CN118226400A - Dam abutment monitoring and falling stone tracing method based on point cloud data - Google Patents
Dam abutment monitoring and falling stone tracing method based on point cloud data Download PDFInfo
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Abstract
The invention discloses a dam abutment monitoring and falling stone tracing method based on point cloud data, which comprises the following steps: s1: the method comprises the steps of data acquisition and processing, namely generating point cloud data through unmanned aerial vehicle scanning, completing preprocessing of the data, and extracting needed landform information; s2: a deformation monitoring step, comprising: establishing a datum point, splicing the point cloud data, and performing deformation monitoring; s3: a step of falling stone reduction, comprising: and (5) scanning and sampling the falling rocks, extracting the characteristics of the falling rocks, and restoring the original positions after identifying and classifying. The method overcomes the defects that the existing dam deformation monitoring technology is difficult to cope with the weather complex condition and has low monitoring precision.
Description
Technical Field
The invention belongs to the field of geotechnical engineering deformation monitoring, and particularly relates to a dam abutment monitoring and falling stone tracing method based on point cloud data.
Background
1. Dam abutment monitoring prior art
The surge of the reservoir landslide and the instability of the dam abutment slope under extreme environments can influence the safety of the dam, and the current situation of inspection and monitoring of the side slope is: (1) In the aspect of daily and emergency inspection of side slope geological disasters, the traditional manual inspection method has the problems of large workload, high cost, large risk, large difficulty, low efficiency and the like. (2) After the database bank side slope data is manually collected, the database bank side slope data needs a long time for manual processing, analysis and judgment, and the change of the on-site geological conditions cannot be reflected in time. (3) In the aspect of traditional monitoring, the monitoring instruments are arranged in a sparse mode in a point-line mode, the dam power station reservoir bank side slope is difficult to cover completely, and a large number of monitoring blind areas exist.
The deformation of the dam abutment of the dam refers to the horizontal and vertical displacement of the mountain body supported by the two ends of the dam in the water storage and drainage process of the reservoir, and reflects the structural stability and safety of the dam. The reasons for deformation of the dam abutment are as follows: (1) The shear strength and deformation modulus of the rock mass are affected by the own properties of the rock mass of the dam, such as lithology, structural surface, fissures, weak interlayers and the like. (2) The rock mass of the dam abutment is subjected to load actions such as arch end thrust, water pressure, seepage pressure and the like transmitted by the dam body, so that the rock mass is subjected to stress change and deformation. (3) The rock mass of the dam is affected by environmental factors such as temperature, water level, earthquake and the like, so that the rock mass is subjected to phenomena such as expansion and contraction, hydration expansion and vibration.
Conventional monitoring methods include leveling, triangulation, corner measurement, and the like. The method has the advantages of simplicity, easiness, high precision and the like, and is suitable for monitoring the deformation of most dams. However, the conventional monitoring method requires a large amount of manual operation, and has low data acquisition and processing speeds. The automatic monitoring method comprises the following steps: the automatic monitoring method adopts advanced sensor technology and automatic data processing technology, and can realize real-time monitoring and data analysis of dam deformation. The automatic monitoring method has the advantages of high data acquisition speed, high processing precision, no need of manual intervention and the like, but requires higher equipment investment and technical support.
GNSS technology: GNSS is a technique for positioning and measuring using satellite signals, which can provide three-dimensional coordinates and time information with high accuracy, and is suitable for various aspects of dam deformation monitoring. The GNSS technology has the advantages of all-weather measurement, high positioning speed, continuous real-time, high automation degree and the like, but also has some problems such as signal interference or shielding, low vertical displacement precision and complex error analysis.
Along with the development of technology, the technology of monitoring the deformation of the dam is also continuously advancing, and the current development trend is as follows:
1) Multi-antenna GPS: satellite signals are received by a plurality of GPS receivers simultaneously, so that the accuracy and reliability of GPS measurement are improved, and real-time monitoring and dynamic analysis of dam deformation are realized.
The GPS system has the advantages of high measurement precision, simple and convenient operation, small instrument volume, convenient carrying, all-weather operation, no need of viewing between observation points and the like. The surveying and mapping industry in China mainly uses a GPS system for high-precision geodetic measurement and control measurement initially, builds measurement control networks of various types and grades, and has been researched and applied for many years, and the GPS technology has been fully applied in other measurement fields such as engineering measurement, deformation observation, aerial photogrammetry, marine measurement, acquisition of geographic data in geographic information systems and the like.
The GPS deformation monitoring technology with multiple antennas makes one GPS receiver capable of being connected to multiple antennas simultaneously. Therefore, only the antenna is required to be installed on each monitoring point, the receiver is not installed any more, and 10 or even more than 20 monitoring points share 1 receiver, so that the cost of the whole monitoring system is greatly reduced, and the working efficiency is greatly improved.
The GPS one-machine multi-antenna monitoring system adopts a GPS receiver and a plurality of antennas to form a GPS monitoring system for real-time monitoring. The system mainly comprises a GPS multi-antenna controller, an antenna array group, a base station, a data processing, a control center, a transmission system (comprising a signal amplifier), a power supply system and the like. The GPS signal enters the multi-antenna controller through the antenna array low noise signal amplifier, and the multi-antenna controller realizes the interference-free reception of the multipath signals. The data can be transmitted to the data processing control center through different communication modes for processing and understanding, and the entity modeling analysis, the database management and the monitoring report formation are monitored according to the data processed and understood.
The GPS multi-antenna controller consists of a hardware controller and a software controller, and is the basis for realizing a one-machine multi-antenna monitoring system. The GPS multi-antenna controller takes an embedded industrial control computer as a core and is provided with an LCD (liquid crystal display) and a double-frequency GPSOEM board and a control circuit board are integrated together to form a functional unit. The method combines the microwave switch technology, the computer real-time control technology and the like in the radio communication organically, thereby realizing that 1 GPS receiver can receive signals transmitted by a plurality of GPS antennas without mutual interference.
In the application of the GPS technology in dam deformation monitoring, 1 permanent GPS receiver is required to be respectively arranged on each deformation observation point and the GPS reference stations on two sides of the dam, so that the acquisition cost of GPS hardware equipment is very high. Huang Dingfa, ding Xiaoli, chen Yongji. Multiple antenna GPS automatic deformation monitoring system [ J ]. Railway school report, 2002, (6) 1 GPS multiple antenna change-over switch was developed successfully, multiple GPS antennas were connected with 1 GPS receiver, GPS antenna signals of each observation point were received in time sharing through the switch switching one by one, and the signals were transmitted to GPS data processing center for GPS network adjustment of multiple epochs, multiple measuring stations and multiple guard houses. Considering the safety and reliability of the monitoring system, a reasonable scheme is that 1 permanent GPS receiver dam body is respectively arranged on the GPS reference stations on two sides of the dam, and 1 multi-antenna GPS receiver system is arranged at all deformation observation points of the dam body.
2) A plurality of satellite navigation positioning system combinations: the method has the advantages of utilizing different satellite navigation positioning systems (such as GPS, GLONASS, BDS and the like), improving the coverage range and usability of dam deformation monitoring, and reducing the cost and complexity of dam deformation monitoring.
The GPS-based combined GLONASS Beidou satellite navigation positioning system, the GALLEO and other various tail-guard navigation positioning systems are one of main measures for improving and ensuring the safety and the precision of the automatic monitoring system for the deformation of the dam.
3) Multi-sensor intelligent data fusion: the characteristics of various sensors (such as an optical fiber sensor, a micro-electromechanical system sensor, a wireless sensor network and the like) are utilized to realize multi-parameter, multi-scale and multi-dimensional data acquisition of dam deformation monitoring, and the accuracy and the efficiency of dam deformation monitoring are improved through an intelligent data fusion technology, so that the intellectualization and the automation of dam deformation monitoring are realized.
The dam deformation monitoring utilizes the intelligent data fusion of multiple sensors, which is a process for realizing the intellectualization and automation of data by utilizing the characteristics of multiple sensors (such as an optical fiber sensor, a micro-electromechanical system sensor, a wireless sensor network and the like) to realize the data acquisition of multiple parameters, multiple dimensions and improving the precision and the efficiency of the data through an intelligent data fusion technology.
The main steps of the intelligent data fusion of the multiple sensors include:
data preprocessing: the original data of the sensor is subjected to operations such as filtering, calibration, synchronization, compression and the like, so that noise and errors are eliminated, and the quality and usability of the data are improved.
And (3) data characteristic extraction: and performing operations such as transformation, dimension reduction, clustering and the like on the preprocessed data to extract characteristics and rules of the data and reduce redundancy and complexity of the data.
Data fusion: according to different fusion layers (such as a data layer, a feature layer, a decision layer and the like), different fusion methods (such as a weighted average method, a Kalman filtering method, a Bayesian estimation method, a D-S evidence theory method, a fuzzy logic reasoning method, an artificial neural network method and the like) are adopted to synthesize and optimize data from different sensors so as to obtain more accurate and complete data.
Data analysis and application: and analyzing and evaluating the fused data to realize application targets such as visualization, classification, identification, prediction, control and the like of the data.
The main advantages of the intelligent data fusion of the multiple sensors are as follows:
The accuracy and the reliability of data are improved: by utilizing redundancy and complementarity of multiple sensors, uncertainty and errors of a single sensor are eliminated, and trust and consistency of data are enhanced.
Coverage and information amount of extension data: by utilizing the diversity and flexibility of a plurality of sensors, more data parameters and dimensions are acquired, and the richness and completeness of data are increased.
The intelligent and automatic data are realized: by utilizing the intelligent data fusion technology, the self-adaptive processing and optimization of the data are realized, the manual intervention and the cost are reduced, and the efficiency and the effect of the data are improved.
Li Mingjun et al have invented a dam deformation multi-measuring point prediction method, a terminal device and a storage medium method. Acquiring space position coordinates of a dam measuring point and historical monitoring data of a certain time period, wherein the space position coordinates and the historical monitoring data comprise water levels of the upper and the lower sides of the dam at each moment, boundary temperatures of the upper and the lower sides of the dam, and deformation monitoring values of the measuring point to be predicted; and inputting the spatial position coordinates of the measuring points, the water levels and the boundary temperatures of the upstream and downstream of the dam at each moment in a certain time period into a Bayes-LSTM network model, and training the Bayes-LSTM network model to obtain a prediction model by taking the deformation monitoring value of the measuring points to be predicted as the expected output of the Bayes-LSTM network model. The intelligent prediction model with multiple measuring points has high prediction precision, can be suitable for deformation measurement of measuring points at different positions, and can obtain deformation of multiple measuring points at the same time. However, the method has the advantages that the trained model is large, the model training parameters are large and complex, and the method is difficult to adapt to complex actual conditions.
Sang Haiwei et al discloses a reservoir dam deformation monitoring system and method, which relate to the technical field of reservoir dams, and the reservoir dam deformation monitoring system and method are used for receiving a target data transmission file according to a deduction platform, reading a plurality of sub-dam body node data in the target data transmission file, determining whether missing exists in the plurality of sub-dam body node data, reading adjacent data of the missing data, supplementing the missing data according to a preset similarity algorithm, improving the safety of data screening, effectively improving the matching degree between the data set transmission number and the actual channel condition by setting the data set transmission number according to the actual channel condition, further effectively improving the data transmission efficiency and the data transmission stability, and preventing the data set from being capable of being pertinently set according to parameters such as the actual channel saturation degree caused by a one-time random transmission mode. The monitoring system is complex and difficult to operate, more equipment is required for monitoring, and the cost is high.
Yang Yu and the like invent a set of reservoir dam deformation monitoring device, which belongs to the technical field of dam detection. When the dam body slightly deforms, the foundation pit building body pulls the traction rope, the traction rope extrudes and rubs the inner wall of the traction sleeve, so that the traction sleeve is driven to synchronously slide between the traction roll shaft and the limiting roll shaft, the deformation of the dam body is transmitted to the displacement sensor through the traction rope, and the breakage and falling caused by sliding between the traction rope and the traction sleeve are reduced. When the dam body is severely deformed, the foundation pit building body pulls the traction rope along with the dam body, the traction rope extrudes and slides to break the traction sleeve, the broken traction rope is limited by the traction roll shaft and the limiting roll shaft in a sliding manner, and the deformation of the dam body is absorbed while the impact of water waves on the traction rope is reduced. The method reduces the detachment of the haulage rope dam body caused by the deformation of the underwater dam body, and reduces the debugging and maintenance difficulty while facilitating the deformation monitoring of the underwater dam body. However, the cost required by the device is high, and the dam deformation monitored by the device cannot distinguish transverse deformation from vertical deformation, so that good data cannot be provided for dam risk prediction.
The method is an interactive mode in which the unmanned aerial vehicle is used for scanning the dam to obtain the point cloud data of the dam, then the point cloud data of the dam are monitored, a large amount of manual operation is not needed, and the accuracy can be guaranteed.
2. Slope falling stone identification method
A computer vision based method: the method mainly processes the side slope image, such as background modeling, image segmentation, feature extraction, classification and the like, so as to identify falling rocks or other abnormal objects in the image. The method has the advantages that the existing camera equipment can be utilized, the cost is low, but the complex analysis of the image is required, and the image is influenced by factors such as illumination, shielding and the like.
The method based on the acoustic emission technology comprises the following steps: the method mainly utilizes an instrument to collect signals emitted by the rock mass, analyzes and summarizes the signals through signal processing, and can detect the energy release and deformation damage process in the rock mass so as to predict the occurrence of side slope disasters. The method has the advantages that microscopic changes of the rock mass can be monitored in real time, but the signals need to be processed in a complex manner and are affected by noise interference.
The method based on unmanned aerial vehicle technology comprises the following steps: the method mainly comprises the steps of carrying sensors such as a laser radar or a camera by using an unmanned aerial vehicle, rapidly acquiring large-area high-density slope point cloud or image data, extracting edge point cloud or foreground targets through point cloud processing or image processing, and identifying isolated dangerous rock according to spatial characteristics of the dangerous rock, such as area, inclination angle, altitude difference and the like. The advantage of this approach is that it can cover complex terrain, acquire accurate spatial information, but requires high equipment costs and data processing capabilities.
Disclosure of Invention
The invention aims at: in order to overcome the defects that the existing dam deformation monitoring technology is difficult to cope with complex weather conditions and low in monitoring precision, the dam abutment monitoring and falling stone tracing method based on point cloud data is disclosed.
The aim of the invention is achieved by the following technical scheme:
A dam abutment monitoring and falling stone tracing method based on point cloud data comprises the following steps:
s1: the method comprises the steps of data acquisition and processing, namely generating point cloud data through unmanned aerial vehicle scanning, completing preprocessing of the data, and extracting needed landform information;
S2: a deformation monitoring step, comprising: establishing a datum point, splicing the point cloud data, and performing deformation monitoring;
S3: a step of falling stone reduction, comprising: and (5) scanning and sampling the falling rocks, extracting the characteristics of the falling rocks, and restoring the original positions after identifying and classifying.
According to a preferred embodiment, the point cloud data generation process in step S1 includes:
S11: starting the unmanned aerial vehicle and the laser radar, flying according to a preset route, transmitting and receiving laser signals to the ground by the laser radar, measuring the distance and the reflection intensity of a target object, and recording corresponding time stamp and gesture information;
S12: setting at least 3 targets as reference points in a stable area, establishing an RTK reference station, sending carrier phases acquired by the reference station to the unmanned aerial vehicle, and solving a difference coordinate;
S13: and downloading original data of the unmanned aerial vehicle and the laser radar, including point cloud data, track data, image data and the like, and storing the synchronous scanning data into a file.
According to a preferred embodiment, step S11 further comprises: and planning the route and the flying height of the unmanned aerial vehicle, and determining the scanning range and the overlapping degree of the laser radar according to the field angle, the angle resolution and the point frequency parameters of the laser radar.
According to a preferred embodiment, the preprocessing of the data in step S1 comprises: performing data analysis, motion distortion correction, track optimization and point cloud color attachment on the point cloud data generated by unmanned aerial vehicle scanning, so as to obtain a complete three-dimensional point cloud data set;
In step S1, post-processing the preprocessed three-dimensional point cloud data set includes: and generating point cloud filtering, point cloud classification, point cloud editing, DSM and DEM, and extracting the required topography and topography information.
According to a preferred embodiment, step S2 specifically comprises:
S21: importing data, comprising: importing the three-dimensional laser point cloud data of the same period obtained by scanning into three-dimensional laser processing software; importing three-dimensional laser point cloud data of different periods into three-dimensional laser scanning processing software;
S22: processing data, deleting irrelevant points in the point cloud data, and splicing by using at least 3 datum points; after the primary splicing is finished, overlapping the common area by using the contemporaneous data to accurately splice, continuously iterating by adopting an ICP method with the minimum sum of squares of the homonymous point spacing of the common area, and gradually improving the alignment precision by adjusting the parameter of the searching distance;
s23: establishing a model, and respectively gridding the obtained point cloud data based on a triangular mesh curved surface modeling method to obtain a dam abutment digital elevation model;
S24: and comparing the dam abutment data models, selecting a two-stage dam abutment digital elevation model to be compared, taking the early model as a reference, taking the later model as a sample, and carrying out displacement calculation analysis on homonymous points in the reference model and the sample model to obtain real-time dam abutment deformation information.
According to a preferred embodiment, in step S22, two site clouds with a common area are obtained as x=respectivelyA common regional point cloud M; x i and y i represent point cloud coordinates, and N x and N y represent the number of point clouds; r n,Tn is an nth rotation matrix and a translation matrix between two site cloud datasets;
The iterative algorithm flow is as follows:
according to the point cloud set of the public area, the distance between the M area in the Y and the corresponding point with the same name in the X is shortest after the M area in the Y is subjected to n-th rotation evidence and translation matrix R n,Tn iterative computation, and the translation rotation matrix is calculated Taking a minimum value solution;
(2) X n+1＝RnXn+Tn; the iteration ending signal is when omega (R n,Tn)-ω(Rn+1,Tn+1) is less than or equal to sigma; where ω represents the function map and σ represents the threshold.
According to a preferred embodiment, step S24 specifically comprises: acquiring an ith phase point cloud data Z i group registered based on a carrier phase differential measuring instrument, wherein the ith phase point cloud data Z i group is divided into a deformation area D i and a non-deformation area F i; after partitioning the point cloud data registered in different periods of the same coordinate system, generating poly data based on the non-deformation region F i as an overlapping common region point cloud set M, and performing adjustment registration processing on the two-period point cloud data Z m and Z n by adopting an S22 method; based on the assumption that the side slopes and the mountain of the two banks of the dam body cannot be deformed, the two-stage deformation area D m、Dn processed by the method is compared and analyzed to obtain high-precision actual dam face deformation information;
performing displacement calculation analysis on homonymous points in the reference model and the sample model to obtain real-time dam face deformation information, wherein the method comprises the following steps of:
The displacement value is calculated by the following formula:
Wherein, the coordinate of the point A on the reference dam face is (A x,Ay,Az); the coordinate of the identical point B of the sample test dam abutment is (B x,By,Bz); the AB displacement vector is v (C x,Cy,Cz); wherein C x＝Bx-Ax,Cy＝By-Ay,Cy＝By-Ay.
According to a preferred embodiment, step S3 comprises:
s31: constructing a falling stone recognition model based on Pointnet ++ and Pointnet models to extract features of different scales and layers;
S32: firstly, the falling stone identification model downsamples the input falling stone point cloud, and a method of sampling FPS by using the furthest point is used for selecting a preset number of center points as the representative of each region; then, dividing a plurality of local areas by using a Ball query method according to the coordinates and the radius of the center point, wherein each area contains a fixed number of points;
S33: performing feature extraction on each local area by using PointNet by using the falling stone recognition model, converting points in each area into coordinates relative to a central point, and obtaining feature vectors of each area through multi-layer convolution and maximum pooling;
S34: the falling stone recognition model carries out up-sampling on the down-sampled point cloud, and a nearest neighbor interpolation NNI method is used for distributing the feature vector of each region to points in the original point cloud, so that fine-granularity point cloud features are obtained;
S35: according to the global and local information of the captured falling stone point cloud, a falling stone area in the point cloud is identified, and the position and the size of the falling stone are calculated;
According to a preferred embodiment, step S3 further comprises:
S36: and scanning the rock walls in different periods, comparing to obtain the position areas where the falling rocks occur on the rock walls, downsampling the falling rocks on the rock walls through a falling rocks identification model after the search range is reduced, and matching with the falling rocks to restore the positions of the falling rocks after the characteristics are extracted.
According to a preferred embodiment, the falling rock identification model utilizes a downsampling module and an upsampling module to achieve downsampling and upsampling of the point cloud data.
The foregoing inventive concepts and various further alternatives thereof may be freely combined to form multiple concepts, all of which are contemplated and claimed herein. Various combinations will be apparent to those skilled in the art from a review of the present disclosure, and are not intended to be exhaustive or all of the present disclosure.
The invention has the beneficial effects that:
According to the dam abutment monitoring and falling stone tracing method based on the point cloud data, the dam abutments of different periods are scanned by the unmanned aerial vehicle, so that the point cloud data can be obtained to form a digital elevation model which correspondingly contains a large amount of information in high precision, and the dam abutment deformation is compared and monitored before and after for dam risk prediction. Based on the point cloud data, the falling rocks are identified and the original positions of the falling rocks are restored, so that the falling rocks risk can be predicted. The dam abutment monitoring and falling stone tracing method based on the point cloud data provides a scheme which is simpler and more convenient to operate, lower in required human resource loss and higher in efficiency.
Drawings
FIG. 1 is a schematic flow chart of a dam abutment monitoring and falling stone tracing method based on point cloud data;
FIG. 2 is a schematic diagram of the working principle of a falling rock identification model;
Fig. 3 is a schematic diagram of a point cloud image obtained by three-dimensional laser scanning of an unmanned aerial vehicle.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
Example 1
Referring to fig. 1, a dam abutment monitoring and falling stone tracing method based on point cloud data is shown, and the dam abutment monitoring and falling stone tracing method comprises the following steps.
Step S1: and a data acquisition and processing step, namely generating point cloud data through unmanned aerial vehicle scanning, finishing preprocessing of the data, and extracting the required topography and topography information.
The laser point cloud technology is a technology for forming a data set by scanning the surface of an object using a laser radar and converting a reflected laser beam into three-dimensional coordinate points. In this way, highly accurate, dense three-dimensional model data can be obtained, and is not affected by ambient light.
The laser point cloud technology utilizes a laser to emit very short laser pulses, and then focuses the very short laser pulses into tiny light spots through a lens to perform directional radiation, so that strong reflection is generated on the surface of an observed object. The receiver captures the reflected laser beam, measures the flight time of the laser beam, calculates the reflected position according to the speed of light, and converts the information into three-dimensional coordinate points, namely point cloud data.
Point cloud data (point cloud data) refers to a set of vectors in a three-dimensional coordinate system. The scan data is recorded in the form of dots, each dot containing three-dimensional coordinates and carrying other information about the properties of the dot, such as color, reflectivity, intensity, etc. The main characteristics of the point cloud data are that the point cloud data have high-precision, high-resolution and high-dimensional geometric information, and can intuitively represent the information of the shape, the surface, the texture and the like of an object in space. Processing and analysis of point cloud data typically requires the use of techniques of computer vision and computer graphics, such as point cloud filtering, registration, segmentation, reconstruction, identification and classification, and the like. Based on the characteristics that the point cloud data can store a large amount of characteristic information, the point cloud data is used for dam abutment deformation monitoring and falling stone tracing.
Specifically, the point cloud data generation process in step S1 includes:
Step S11: and starting the unmanned aerial vehicle and the laser radar, flying according to a preset route, transmitting and receiving laser signals to the ground by the laser radar, measuring the distance and the reflection intensity of a target object, and recording corresponding time stamp and gesture information.
Preferably, step S11 further includes: preparing unmanned aerial vehicle and laser radar equipment, installing sensors, a positioning and attitude-determining system, a data memory and the like, and performing parameter setting and calibration. And planning the route and the flying height of the unmanned aerial vehicle, and determining the scanning range and the overlapping degree of the laser radar according to the field angle, the angle resolution and the point frequency parameters of the laser radar.
Step S12: at least 3 targets are set in the stable area to serve as reference points, RTK reference stations are established, carrier phases acquired by the reference stations are sent to the unmanned aerial vehicle, and difference solving coordinates are obtained.
Step S13: and downloading original data of the unmanned aerial vehicle and the laser radar, including point cloud data, track data, image data and the like, and storing the synchronous scanning data into a file.
Preferably, in the process of three-dimensional laser scanning, the acquisition of the point cloud data is often influenced by factors such as object shielding, uneven illumination and the like, and blind spot scanning of an area of an object with a complex shape is easy to cause, so that a hole is formed. Meanwhile, as the scanning measurement range is limited, for large-size objects or large-range scenes, complete measurement cannot be carried out at one time, and multiple times of scanning measurement are needed, so that the scanning result is often a plurality of pieces of noisy point cloud data with different coordinate systems, the requirements of people on the reality and instantaneity of a digital model cannot be completely met, and the three-dimensional point cloud data are required to be subjected to preprocessing such as denoising, simplification, registration, hole filling and the like.
Preferably, the preprocessing of the data in step S1 includes: and carrying out data analysis, motion distortion correction, track optimization and point cloud color attachment on the point cloud data generated by unmanned aerial vehicle scanning, so as to obtain a complete three-dimensional point cloud data set.
Preferably, in step S1, post-processing the preprocessed three-dimensional point cloud data set includes: point cloud filtering, point cloud classification, point cloud editing, DSM and DEM generation, and extracting required topography and relief information, as shown in FIG. 3.
Step S2: the dam abutment deformation monitoring step comprises the following steps: and establishing a datum point, and splicing the point cloud data to perform deformation monitoring.
Specifically, step S2 specifically includes:
Step S21: importing data, comprising: importing the three-dimensional laser point cloud data of the same period obtained by scanning into three-dimensional laser processing software; and importing three-dimensional laser point cloud data of different periods into the three-dimensional laser scanning processing software.
Step S22: processing data, deleting irrelevant points in the point cloud data, and splicing by using at least 3 datum points; and after the primary splicing is finished, overlapping the common area by using the contemporaneous data to accurately splice, continuously iterating by adopting an ICP method with the minimum sum of squares of the homonymous point spacing of the common area, and gradually improving the alignment precision by adjusting the parameter of the searching distance.
Preferably, in step S22, two site clouds with a public area are acquired asA common regional point cloud M; x i and y i represent point cloud coordinates, and N x and N y represent the number of point clouds; r n,Tn is the nth rotation matrix and translation matrix between the two site cloud datasets.
The iterative algorithm flow is as follows:
according to the point cloud set of the public area, the distance between the M area in the Y and the corresponding point with the same name in the X is shortest after the M area in the Y is subjected to n-th rotation evidence and translation matrix R n,Tn iterative computation, and the translation rotation matrix is calculated Taking the minimum value to be solved.
(2) X n+1＝RnXn+Tn; the iteration ending signal is when omega (R n,Tn)-ω(Rn+1,Tn+1) is less than or equal to sigma;
ω represents a function map, solving the minimum of this function to solve for R n,Tn; σ represents a threshold value, which may be set in advance, and when the error reduction value of the last time of the iteration relative to the previous time is smaller than the threshold value, the iteration is stopped.
Step S23: and establishing a model, and respectively gridding the obtained point cloud data based on a triangular mesh curved surface modeling method to obtain a dam abutment digital elevation model.
Step S24: and comparing the dam abutment data models, selecting a two-stage dam abutment digital elevation model to be compared, taking the early model as a reference, taking the later model as a sample, and carrying out displacement calculation analysis on homonymous points in the reference model and the sample model to obtain real-time dam abutment deformation information.
Preferably, step S24 specifically includes: acquiring an ith phase point cloud data Z i group registered based on a carrier phase differential measuring instrument, wherein the ith phase point cloud data Z i group is divided into a deformation area D i and a non-deformation area F i; after partitioning the point cloud data registered in different periods of the same coordinate system, generating poly data based on the non-deformation region F i as an overlapping common region point cloud set M, and performing adjustment registration processing on the two-period point cloud data Z m and Z n by adopting an S22 method; based on the assumption that the side slopes and the mountain of the two banks of the dam body cannot deform, the two-stage deformation area D m、Dn processed by the method is compared and analyzed to obtain high-precision actual dam face deformation information.
Performing displacement calculation analysis on homonymous points in the reference model and the sample model to obtain real-time dam face deformation information, wherein the method comprises the following steps of:
The displacement value is calculated by the following formula:
Wherein, the coordinate of the point A on the reference dam face is (A x,Ay,Az); the coordinate of the identical point B of the sample test dam abutment is (B x,By,Bz); the AB displacement vector is v (C x,Cy,Cz); wherein C x＝Bx-Ax,Cy＝By-Ay,Cy＝By-Ay.
Step S3: a step of falling stone reduction, comprising: and (5) scanning and sampling the falling rocks, extracting the characteristics of the falling rocks, and restoring the original positions after identifying and classifying.
The identification of falling rocks can be regarded as a classification problem, with falling rocks being one type and non-falling rocks being one type. However, characteristics of the falling rocks are varied and difficult to confirm, for example, the shape and color of the falling rocks are affected by factors such as the kind of rock, the degree of weathering, the lighting conditions, etc., and may be similar to or different from the surrounding environment, and difficult to distinguish. The size and speed of the falling rocks are affected by factors such as slope, height, gravity, friction, etc., and may be very fast or very slow and difficult to capture. The occurrence time and place of the falling rocks are affected by rainfall, earthquake, artificial interference and other factors, and the falling rocks can occur anytime and anywhere and are difficult to predict. Therefore, the identification of the falling rocks needs to realize real-time monitoring and analysis of the falling rocks by means of advanced technologies and equipment such as computer vision, deep learning, multi-sensor intelligent data fusion and the like, so that the accuracy and the efficiency of the identification are improved, and data support is provided for road management and maintenance.
Specifically, referring to fig. 2, step S3 includes:
Step S31: and constructing a falling stone recognition model based on Pointnet ++ and Pointnet models to extract features of different scales and layers. The falling rock identification model utilizes a down-sampling module and an up-sampling module to realize down-sampling and up-sampling of point cloud data.
The downsampling module consists of three parts: sampling, grouping and PointNet. Sampling is to sample the point cloud in a far point mode, and a certain number of key points are selected as center points. Grouping is to select points within a certain radius around the center point as a local area according to the center point, and then apply PointNet to the points of the areas to extract the point characteristics. PointNet is a sub-network of multi-layer perceptrons (MLPs) and max pooling (Max Pooling) that can achieve both the displacement invariance and rotation invariance of the point cloud.
The up-sampling module is used for up-sampling the point clouds, recovering the number of the original point clouds and simultaneously retaining the local and global characteristics. The upsampling module consists of three parts: internationo, internationo and MLP. Interpolation is to perform backward Interpolation on point cloud, and map low-level point features to high-level point features according to distance weights. Concatenation is to splice the interpolated point features with the point features of the previous layer to form richer features. The MLP is used for further processing the spliced characteristics and improving the expression capacity of the characteristics.
Finally, a maximum pooling layer (max pooling) is adopted to obtain a point cloud characteristic matrix of the falling rocks for identification and classification.
Step S32: firstly, the falling stone identification model downsamples the input falling stone point cloud, and a method of sampling FPS by using the furthest point is used for selecting a preset number of center points as the representative of each region; then, according to the coordinates and the radius of the center point, a Ball query method is used to divide a plurality of local areas, and each area contains a fixed number of points.
Step S33: the falling rock identification model performs feature extraction on each local area by using PointNet, converts the points in each area into coordinates relative to a central point, and then obtains the feature vector of each area through multi-layer convolution and maximum pooling.
Step S34: the falling stone recognition model carries out up-sampling on the down-sampled point cloud, and the feature vector of each region is distributed to points in the original point cloud by using a nearest neighbor interpolation NNI method, so that fine-grained point cloud features are obtained.
Step S35: and identifying the falling stone area in the point cloud according to the global and local information of the captured falling stone point cloud, and calculating the position and the size of the falling stone.
Step S36: and scanning the rock walls in different periods, comparing to obtain the position areas where the falling rocks occur on the rock walls, downsampling the falling rocks on the rock walls through a falling rocks identification model after the search range is reduced, and matching with the falling rocks to restore the positions of the falling rocks after the characteristics are extracted.
According to the dam abutment monitoring and falling stone tracing method based on the point cloud data, the dam abutments of different periods are scanned by the unmanned aerial vehicle, so that the point cloud data can be obtained to form a digital elevation model which correspondingly contains a large amount of information in high precision, and the dam abutment deformation is compared and monitored before and after for dam risk prediction. Based on the point cloud data, the falling rocks are identified and the original positions of the falling rocks are restored, so that the falling rocks risk can be predicted.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The dam abutment monitoring and falling stone tracing method based on the point cloud data is characterized by comprising the following steps of:
s1: the method comprises the steps of data acquisition and processing, namely generating point cloud data through unmanned aerial vehicle scanning, completing preprocessing of the data, and extracting needed landform information;
S2: a deformation monitoring step, comprising: establishing a datum point, splicing the point cloud data, and performing deformation monitoring;
S3: a step of falling stone reduction, comprising: and (5) scanning and sampling the falling rocks, extracting the characteristics of the falling rocks, and restoring the original positions after identifying and classifying.
2. The dam abutment monitoring and falling stone tracing method as claimed in claim 1, wherein the cloud data generation process in step S1 comprises:
S11: starting the unmanned aerial vehicle and the laser radar, flying according to a preset route, transmitting and receiving laser signals to the ground by the laser radar, measuring the distance and the reflection intensity of a target object, and recording corresponding time stamp and gesture information;
S12: setting at least 3 targets as reference points in a stable area, establishing an RTK reference station, sending carrier phases acquired by the reference station to the unmanned aerial vehicle, and solving a difference coordinate;
S13: and downloading original data of the unmanned aerial vehicle and the laser radar, including point cloud data, track data, image data and the like, and storing the synchronous scanning data into a file.
3. The dam abutment monitoring and falling stone tracing method as claimed in claim 2, wherein step S11 further comprises: and planning the route and the flying height of the unmanned aerial vehicle, and determining the scanning range and the overlapping degree of the laser radar according to the field angle, the angle resolution and the point frequency parameters of the laser radar.
4. The dam abutment monitoring and falling rock tracing method as claimed in claim 2, wherein the preprocessing of the data in step S1 comprises: performing data analysis, motion distortion correction, track optimization and point cloud color attachment on the point cloud data generated by unmanned aerial vehicle scanning, so as to obtain a complete three-dimensional point cloud data set;
In step S1, post-processing the preprocessed three-dimensional point cloud data set includes: and generating point cloud filtering, point cloud classification, point cloud editing, DSM and DEM, and extracting the required topography and topography information.
5. The dam abutment monitoring and falling stone tracing method as claimed in claim 2, wherein the step S2 specifically comprises:
S21: importing data, comprising: importing the three-dimensional laser point cloud data of the same period obtained by scanning into three-dimensional laser processing software; importing three-dimensional laser point cloud data of different periods into three-dimensional laser scanning processing software;
S22: processing data, deleting irrelevant points in the point cloud data, and splicing by using at least 3 datum points; after the primary splicing is finished, overlapping the common area by using the contemporaneous data to accurately splice, continuously iterating by adopting an ICP method with the minimum sum of squares of the homonymous point spacing of the common area, and gradually improving the alignment precision by adjusting the parameter of the searching distance;
s23: establishing a model, and respectively gridding the obtained point cloud data based on a triangular mesh curved surface modeling method to obtain a dam abutment digital elevation model;
S24: and comparing the dam abutment data models, selecting a two-stage dam abutment digital elevation model to be compared, taking the early model as a reference, taking the later model as a sample, and carrying out displacement calculation analysis on homonymous points in the reference model and the sample model to obtain real-time dam abutment deformation information.
6. The dam abutment monitoring and falling stone tracing method as claimed in claim 5, wherein in step S22, two site clouds with public areas are obtained respectively as followsA common regional point cloud M; x i and y i represent point cloud coordinates, and N x and N y represent the number of point clouds; r n,Tn is an nth rotation matrix and a translation matrix between two site cloud datasets;
The iterative algorithm flow is as follows:
according to the point cloud set of the public area, the distance between the M area in the Y and the corresponding point with the same name in the X is shortest after the M area in the Y is subjected to n-th rotation evidence and translation matrix R n,Tn iterative computation, and the translation rotation matrix is calculated Taking a minimum value solution;
(2) X n+1＝RnXn+Tn; the iteration ending signal is when omega (R n,Tn)-ω(Rn+1,Tn+1) is less than or equal to sigma;
Where ω represents the function map and σ represents the threshold.
7. The dam abutment monitoring and falling stone tracing method as claimed in claim 5, wherein the step S24 specifically comprises: acquiring an ith phase point cloud data Z i group registered based on a carrier phase differential measuring instrument, wherein the ith phase point cloud data Z i group is divided into a deformation area D i and a non-deformation area F i; after partitioning the point cloud data registered in different periods of the same coordinate system, generating poly data based on the non-deformation region F i as an overlapping common region point cloud set M, and performing adjustment registration processing on the two-period point cloud data Z m and Z n by adopting an S22 method; based on the assumption that the side slopes and the mountain of the two banks of the dam body cannot be deformed, the two-stage deformation area D m、Dn processed by the method is compared and analyzed to obtain high-precision actual dam face deformation information;
performing displacement calculation analysis on homonymous points in the reference model and the sample model to obtain real-time dam face deformation information, wherein the method comprises the following steps of:
The displacement value is calculated by the following formula:
Wherein, the coordinate of the point A on the reference dam face is (A x,Ay,Az); the coordinate of the identical point B of the sample test dam abutment is (B x,By,Bz); the AB displacement vector is v (C x,Cy,Cz); wherein C x＝Bx-Ax,Cy＝By-Ay,Cy＝By-Ay.
8. The dam abutment monitoring and falling stone tracing method as claimed in claim 5, wherein the step S3 comprises:
s31: constructing a falling stone recognition model based on Pointnet ++ and Pointnet models to extract features of different scales and layers;
S32: firstly, the falling stone identification model downsamples the input falling stone point cloud, and a method of sampling FPS by using the furthest point is used for selecting a preset number of center points as the representative of each region; then, dividing a plurality of local areas by using a Ball query method according to the coordinates and the radius of the center point, wherein each area contains a fixed number of points;
S33: performing feature extraction on each local area by using PointNet by using the falling stone recognition model, converting points in each area into coordinates relative to a central point, and obtaining feature vectors of each area through multi-layer convolution and maximum pooling;
S34: the falling stone recognition model carries out up-sampling on the down-sampled point cloud, and a nearest neighbor interpolation NNI method is used for distributing the feature vector of each region to points in the original point cloud, so that fine-granularity point cloud features are obtained;
S35: and identifying the falling stone area in the point cloud according to the global and local information of the captured falling stone point cloud, and calculating the position and the size of the falling stone.
9. The dam abutment monitoring and falling stone tracing method as claimed in claim 8, wherein the step S3 further comprises:
S36: and scanning the rock walls in different periods, comparing to obtain the position areas where the falling rocks occur on the rock walls, downsampling the falling rocks on the rock walls through a falling rocks identification model after the search range is reduced, and matching with the falling rocks to restore the positions of the falling rocks after the characteristics are extracted.
10. The dam abutment monitoring and falling stone tracing method of claim 8, wherein the falling stone identification model utilizes a downsampling module and an upsampling module to realize downsampling and upsampling of point cloud data.
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