Deformation monitoring method, system and medium for rock wading slope

Abstract

The present application relates to the technical field of deformation monitoring of water-wading slopes, and specifically to a deformation monitoring method, system and medium applied to rock water-wading slopes, the method comprising: obtaining point cloud data of each area in the water-wading slope in each sampling period; determining the rock deformation index of each cluster corresponding to each area in the water-wading slope in each sampling period; determining the rock complexity, slope instability value, and minimum segmentation threshold of each area in the water-wading slope in each sampling period, combining the spatial partitioning algorithm, and performing nearest neighbor search on the point cloud data of two adjacent sampling periods to obtain the nearest neighbor point cloud data corresponding to each point cloud data; determining the slope deformation degree of each area in the water-wading slope in each sampling period; and monitoring and warning the deformation of the rock water-wading slope. The present application is conducive to improving the search accuracy of the nearest neighbor point cloud data, reducing subsequent calculation errors, and improving the monitoring accuracy of areas with higher deformation risks in rock water-wading slopes.

Description

Technical Field

[0001] The present application relates to the technical field of deformation monitoring of water-related slopes, and in particular to a deformation monitoring method, system and medium applied to rock water-related slopes. Background Art

[0002] Rock wading slopes are a key component in water conservancy project construction, and their stability has an important impact on the surrounding environment and the safety of people's lives and property. Building a complete monitoring and early warning system for wading slopes and issuing early warnings before the slopes become unstable has become an effective method for wading slope management. The deformation of rock wading slopes is a precursor to slope instability, and timely detection of slope deformation trends is the main content of the rock wading slope monitoring system.

[0003] 3D laser scanning technology is a non-contact active measurement technology developed based on traditional measurement technology, and has been widely used in the field of deformation monitoring of rock wading slopes. At present, the traditional KD-tree construction method uses the same threshold to establish a spatial index for the point cloud data of rock wading slopes, ignoring the characteristics of different rock deformation probabilities in different regions, resulting in an increase in the error of subsequent calculation of slope deformation degree, resulting in a decrease in the accuracy of deformation monitoring of rock wading slopes. Summary of the invention

[0004] In order to solve the above technical problems, a deformation monitoring method, system and medium applied to rock wading slopes are provided to solve the existing problems.

[0005] The solution to the technical problem of this application is to provide a deformation monitoring method, system and medium for rock wading slopes, including the following steps:

[0006] In a first aspect, an embodiment of the present application provides a deformation monitoring method applied to a rock wading slope, the method comprising the following steps:

[0007] Obtain the point cloud data of each area in the wading slope in each sampling period;

[0008] Cluster all point cloud data of each area in the wading slope in each sampling period; determine the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period according to the distance between the point cloud data in each cluster corresponding to each area and its cluster center, as well as the distribution discreteness of different point cloud data;

[0009] According to the position distribution relationship between the fitting planes of the point cloud data in different clusters corresponding to each area, combined with the rock mass deformation index, the rock mass complexity of each area in the wading slope in each sampling period is determined;

[0010] According to the difference of the rock deformation index of all clusters in the same area of ​​the wading slope in different sampling periods, and the frequency of previous acquisition of the point cloud data of each area of ​​the wading slope in each sampling period, combined with the rock mass complexity, the slope instability value of each area of ​​the wading slope in each sampling period is determined;

[0011] Based on the slope instability value, the minimum segmentation threshold of each area in the wading slope in each sampling period is determined, and the nearest neighbor search is performed on the point cloud data of two adjacent sampling periods in combination with the space partitioning algorithm to obtain the nearest neighbor point cloud data corresponding to each point cloud data;

[0012] According to the distance relationship between the point cloud data of each area in the wading slope in each sampling period and its nearest neighbor point cloud data, the slope deformation degree of each area in the wading slope in each sampling period is determined; the deformation of the rock wading slope is monitored and warned.

[0013] Preferably, the step of determining the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period includes:

[0014] Perform plane fitting on all point cloud data in each cluster to obtain the fitting plane corresponding to each cluster;

[0015] Calculate the discrete degree of the distance from all point cloud data in each cluster to the corresponding fitting plane;

[0016] Calculate the distance between each point cloud data in each cluster and its cluster center, recorded as the first distance;

[0017] The cumulative sum of the first distances of all point cloud data in each cluster is calculated, and the product of the cumulative sum and the discrete degree is used as the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period.

[0018] Preferably, the determining of the rock mass complexity of each area in the wading slope in each sampling period includes:

[0019] The rock mass deformation indexes of all clusters corresponding to each area in the wading slope in each sampling period are used to form a rock mass characteristic vector for each area;

[0020] No. The first sampling period in the wading slope The rock mass complexity of the region The calculation formula is: ,in, For the The first sampling period in the wading slope The modulus of the rock mass characteristic vector in each region, For the The first sampling period in the wading slope Region The clusters and The cosine value of the angle between the fitting planes corresponding to the clusters, For the The first sampling period in the wading slope The number of all clusters corresponding to the region, is an exponential function with a natural constant as its base.

[0021] Preferably, the determining of the slope instability value of each area in the wading slope in each sampling period includes:

[0022] Calculate the distance between the rock mass characteristic vector of each area in the wading slope in each sampling period and the same area in the wading slope in the adjacent sampling period, and record it as the second distance;

[0023] The sum of the acquisition frequencies of all point cloud data in each area of ​​the wading slope in each sampling period in all previous sampling periods is counted, and recorded as the sampling frequency of each area in the wading slope in each sampling period;

[0024] The sum of the second distance and the rock mass complexity is calculated, and the ratio of the sum to the sampling frequency is used as the slope instability value of each area in the wading slope in each sampling period.

[0025] Preferably, the minimum segmentation threshold of each area in the wading slope in each sampling period is determined by the calculation method as follows: ,in, For the The first sampling period in the wading slope The minimum segmentation threshold of the region, For the The maximum slope instability value of all areas in the wading slope during the sampling period, For the The minimum slope instability value of all areas in the wading slope during the sampling period is For the The first sampling period in the wading slope The slope instability value of the area is is the preset threshold, is the ceiling function.

[0026] Preferably, the step of obtaining the nearest neighbor point cloud data corresponding to each point cloud data includes:

[0027] Based on the minimum segmentation threshold, a spatial partitioning algorithm is used to establish a spatial index for all point cloud data in each area of ​​the wading slope in each sampling period;

[0028] Through the nearest neighbor search, the nearest neighbor points of each point cloud data of each area in the wading slope in each sampling period in the adjacent sampling period are recorded as the nearest neighbor point cloud data corresponding to each point cloud data of each area in the wading slope in each sampling period.

[0029] Preferably, the slope deformation degree of each area in the wading slope in each sampling period is the distance between all point cloud data of each area in the wading slope in each sampling period and all corresponding nearest neighbor point cloud data.

[0030] Preferably, the deformation monitoring and early warning of rock wading slopes includes:

[0031] The maximum value of the slope deformation in all historical sampling periods is recorded as the deformation warning threshold;

[0032] If the slope deformation degree of each area in the wading slope in each sampling period is greater than the deformation warning threshold, the rock wading slope has deformed; otherwise, the rock wading slope has not deformed.

[0033] In a second aspect, the present application also provides a deformation monitoring system for rock wading slopes, the system comprising:

[0034] A data acquisition module is used to obtain point cloud data of each area in the wading slope in each sampling period;

[0035] The deformation feature analysis module clusters all the point cloud data of each area in the wading slope in each sampling period; determines the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period according to the distance between the point cloud data in each cluster corresponding to each area and its cluster center, and the distribution discreteness of different point cloud data; determines the rock complexity of each area in the wading slope in each sampling period according to the position distribution relationship between the fitting planes where the point cloud data in different clusters corresponding to each area are located, combined with the rock deformation index; determines the rock deformation index of all clusters in the same area of ​​the wading slope in different sampling periods. The difference between the two sampling periods and the frequency of the point cloud data of each area in the wading slope in each sampling period is determined, combined with the rock mass complexity, to determine the slope instability value of each area in the wading slope in each sampling period; based on the slope instability value, determine the minimum segmentation threshold of each area in the wading slope in each sampling period, combine the space division algorithm, and perform the nearest neighbor search on the point cloud data of two adjacent sampling periods to obtain the nearest neighbor point cloud data corresponding to each point cloud data; determine the slope deformation degree of each area in the wading slope in each sampling period according to the distance relationship between the point cloud data of each area in the wading slope in each sampling period and its nearest neighbor point cloud data;

[0036] The deformation monitoring and early warning module is used to monitor and warn the deformation of the rock wading slope based on the slope deformation degree.

[0037] In a third aspect, an embodiment of the present application also provides a deformation monitoring medium applied to rock water-wading slopes, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned deformation monitoring methods applied to rock water-wading slopes are implemented.

[0038] This application has at least the following beneficial effects:

[0039] The present application clusters all point cloud data of each area in the wading slope in each sampling period; according to the distance between the point cloud data in each cluster corresponding to each area and its cluster center, and the distribution discreteness of different point cloud data, the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period is determined. The beneficial effect is that based on the clustering results, by analyzing the position distribution of the point cloud data, the steepness of the slope surface of the rock wading slope is reflected, and then the risk degree of rock deformation is reflected, and the terrain characteristics of different rock slopes in different areas of the rock wading slope are taken into account; according to the different clusters corresponding to each area, the rock deformation index of each cluster corresponding to each area is determined. The position distribution relationship between the fitting planes where the internal point cloud data is located is combined with the rock deformation index to determine the rock complexity of each area in the wading slope in each sampling period. Its beneficial effect lies in the influence of the angle between different rock slope surfaces corresponding to different clusters on the deformation of the rock wading slope, reflecting the complexity of the surface of the rock wading slope in the corresponding area; according to the difference in the rock deformation index of all clusters in the same area of ​​the wading slope in different sampling periods, and the frequency of previous collection of point cloud data in each area of ​​the wading slope in each sampling period, combined with the rock complexity, the rock complexity of each area in the wading slope in each sampling period is determined. The slope instability value of the area has the beneficial effect of taking into account the frequency of point cloud data collection, thereby reflecting the duration of water immersion of the rock mass in the corresponding area, the degree of erosion of the slope rock, and then reflecting the probability of deformation of the rock slope, thereby improving the accuracy of judging the instability and deformation of the slope; based on the slope instability value, the minimum segmentation threshold of each area in the wading slope in each sampling period is determined, combined with the space division algorithm, and the nearest neighbor search is performed on the point cloud data of two adjacent sampling periods to obtain the nearest neighbor point cloud data corresponding to each point cloud data, which has the beneficial effect of collecting point cloud data according to the different risks of instability and deformation in different areas. Using different KD-tree construction methods is beneficial to improving the search accuracy of the nearest neighbor point cloud data and reducing subsequent calculation errors; determining the slope deformation of each area in the wading slope in each sampling period based on the distance relationship between the point cloud data of each area in the wading slope and its nearest neighbor point cloud data; monitoring and early warning of the deformation of the rock wading slope, the beneficial effect is that through the difference between the point cloud data and its nearest neighbor point cloud data, the deformation degree of different areas of the rock wading slope is analyzed, and an alarm is issued in time when the slope is deformed, thereby improving the monitoring accuracy of areas with higher deformation risks in the rock wading slope. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The deformation monitoring method for rock wading slopes of the present application is further described in detail below in conjunction with the accompanying drawings.

[0041] Figure 1 A flowchart of the steps of the deformation monitoring method for rock wading slopes provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the clustering effect of point cloud data in each area provided in an embodiment of the present application;

[0043] Figure 3 A flowchart of the steps of a method for obtaining the rock deformation index of each cluster corresponding to each area in a wading slope in each sampling period provided in an embodiment of the present application;

[0044] Figure 4 A distribution histogram of slope deformation in all regions before improvement provided in an embodiment of the present application;

[0045] Figure 5 A distribution histogram of slope deformation in all areas after improvement provided in an embodiment of the present application;

[0046] Figure 6 A block diagram of a deformation monitoring system for rock wading slopes provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of this application more clear, the deformation monitoring method, system and medium for rock wading slope proposed in this application are further described in detail below in combination with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0049] See also Figure 1 , which shows a flowchart of a deformation monitoring method for rock wading slopes provided by an embodiment of the present application, the method comprising the following steps:

[0050] Step 1: Obtain the point cloud data of each area in the wading slope in each sampling period.

[0051] Stable reference points are arranged outside the wading slope, and a three-dimensional laser scanner is set up at the reference points. The three-dimensional laser scanner emits laser and measures the distance by the time it takes for the laser to reflect back, thereby obtaining the three-dimensional spatial coordinates of each point on the surface of the wading slope relative to the three-dimensional laser scanner. Therefore, taking the highest interface at the top of the wading slope as a reference, the three-dimensional laser scanner is used to scan the slope above the horizontal ground to obtain the point cloud data of the wading slope. The point cloud data of the wading slope are sampled once every T time to obtain the point cloud data of the wading slope in each sampling period.

[0052] Taking the reference point where the 3D laser scanner is located as the coordinate origin, a 3D space is constructed for all point cloud data. The X-axis is the horizontal rotation axis of the 3D laser scanner, the Y-axis is the vertical rotation axis of the 3D laser scanner, and the Z-axis is the laser emission direction. The xOy plane in the 3D space is divided into various regions, which means that the wading slope is divided into various regions, and all point cloud data are divided into different regions according to the horizontal and vertical coordinates. Thus, the point cloud data of each region in the wading slope in each sampling period is obtained;

[0053] Preferably, in this embodiment, point cloud data of the wading slope is collected every other day, and the xOy plane in the three-dimensional space is evenly divided into 0.5m×0.5m square areas. As other implementation methods, the implementer can set them according to the actual situation.

[0054] At this point, the point cloud data of each area in the wading slope in each sampling period are obtained.

[0055] Step 2: Cluster all point cloud data of each area in the wading slope in each sampling period; determine the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period according to the distance between the point cloud data in each cluster corresponding to each area and its cluster center, as well as the distribution discreteness of different point cloud data.

[0056] Among the many factors that affect the stability of rock wading slopes, topographic conditions are the most direct. The stress distribution of different forms of rock slopes in rock wading slopes is different, resulting in differences in the stability of the slopes. Therefore, the risk of slope deformation in each area of ​​the rock wading slope is different. In order to clarify the topographic characteristics of different areas in the rock wading slope, the location distribution of point cloud data in each area is analyzed, specifically:

[0057] Using cluster analysis algorithm, all point cloud data of each area in the wading slope in each sampling period are clustered to obtain multiple clusters;

[0058] Preferably, in this embodiment, the K-means clustering algorithm is used for clustering, wherein the number of clusters is set to 3. As other implementations, the implementer can set it according to the actual situation. The K-means clustering algorithm is a well-known technology and will not be described here. As other implementations, the implementer can adopt other methods of the prior art, such as hierarchical clustering algorithms, etc. This embodiment does not impose any special restrictions on this.

[0059] Furthermore, the clustering effect diagram of the point cloud data in each area provided in the embodiment of the present application is as follows: Figure 2 shown.

[0060] Perform plane fitting on all point cloud data in each cluster to obtain the fitting plane corresponding to each cluster;

[0061] Calculate the discrete degree of the distance from all point cloud data in each cluster to the corresponding fitting plane;

[0062] Preferably, in this embodiment, the least squares method is used for plane fitting, wherein the least squares method is a well-known technology and will not be described in detail here; secondly, the degree of dispersion is calculated by calculating the standard deviation of the Euclidean distance of all point cloud data in each cluster to the corresponding fitting plane. As other implementation methods, the implementer can adopt other methods of the prior art, such as variance, coefficient of variation, etc., and this embodiment does not impose any special restrictions on this.

[0063] Furthermore, based on the clustering results of the point cloud data in each area of ​​the rock wading slope and the location distribution of the point cloud data, the rock deformation index is determined to reflect the risk possibility of rock deformation in the corresponding area, specifically:

[0064] Calculate the distance between each point cloud data in each cluster and its cluster center, recorded as the first distance;

[0065] Preferably, in this embodiment, the Euclidean distance between each point cloud data in each cluster and its cluster center is calculated and recorded as the first distance; as other implementation methods, the implementer can use other methods of the prior art to measure the distance, such as Manhattan distance, etc., and this embodiment does not impose any special restrictions on this.

[0066] Calculating the cumulative sum of the first distances of all point cloud data in each cluster, and taking the product of the cumulative sum and the discrete degree as the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period;

[0067] Preferably, in this embodiment, the calculation formula for the rock mass deformation characteristic value of each cluster corresponding to each area in the wading slope in each sampling period is: ,in, For the The first sampling period in the wading slope The corresponding area The rock mass deformation index of each cluster; For the The first sampling period in the wading slope The corresponding area The degree of dispersion of clusters, For the The first sampling period in the wading slope The corresponding area The first Point cloud data, For the The first sampling period in the wading slope The corresponding area The cluster centers of the clusters, For the The first sampling period in the wading slope The corresponding area The number of all point cloud data in a cluster, represents the distance calculation function; secondly, is the first distance.

[0068] It should be noted that the steeper the slope of the rock wading slope, the higher the possibility of displacement deformation such as landslide; the larger the first distance, the sparser the distribution of point cloud data in the corresponding cluster, the steeper the slope of the rock wading slope, the greater the probability of displacement deformation; secondly, the more point cloud data in each cluster, the larger the rock mass in the corresponding area, the greater the risk of instability, and the need for key monitoring. In addition, the greater the degree of dispersion, the rougher the rock surface, the more likely it is to accumulate surface water on its rock surface, affecting the stability of its internal structure, causing the bite force of the rock layer to weaken, and making it more likely for the rock mass to slide and deform. The larger the obtained rock deformation index, the greater the risk of rock mass deformation.

[0069] Further, the flowchart of the steps of the method for obtaining the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period provided by the embodiment of the present application is as follows: Figure 3 shown.

[0070] At this point, the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period is obtained.

[0071] Step 3: Determine the rock complexity of each area in the wading slope in each sampling period according to the position distribution relationship between the fitting planes of the point cloud data in different clusters corresponding to each area, combined with the rock deformation index; determine the slope instability value of each area in the wading slope in each sampling period according to the difference in the rock deformation index of all clusters in the same area in the wading slope in different sampling periods, and the frequency of previously collecting the point cloud data of each area in the wading slope in each sampling period, combined with the rock complexity.

[0072] Furthermore, based on the rock mass deformation index and the positional relationship of the fitting planes corresponding to different clusters, the influence of the angles between different rock mass slopes on the deformation of the rock wading slope is analyzed to determine the rock mass complexity, which reflects the complexity of the rock wading slope surface in the corresponding area. Specifically,

[0073] The rock mass deformation indexes of all clusters corresponding to each area in the wading slope in each sampling period are used to form a rock mass characteristic vector for each area;

[0074] The calculation formula for the rock mass complexity of each area in the wading slope during each sampling period is: ,in, For the The first sampling period in the wading slope The rock mass complexity of the region, For the The first sampling period in the wading slope The modulus of the rock mass characteristic vector in each region, For the The first sampling period in the wading slope Region The clusters and The cosine value of the angle between the fitting planes corresponding to the clusters, For the The first sampling period in the wading slope The number of all clusters corresponding to the region, is an exponential function with a natural constant as its base.

[0075] Preferably, in this embodiment, by determining the normal vector of the fitting plane, the cosine similarity of the normal vectors of the two fitting planes is calculated, and the cosine value of the angle between the corresponding fitting planes of the two clusters is calculated, that is, It should be noted that the method for calculating the angle between two planes is a well-known technology and will not be described in detail here.

[0076] It should be noted that the greater the slope and surface roughness of the rock mass slope in each area, the greater the rock mass deformation index and the greater the modulus of the rock mass characteristic vector; secondly, the greater the angle between different rock mass slopes in each area of ​​the rock wading slope, the more difficult it is to maintain coupling between different rock masses in the wading slope, the greater the risk of faults and cross-section deformation at the rock mass interface, and the cosine value The smaller the The larger it is, the greater the complexity of the resulting rock mass, which means that the greater the complexity of the slope in the corresponding area is, the greater the risk of deformation.

[0077] Furthermore, the characteristic change degree of the point cloud data of each area of ​​the rock wading slope in two adjacent sampling periods and the immersion time of each area are analyzed as key factors reflecting the possibility of instability of the rock wading slope; according to the difference in the rock deformation characteristic vectors of the same area in adjacent sampling periods and the difference in the complexity of the rock mass, the slope instability value is determined to reflect the probability of instability deformation in the corresponding area of ​​the rock wading slope, specifically:

[0078] Calculate the distance between the rock mass characteristic vector of each area in the wading slope in each sampling period and the same area in the wading slope in the adjacent sampling period, and record it as the second distance;

[0079] Preferably, in this embodiment, the Euclidean distance of the rock mass characteristic vector between each area in the wading slope in each sampling period and the same area in the wading slope in the previous sampling period is calculated and recorded as the second distance; as other implementation methods, the implementer can adopt other methods of the prior art, such as DTW distance, etc., and this embodiment does not impose any special restrictions on this.

[0080] The sum of the acquisition frequencies of all point cloud data in each area of ​​the wading slope in each sampling period in all previous sampling periods is counted, and recorded as the sampling frequency of each area in the wading slope in each sampling period;

[0081] Calculating the sum of the second distance and the rock mass complexity, and taking the ratio of the sum to the sampling frequency as the slope instability value of each area in the wading slope in each sampling period;

[0082] Preferably, in this embodiment, the slope instability value of each area in the wading slope in each sampling period is calculated as follows: ,in, For the The first sampling period in the wading slope The slope instability value of the area is For the The first sampling period in the wading slope The rock mass characteristic vector of the region is For the The first sampling period in the wading slope The rock mass characteristic vector of the region is For the The first sampling period in the wading slope The rock mass complexity of the region, For the The first sampling period in the wading slope The sampling frequency of the area, is the distance calculation function, To preset a value greater than 0 to avoid the denominator being 0, in this embodiment, The value is 1. As other implementation methods, the implementer can set it according to the actual situation.

[0083] It should be noted that the slope instability value of all areas in the wading slope in the first sampling period is a preset instability threshold. In this embodiment, the value is 0.1. As for other implementation methods, the implementer can set it according to the actual situation.

[0084] It should be noted that if the rock mass is immersed in water for a long time, the slope rock will continue to be eroded, and the stability of the rock inside will be reduced; moreover, the rock at the bottom of the submerged slope will soften over time, resulting in insufficient stability. The lower the frequency of point cloud data collection in each area, the longer it is submerged in the water environment, the greater the probability of unstable deformation of the rock wading slope, and the larger the obtained slope instability value. Secondly, the larger the second distance, the greater the degree of characteristic change of the point cloud data of each area collected in adjacent sampling periods, and the greater the possibility of slope instability and deformation in the corresponding area. At the same time, the greater the complexity of the rock mass, the greater the risk of instability, and the larger the obtained slope instability value, the greater the probability of unstable deformation in the corresponding area of ​​the rock wading slope.

[0085] At this point, the slope instability value of each area in the wading slope in each sampling period is obtained.

[0086] Step 4: Based on the slope instability value, determine the minimum segmentation threshold of each area in the wading slope in each sampling period, combine the space division algorithm, and perform nearest neighbor search on the point cloud data of two adjacent sampling periods to obtain the nearest neighbor point cloud data corresponding to each point cloud data; determine the slope deformation of each area in the wading slope in each sampling period according to the distance relationship between the point cloud data of each area in the wading slope in each sampling period and its nearest neighbor point cloud data; monitor and warn the deformation of the rock wading slope.

[0087] Furthermore, the spatial partitioning algorithm is a commonly used data structure for processing point cloud data. Based on the slope instability value, a spatial partitioning tree is constructed for the point cloud data of each area in the wading slope in each sampling period. In order to improve the accuracy of obtaining the nearest neighbor point cloud data of each point cloud data, the minimum segmentation threshold is determined for different areas of the wading slope, specifically:

[0088] The calculation method of the minimum segmentation threshold of each area in the wading slope in each sampling period is: ,in, For the The first sampling period in the wading slope The minimum segmentation threshold of the region, For the The maximum slope instability value of all areas in the wading slope during the sampling period, For the The minimum slope instability value of all areas in the wading slope during the sampling period is For the The first sampling period in the wading slope The slope instability value of the area is is a preset threshold value. In this embodiment, the preset threshold value The value is 10. As other implementation methods, the implementer can set it according to the actual situation. is the ceiling function.

[0089] Preferably, in this embodiment, the space division algorithm performs space division by constructing a KD tree using a KD-tree algorithm, wherein the KD-tree algorithm is a well-known technology and will not be described in detail here. As other implementation methods, implementers can adopt other methods of the prior art, such as octrees, etc. This embodiment does not impose any special restrictions on this.

[0090] It should be noted that the greater the slope instability value of each area of ​​the rock wading slope, the greater the risk of unstable deformation, and the smaller the minimum segmentation threshold, which reflects the degree of subdivision of the point cloud data for KD-tree division. The higher the accuracy of the subsequent search for the nearest neighbor point cloud data of the point cloud data through KD-tree, the higher the accuracy of slope deformation monitoring.

[0091] It should be noted that the minimum segmentation threshold means that when constructing a KD-tree for point cloud data, if the number of point cloud data in a node is less than or equal to the minimum segmentation threshold, the generation of new nodes is stopped and the node is used as a leaf node.

[0092] Furthermore, based on the minimum segmentation threshold, a spatial index is established using KD-tree, and then the nearest neighbor point cloud data is quickly searched to determine the slope deformation degree to reflect the deformation degree of the corresponding area of ​​the rock wading slope, specifically:

[0093] Based on the minimum segmentation threshold, a KD-tree algorithm is used to establish a spatial index for all point cloud data in each area of ​​the wading slope in each sampling period;

[0094] Through the nearest neighbor search, the nearest neighbor point of each point cloud data of each area in the wading slope in each sampling period in the previous sampling period is recorded as the nearest neighbor point cloud data corresponding to each point cloud data of each area in the wading slope in each sampling period;

[0095] It should be noted that the KD-tree algorithm and the nearest neighbor search are well-known technologies and will not be described in detail here.

[0096] The distance between all point cloud data of each area in the wading slope in each sampling period and all its corresponding nearest neighbor point cloud data is taken as the slope deformation degree of each area in the wading slope in each sampling period;

[0097] Preferably, in this embodiment, the Hausdorff distance between all point cloud data of each area in the wading slope in each sampling period and all corresponding nearest neighbor point cloud data is used as the slope deformation degree of each area in the wading slope in each sampling period, wherein the calculation of the Hausdorff distance is a well-known technology and will not be repeated here.

[0098] It should be noted that, the greater the slope deformation degree, the greater the deformation degree of the corresponding area of ​​the rock wading slope.

[0099] Furthermore, by counting the range of slope deformation in different regions, the slope deformation distribution histogram of all regions calculated by the KD-tree algorithm provided in the embodiment of the present application before and after improvement is as follows: Figure 4 and Figure 5 As shown in the figure, the horizontal axis is the slope deformation degree and the vertical axis is the number of regions. Figure 4 After the traditional KD-tree algorithm is used to establish the spatial index, the distribution histogram of slope deformation in all regions is calculated, that is, the same segmentation threshold is used to establish the spatial index for the point cloud data in different regions. Figure 5 It is the distribution histogram of slope deformation in all areas after the KD-tree algorithm is improved. Figure 4 The slope deformation in most areas is distributed between 0 and 0.25 meters, and in a very small part of the area, the slope deformation is distributed between 0.25 and 0.9 meters. Figure 4 , Figure 5 In the paper, different minimum segmentation thresholds are used to construct spatial indexes for different areas and calculate slope deformation, which reduces the calculation error of slope deformation in some areas, thereby improving the monitoring accuracy of areas with high deformation risk in rock wading slopes.

[0100] When the monitoring of the rock wading slope begins, the point cloud data of each area of ​​the wading slope is scanned by a three-dimensional laser scanner every T time, and the point cloud data of each area in the wading slope in each historical sampling period is recorded;

[0101] The maximum value of the slope deformation in all historical sampling periods is recorded as the deformation warning threshold;

[0102] Preferably, in this embodiment, the point cloud data of each area in the wading slope of 100 historical sampling periods are continuously obtained, and the slope deformation of each area in the wading slope of 100 historical sampling periods is calculated. As other implementation methods, the implementer can set them according to actual conditions.

[0103] If the slope deformation degree of each area in the wading slope in each sampling period is greater than the deformation warning threshold, the rock wading slope has deformed; if the slope deformation degree of each area in the wading slope in each sampling period is less than or equal to the deformation warning threshold, the rock wading slope has not deformed.

[0104] The embodiment of the present application also provides a deformation monitoring system applied to a rock wading slope, including:

[0105] A data acquisition module is used to obtain point cloud data of each area in the wading slope in each sampling period;

[0106] The deformation feature analysis module clusters all the point cloud data of each area in the wading slope in each sampling period; determines the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period according to the distance between the point cloud data in each cluster corresponding to each area and its cluster center, and the distribution discreteness of different point cloud data; determines the rock complexity of each area in the wading slope in each sampling period according to the position distribution relationship between the fitting planes where the point cloud data in different clusters corresponding to each area are located, combined with the rock deformation index; determines the rock deformation index of all clusters in the same area of ​​the wading slope in different sampling periods. The difference between the two sampling periods and the frequency of the point cloud data of each area in the wading slope in each sampling period is determined, combined with the rock mass complexity, to determine the slope instability value of each area in the wading slope in each sampling period; based on the slope instability value, determine the minimum segmentation threshold of each area in the wading slope in each sampling period, combine the space division algorithm, and perform the nearest neighbor search on the point cloud data of two adjacent sampling periods to obtain the nearest neighbor point cloud data corresponding to each point cloud data; determine the slope deformation degree of each area in the wading slope in each sampling period according to the distance relationship between the point cloud data of each area in the wading slope in each sampling period and its nearest neighbor point cloud data;

[0107] The deformation monitoring and early warning module is used to monitor and warn the deformation of the rock wading slope based on the slope deformation degree.

[0108] Further, the block diagram of the deformation monitoring system for rock wading slopes provided in the embodiment of the present application is as follows: Figure 6 shown.

[0109] Based on the same inventive concept as the above method, an embodiment of the present application also provides a deformation monitoring medium applied to rock water-wading slopes, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned deformation monitoring methods applied to rock water-wading slopes are implemented.

[0110] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0111] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A deformation monitoring method applied to rock wading slopes, characterized in that: The method comprises the following steps: Obtain the point cloud data of each area in the wading slope in each sampling period; Cluster all point cloud data of each area in the wading slope in each sampling period; determine the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period according to the distance between the point cloud data in each cluster corresponding to each area and its cluster center, as well as the distribution discreteness of different point cloud data; According to the position distribution relationship between the fitting planes of the point cloud data in different clusters corresponding to each area, combined with the rock mass deformation index, the rock mass complexity of each area in the wading slope in each sampling period is determined; According to the difference of the rock deformation index of all clusters in the same area of ​​the wading slope in different sampling periods, and the frequency of previous acquisition of the point cloud data of each area of ​​the wading slope in each sampling period, combined with the rock mass complexity, the slope instability value of each area of ​​the wading slope in each sampling period is determined; Based on the slope instability value, the minimum segmentation threshold of each area in the wading slope in each sampling period is determined, and the nearest neighbor search is performed on the point cloud data of two adjacent sampling periods in combination with the space partitioning algorithm to obtain the nearest neighbor point cloud data corresponding to each point cloud data; According to the distance relationship between the point cloud data of each area in the wading slope in each sampling period and its nearest neighbor point cloud data, the slope deformation degree of each area in the wading slope in each sampling period is determined; the deformation of the rock wading slope is monitored and warned.

2. The deformation monitoring method for rock wading slopes according to claim 1, characterized in that: The step of determining the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period includes: Perform plane fitting on all point cloud data in each cluster to obtain the fitting plane corresponding to each cluster; Calculate the discrete degree of the distance from all point cloud data in each cluster to the corresponding fitting plane; Calculate the distance between each point cloud data in each cluster and its cluster center, recorded as the first distance; The cumulative sum of the first distances of all point cloud data in each cluster is calculated, and the product of the cumulative sum and the discrete degree is used as the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period.

3. The deformation monitoring method for rock wading slopes according to claim 2, characterized in that: Determining the rock mass complexity of each area in the wading slope in each sampling period includes: The rock mass deformation indexes of all clusters corresponding to each area in the wading slope in each sampling period are used to form a rock mass characteristic vector for each area; No. The first sampling period in the wading slope The rock mass complexity of the region The calculation formula is: ,in, For the The first sampling period in the wading slope The modulus of the rock mass characteristic vector in each region, For the The first sampling period in the wading slope Region The clusters and The cosine value of the angle between the fitting planes corresponding to the clusters, For the The first sampling period in the wading slope The number of all clusters corresponding to the region, is an exponential function with a natural constant as its base.

4. The deformation monitoring method for rock wading slopes according to claim 3 is characterized in that: Determining the slope instability value of each area in the wading slope in each sampling period includes: Calculate the distance between the rock mass characteristic vector of each area in the wading slope in each sampling period and the same area in the wading slope in the adjacent sampling period, and record it as the second distance; The sum of the acquisition frequencies of all point cloud data in each area of ​​the wading slope in each sampling period in all previous sampling periods is counted, and recorded as the sampling frequency of each area in the wading slope in each sampling period; The sum of the second distance and the rock mass complexity is calculated, and the ratio of the sum to the sampling frequency is used as the slope instability value of each area in the wading slope in each sampling period.

5. The deformation monitoring method for rock wading slope according to claim 1, characterized in that: The minimum segmentation threshold for each area in the wading slope in each sampling period is determined by the following calculation method: ,in, For the The first sampling period in the wading slope The minimum segmentation threshold of the region, For the The maximum slope instability value of all areas in the wading slope during the sampling period, For the The minimum slope instability value of all areas in the wading slope during the sampling period is For the The first sampling period in the wading slope The slope instability value of the area is is the preset threshold, is the ceiling function.

6. The deformation monitoring method for rock wading slope according to claim 1, characterized in that: The obtaining of the nearest neighbor point cloud data corresponding to each point cloud data includes: Based on the minimum segmentation threshold, a spatial partitioning algorithm is used to establish a spatial index for all point cloud data in each area of ​​the wading slope in each sampling period; Through the nearest neighbor search, the nearest neighbor points of each point cloud data of each area in the wading slope in each sampling period in the adjacent sampling period are recorded as the nearest neighbor point cloud data corresponding to each point cloud data of each area in the wading slope in each sampling period.

7. The deformation monitoring method for rock wading slope according to claim 1, characterized in that: The slope deformation degree of each area in the wading slope in each sampling period is the distance between all point cloud data of each area in the wading slope in each sampling period and all corresponding nearest neighbor point cloud data.

8. The deformation monitoring method for rock wading slopes according to claim 1, characterized in that: The monitoring and early warning of deformation of rock wading slopes includes: The maximum value of the slope deformation in all historical sampling periods is recorded as the deformation warning threshold; If the slope deformation degree of each area in the wading slope in each sampling period is greater than the deformation warning threshold, the rock wading slope has deformed; otherwise, the rock wading slope has not deformed.

9. The deformation monitoring system applied to rock wading slopes is characterized by: The system comprises: A data acquisition module is used to obtain point cloud data of each area in the wading slope in each sampling period; The deformation feature analysis module clusters all the point cloud data of each area in the wading slope in each sampling period; determines the rock deformation index of each cluster corresponding to each area in the wading slope in each sampling period according to the distance between the point cloud data in each cluster corresponding to each area and its cluster center, and the distribution discreteness of different point cloud data; determines the rock complexity of each area in the wading slope in each sampling period according to the position distribution relationship between the fitting planes where the point cloud data in different clusters corresponding to each area are located, combined with the rock deformation index; determines the rock deformation index of all clusters in the same area of ​​the wading slope in different sampling periods. The difference between the two sampling periods and the frequency of the point cloud data of each area in the wading slope in each sampling period is determined, combined with the rock mass complexity, to determine the slope instability value of each area in the wading slope in each sampling period; based on the slope instability value, determine the minimum segmentation threshold of each area in the wading slope in each sampling period, combine the space division algorithm, and perform the nearest neighbor search on the point cloud data of two adjacent sampling periods to obtain the nearest neighbor point cloud data corresponding to each point cloud data; determine the slope deformation degree of each area in the wading slope in each sampling period according to the distance relationship between the point cloud data of each area in the wading slope in each sampling period and its nearest neighbor point cloud data; The deformation monitoring and early warning module is used to monitor and warn the deformation of the rock wading slope based on the slope deformation degree.

10. A deformation monitoring medium for rock wading slopes, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the deformation monitoring method applied to rock wading slopes as described in any one of claims 1-8 are implemented.

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