displacement monitoring equipment provided in an embodiment of this application. Figure 3 This is a second location selection device for a displacement monitoring equipment provided in an embodiment of this application. For example... Figure 2 As shown, the addressing device 200 includes:

[0105] The acquisition module 210 is used to acquire the monitored point cloud data of the monitored area and the point cloud data of the candidate installation area of ​​the displacement monitoring device;

[0106] The module 220 is used to establish a voxel grid model of the candidate installation area based on the point cloud data of the candidate installation area; wherein, the voxel grid model includes multiple voxel grids, and each voxel grid corresponds to a candidate installation position of the displacement monitoring device;

[0107] The determining module 230 is used to determine the relative angle information between each candidate installation location and the monitored area based on the monitored point cloud data, and to determine the installation adaptation index of each candidate installation location for the monitored area based on the relative angle information.

[0108] The filtering module 240 is used to filter the target installation location of the displacement monitoring device from multiple candidate installation locations based on the installation adaptability index of each candidate installation location for the monitored area.

[0109] Furthermore, when the determining module 230 determines the relative angle information between each candidate installation location and the monitored area based on the monitored point cloud data, and determines the installation adaptation index of each candidate installation location for the monitored area based on the relative angle information, the determining module is used to:

[0110] Based on the spatial location information of the monitored point cloud data, the monitored area is divided into multiple local areas;

[0111] Based on the monitored point cloud data corresponding to each local area, determine the centroid incidence angle from each candidate installation location to the centroid of each local area;

[0112] Based on the centroid incidence angle from each candidate installation location to the centroid of each local region, an installation adaptation index is determined for each candidate installation location relative to each local region of the monitored area; wherein, the installation adaptation index is negatively correlated with the centroid incidence angle.

[0113] Furthermore, the spatial location information includes the spatial distance between monitored points in the monitored point cloud data and the curvature and / or normal at each monitored point; when the determining module 230 divides the monitored area into multiple local areas based on the spatial location information of the monitored point cloud data, the determining module 230 is used to:

[0114] Determine the curvature and / or normal at each of the multiple monitored points included in the monitored point cloud data;

[0115] Based on the curvature and / or normal at each monitored point, select multiple monitored points that belong to the flat region of the monitored area from the plurality of monitored points;

[0116] Based on the spatial distance between each monitored point belonging to a flat area within the monitored region, the multiple monitored points are clustered into multiple local regions.

[0117] Furthermore, when determining the centroid incidence angle from each candidate installation location to the centroid of each local region based on the monitored point cloud data corresponding to each local region, the determining module 230 is used to:

[0118] Based on the monitored point cloud data corresponding to each local region, a fitting plane corresponding to each local region is obtained;

[0119] Determine the centroid of each local region and the plane normal vector of the fitted plane corresponding to each local region;

[0120] For each candidate installation location, and for each local region, determine the direction vector between the centroid of the local region and the candidate installation location;

[0121] The angle between the direction vector from the local region to the centroid of the candidate installation position and the plane normal vector of the fitted plane corresponding to the local region is determined as the centroid incidence angle from the candidate installation position to the centroid of the local region.

[0122] Furthermore, when determining the installation fit index of each candidate installation location for each local region in the monitored area based on the centroid incidence angle from each candidate installation location to the centroid of each local region, the determining module 230 is used to:

[0123] For each candidate installation location and each local area, the installation compatibility index of the candidate installation location for that local area is determined by the following formula:

[0124]

[0125] In the formula, α represents the centroid incidence angle from the candidate installation position to the centroid of the local region; ad() represents the installation adaptation index of the candidate installation position for the local region; C1, C2 and C3 represent constants.

[0126] Furthermore, when the number of displacement monitoring devices is one, when the filtering module 240 is used to filter the target installation location of the displacement monitoring device from multiple candidate installation locations based on the installation adaptation index of each candidate installation location for the monitored area, the filtering module 240 is used to:

[0127] Determine the average installation fit index of each candidate installation location for each local area within the monitored area;

[0128] The candidate installation location with the highest average value is determined as the target installation location of the displacement monitoring device;

[0129] When the number of displacement monitoring devices is N, where N is a positive integer greater than 1, when the filtering module 240 filters the target installation location of the displacement monitoring device from multiple candidate installation locations based on the installation compatibility index of each candidate installation location for the monitored area, the filtering module 240 is used to:

[0130] N candidate installation locations are selected multiple times from multiple candidate installation locations to obtain m sets of candidate installation locations, where m is a positive integer greater than 1;

[0131] For each group of candidate installation locations, for each local area in the monitored area, determine the maximum installation compatibility index between each candidate installation location in the group and that local area;

[0132] The sum of the maximum installation adaptation indices corresponding to each local area is determined as the installation adaptation index of the candidate installation locations in that group.

[0133] The candidate installation location with the highest installation adaptation index among the m groups of candidate installation locations is determined as the target installation location for the N displacement monitoring devices.

[0134] Furthermore, such as Figure 3 As shown, the addressing device further includes a refinement module 250; the refinement module 250 is used for:

[0135] The voxel mesh corresponding to the target installation location is further divided into voxel meshes to establish a refined voxel mesh model corresponding to the target installation location. The refined voxel mesh model includes multiple refined voxel meshes, and each refined voxel mesh corresponds to a refined installation location of the displacement monitoring device. The resolution of the refined voxel mesh model is smaller than that of the voxel mesh model.

[0136] Based on the adaptation index of each refined installation location for the monitored area, the precise installation location of the displacement monitoring device is further selected from multiple refined installation locations.

[0137] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.

[0138] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, they can perform the operations described above. Figure 1 The steps of the displacement monitoring device location method in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0139] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the displacement monitoring device location method in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0140] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0144] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A siting method of a displacement monitoring device, characterized by, The site selection method comprises: acquiring monitored point cloud data of a monitored area and point cloud data of a candidate installation area of a displacement monitoring device; establishing a voxel grid model of the candidate installation area based on the point cloud data of the candidate installation area; wherein the voxel grid model comprises a plurality of voxel grids, and each voxel grid corresponds to a candidate installation position of the displacement monitoring device; determining relative angle information between each candidate installation position and the monitored area according to the monitored point cloud data, and determining an installation adaptation index of each candidate installation position for the monitored area according to the relative angle information; screening a target installation position of the displacement monitoring device from the plurality of candidate installation positions according to the installation adaptation index of each candidate installation position for the monitored area.

2. The method of claim 1, wherein, The method comprises: dividing the monitored area into a plurality of local areas based on spatial position information of the monitored point cloud data; determining a centroid incident angle of each candidate installation position to a centroid of each local area based on the monitored point cloud data corresponding to each local area; determining an installation adaptation index of each candidate installation position for each local area in the monitored area based on the centroid incident angle of each candidate installation position to the centroid of each local area; wherein the installation adaptation index is negatively correlated with the centroid incident angle.

3. The method of claim 2, wherein, The spatial position information comprises spatial distances between monitored points in the monitored point cloud data, and curvatures and / or normals at each monitored point; and the method comprises: determining curvatures and / or normals at each monitored point in the monitored point cloud data; screening a plurality of monitored points belonging to a shape flat area in the monitored area from the plurality of monitored points according to the curvatures and / or normals at each monitored point; clustering the plurality of monitored points to obtain a plurality of local areas according to spatial distances between each monitored point belonging to the shape flat area in the monitored area.

4. The method of claim 2, wherein, The method comprises: fitting a fitting plane corresponding to each local area based on the monitored point cloud data corresponding to each local area; determining a centroid of each local area and a plane normal vector of the fitting plane corresponding to each local area; for each candidate installation position, determining a direction vector between the centroid of each local area and the candidate installation position; determining a vector included angle between the direction vector between the centroid of each local area and the candidate installation position and the plane normal vector of the fitting plane corresponding to each local area as a centroid incident angle of the candidate installation position to the centroid of each local area.

5. The method of claim 2, wherein, The determining, based on the centroid incidence angle of each candidate installation position to each local region centroid, of an installation adaptation index of each candidate installation position for each local region in the monitored region comprises: For each candidate installation position and each local region, the installation adaptation index of the candidate installation position for the local region is determined by the following formula: wherein denotes the centroid incidence angle of the candidate mounting position to the centroid of the local area; the installation fitness index of the candidate mounting position for the local area; 、 and denotes a constant.

6. The method of claim 2, wherein, When the number of displacement monitoring devices is one, the screening of a target installation position of the displacement monitoring device from a plurality of candidate installation positions according to the installation adaptation index of each candidate installation position for the monitored region comprises: determining the average value of the installation adaptation index of each candidate installation position for each local region in the monitored region; determining the candidate installation position with the highest average value as the target installation position of the displacement monitoring device; When the number of displacement monitoring devices is N, N being a positive integer greater than 1, the screening of a target installation position of the displacement monitoring device from a plurality of candidate installation positions according to the installation adaptation index of each candidate installation position for the monitored region comprises: selecting N candidate installation positions from a plurality of candidate installation positions multiple times to obtain m groups of candidate installation positions, m being a positive integer greater than 1; For each group of candidate installation positions, the maximum installation adaptation index of each candidate installation position in the group of candidate installation positions for each local region in the monitored region is determined; the sum of the maximum installation adaptation indices corresponding to each local region is determined as the installation adaptation index of the group of candidate installation positions; the group of candidate installation positions with the highest installation adaptation index among the m groups of candidate installation positions is determined as the target installation positions of the N displacement monitoring devices.

7. The method of claim 1, wherein, After the screening of a target installation position of the displacement monitoring device from a plurality of candidate installation positions according to the installation adaptation index of each candidate installation position for the monitored region, the site selection method further comprises: performing voxel grid division again on the voxel grid corresponding to the target installation position to establish a refined voxel grid model corresponding to the target installation position, wherein the refined voxel grid model comprises a plurality of refined voxel grids, each refined voxel grid corresponding to a refined installation position of the displacement monitoring device; the resolution of the refined voxel grid model is smaller than that of the voxel grid model; based on the adaptation index of each refined installation position for the monitored region, the precise installation position of the displacement monitoring device is screened again from a plurality of refined installation positions.

8. A siting device for a displacement monitoring apparatus, characterized in that The site selection device comprises: an acquisition module configured to acquire monitored point cloud data of a monitored region and point cloud data of a candidate installation region of a displacement monitoring device; an establishment module configured to establish a voxel grid model of the candidate installation region based on the point cloud data of the candidate installation region; wherein the voxel grid model comprises a plurality of voxel grids, each voxel grid corresponding to a candidate installation position of the displacement monitoring device; determining a relative angle information between each candidate installation position and the monitored region according to the monitored point cloud data, and determining an installation adaptation index of each candidate installation position for the monitored region according to the relative angle information; screening a target installation position of the displacement monitoring device from the plurality of candidate installation positions according to the installation adaptation index of each candidate installation position for the monitored region.

9. An electronic device, comprising: comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, the machine readable instructions are executed by the processor to perform the steps of the site selection method of the displacement monitoring device according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to perform the steps of the site selection method of the displacement monitoring device according to any one of claims 1 to 7.

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