CN117368943A - Slope monitoring and early warning method and system based on laser radar - Google Patents
Slope monitoring and early warning method and system based on laser radar Download PDFInfo
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Abstract
The application discloses a slope monitoring and early warning method and system based on a laser radar, and relates to the technical field of slope monitoring, wherein the method comprises the following steps: connecting a slope database, and extracting target slope characteristic parameters; according to the characteristic parameters of the target slope, the laser radar equipment is used for carrying out integral scanning on the target slope, and a point cloud data set is obtained; performing point cloud registration on the point cloud data set to construct a registration point cloud data set; the method comprises the steps of generating a comparison point cloud data set by an interactive slope history monitoring log set; based on the registration point cloud data set and the comparison point cloud data set, slope comparison is carried out, and a slope deformation result is obtained; and activating a slope deformation evaluator according to the target slope characteristic parameters, generating a target slope monitoring result, and carrying out slope deformation early warning. The method has the technical effects of high efficiency and accuracy on the curved surface target and good noise filtering capability on the slope.
Description
Technical Field
The invention relates to the technical field of slope monitoring, in particular to a slope monitoring and early warning method and system based on a laser radar.
Technical Field
The method brings great convenience to social life in the development of infrastructure construction and industry, and simultaneously, a large number of slopes which are widely distributed, numerous and different in structure are produced, and higher requirements are also put forward on slope monitoring, so that the traditional slope scanning monitoring based on total stations, which is widely used in slope monitoring calculation, has the technical problems of low efficiency, poor accuracy and poor noise processing capability on the slopes for curved surface targets.
Disclosure of Invention
The purpose of the application is to provide a slope monitoring and early warning method and system based on a laser radar. The method is used for solving the technical problems of low efficiency, poor accuracy and poor noise processing capability on the slope of a curved surface target in the prior art.
In view of the technical problems, the application provides a slope monitoring and early warning method and system based on a laser radar.
In a first aspect, the present application provides a slope monitoring and early warning method based on a laser radar, where the method includes:
connecting a slope database, and extracting target slope characteristic parameters; according to the characteristic parameters of the target slope, the laser radar equipment is used for carrying out integral scanning on the target slope, and a point cloud data set is obtained; performing point cloud registration on the point cloud data in the point cloud data set to construct a registration point cloud data set; the interactive slope history monitoring log set is used for generating a comparison point cloud data set based on the preamble slope monitoring log; based on the registration point cloud data set and the comparison point cloud data set, slope comparison is carried out, and a slope deformation result is obtained; activating a slope deformation evaluator according to the target slope characteristic parameters, mapping the slope deformation result to generate a target slope monitoring result, and performing slope deformation early warning according to the target slope monitoring result.
In a second aspect, the present application further provides a slope monitoring and early warning system based on a lidar, where the system includes:
the parameter acquisition module is used for connecting the slope database and extracting characteristic parameters of the target slope; the laser scanning module is used for integrally scanning the target slope by using laser radar equipment according to the characteristic parameters of the target slope to obtain a point cloud data set; the point cloud registration module is used for carrying out point cloud registration on the point cloud data in the point cloud data set to construct a registration point cloud data set; the historical point cloud acquisition module is used for interacting a slope historical monitoring log set and generating a comparison point cloud data set based on the preamble slope monitoring log; the deformation comparison module is used for carrying out slope comparison based on the registration point cloud data set and the comparison point cloud data set to obtain a slope deformation result; and the evaluation and early warning module is used for activating a slope deformation evaluator according to the target slope characteristic parameters, mapping the slope deformation result to generate a target slope monitoring result, and carrying out slope deformation early warning according to the target slope monitoring result.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
extracting characteristic parameters of the target slope by connecting the slope database; according to the characteristic parameters of the target slope, the laser radar equipment is used for carrying out integral scanning on the target slope, and a point cloud data set is obtained; performing point cloud registration on the point cloud data in the point cloud data set to construct a registration point cloud data set; the interactive slope history monitoring log set is used for generating a comparison point cloud data set based on the preamble slope monitoring log; based on the matching point cloud data set and the comparison point cloud data set, slope comparison is carried out, and a slope deformation result is obtained; activating a slope deformation estimator according to the characteristic parameters of the target slope, mapping the slope deformation result to generate a target slope monitoring result, and carrying out slope deformation early warning according to the target slope monitoring result. And further, the technical effects of high efficiency, high accuracy and good noise filtering capability on the side slope for the curved surface target are achieved.
The foregoing description is merely an overview of the technical solutions of the present application, and may be implemented according to the content of the specification, so that the technical means of the present application can be more clearly explained, and the following specific embodiments of the present application are given for more understanding of the above and other objects, features and advantages of the present application.
Drawings
Embodiments of the invention and the following brief description are described with reference to the drawings, in which:
FIG. 1 is a schematic flow chart of a slope monitoring and early warning method based on a laser radar;
FIG. 2 is a schematic flow chart of acquiring a point cloud data set by integrally scanning a target slope with laser radar equipment in a slope monitoring and early warning method based on the laser radar;
fig. 3 is a schematic structural diagram of a slope monitoring and early warning system based on a laser radar.
Reference numerals illustrate: the system comprises a parameter acquisition module 11, a laser scanning module 12, a point cloud registration module 13, a history point cloud acquisition module 14, a deformation comparison module 15 and an evaluation early warning module 16.
Detailed Description
The slope monitoring and early warning method and system based on the laser radar solve the technical problems of low efficiency, poor accuracy and poor noise processing capacity on the slope of a curved surface target in the prior art.
In order to solve the above problems, the technical embodiment adopts the following overall concept:
firstly, connecting a slope database, and extracting characteristic parameters of a target slope; then, according to the characteristic parameters of the target slope, the laser radar equipment is used for carrying out integral scanning on the target slope, and a point cloud data set is obtained; then, carrying out point cloud registration on the point cloud data in the point cloud data set, and constructing a registration point cloud data set; then, an interactive slope history monitoring log set generates a comparison point cloud data set based on the preamble slope monitoring log; further, based on the matching point cloud data set and the comparison point cloud data set, slope comparison is carried out, and a slope deformation result is obtained; and finally, activating a slope deformation estimator according to the characteristic parameters of the target slope, mapping the slope deformation result to generate a target slope monitoring result, and carrying out slope deformation early warning according to the target slope monitoring result. And further, the technical effects of high efficiency, high accuracy and good noise filtering capability on the side slope for the curved surface target are achieved.
In order to better understand the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments, and it should be noted that the described embodiments are only some examples of the present application, and not all examples of the present application, and it should be understood that the present application is not limited by the example embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.
Example 1
As shown in fig. 1, the application provides a slope monitoring and early warning method based on a laser radar, which comprises the following steps:
s100: connecting a slope database, and extracting target slope characteristic parameters;
optionally, the slope database is constructed based on the design construction scheme, drawing, engineering log and other data sources of the target slope. The target slope characteristic parameters comprise slope, height, soil type, vegetation coverage and the like of the slope. The characteristic parameters of the target slope are obtained through the interactive slope database, so that a basis is provided for the follow-up integral scanning of the target slope
S200: according to the characteristic parameters of the target slope, the laser radar equipment is used for carrying out integral scanning on the target slope, and a point cloud data set is obtained;
the laser radar, also called laser array distance scanner, is a non-contact surveying instrument, working on the basis of the time-of-flight principle. The position of a point on the surface of the object being measured relative to the instrument is calculated by measuring the time the light pulse is emitted from the instrument to the object and back, and the angle of the emitted light beam. The working principle of the laser radar comprises a continuously repeated scanning process, position information of a plurality of points on the surface of a measured target is obtained by emitting pulse laser beams, and the information is mapped to a coordinate system of an instrument. The collection of points forms point cloud data for representing the surface shape and structure of the object under test. Lidar generally includes the following components: and (3) a scanning assembly: consists of a plurality of laser transmitters and a plurality of receivers for transmitting laser beams and receiving returned optical signals. And a control processing component: and the system is used for controlling the operation of the scanning assembly and processing the received data to generate point cloud data. And a data transmission component: the data connection between the scanning component and the processing algorithm component is established, and the scanning data is transmitted to a subsequent processing unit. And a power supply assembly: for providing the required power supply for the lidar.
Further, as shown in fig. 2, according to the target slope characteristic parameter, the laser radar device is used to perform overall scanning on the target slope to obtain a point cloud data set, and step S200 further includes:
s210: the method comprises the steps of interacting a first user to obtain a first monitoring task;
s220: determining a laser radar equipment scanning scheme by taking the first monitoring task as a constraint condition and combining the target slope characteristic parameter, wherein the scanning scheme comprises a laser radar equipment model, a scanning station measuring sequence, a scanning interval and scanning times;
s230: and according to the scanning scheme, carrying out overall scanning on the target slope, obtaining N groups of original point cloud data, and generating the point cloud data set, wherein the N groups of original point cloud data correspond to the N times of scanning.
Optionally, the first monitoring task includes monitoring range, monitoring accuracy, monitoring purpose, and the like. The monitoring range identifies the scale characteristics of the monitored target slope, and influences the selection and arrangement of the laser radar equipment; based on the monitoring precision, the laser radar equipment model selection and the position setting of the scanning station can be performed; different monitoring purposes may require different data. If the monitoring purpose is to detect small changes, a higher density of scanning and more frequent monitoring are required. If the monitoring purpose is volumetric calculation, a high accuracy distance measurement is required.
Alternatively, if the monitoring range is large, multiple laser radar devices may be used to scan simultaneously or multiple times. Wherein, a plurality of laser radar equipment models are the same.
Optionally, the type of the laser radar device depends on the requirements of a monitoring task, the size of a side slope, the distance and other factors. Different types of lidar devices have different measurement accuracy and scanning ranges. The scanning station sequence is used to determine where to place the scanning station of the lidar device in order to obtain comprehensive slope data. The scan station sequence parameters include the position, height and angle of the station to maximize coverage of the slope surface. The scanning station measuring sequence parameters are obtained based on laser radar equipment model and first monitoring task measurement and calculation.
The scan interval refers to a time interval between two scans by performing a predetermined number of times at a certain interval. Optionally, the multiple scans include: after the same station is continuously scanned for a plurality of times at intervals, the subsequent stations in the scanning station measuring sequence are acquired to be scanned for a plurality of times at intervals, and after the scanning interval is passed, the scanning station measuring sequence is scanned for a second time.
The N groups of original point cloud data acquisition time intervals in the point cloud data set are all scanning intervals. The laser radar is used for carrying out integral scanning to obtain a large amount of accurate and dense three-dimensional coordinate point cloud data, and the laser radar has the technical effects of no need of contact, high acquisition efficiency, accurate acquisition result and abundant details.
S300: performing point cloud registration on the point cloud data in the point cloud data set to construct a registration point cloud data set;
point cloud registration in a point cloud data set refers to the alignment of point cloud data from multiple scan points and multiple receivers so that they all reference the same coordinate system, optionally the instrument coordinate system. The point cloud registration is used for ensuring that the point cloud data collected under different scanning positions and angles can be correctly corresponding in the same space so as to carry out subsequent analysis and modeling.
Optionally, the basic method of coordinate registration includes a pairing mode, a global mode and an absolute mode. The pairing mode and the global mode belong to a relative mode, and the coordinate system of each station is converted into the coordinate system of the station by taking the coordinate system of a certain scanning station as a reference. The common manifestations of these two approaches are: in the process of scanning, the coordinates of the set control points or targets are unknown before scanning, and adjacent measuring stations often need to be partially overlapped. In the absolute method, before scanning, the coordinate value of the control point is measured, each measuring station is converted into the coordinate system where the control point is located when the scanning data is processed, and overlapping among the measuring stations is not required.
Further, performing point cloud registration on the point cloud data in the point cloud data set to construct a registered point cloud data set, and step S300 further includes:
s310: extracting first original point cloud data in the point cloud data set, and carrying out point cloud data preprocessing on the first original point cloud data;
s320: performing point cloud registration on the preprocessed first original point cloud data based on a point cloud registration algorithm to obtain a 1PASS registration point cloud data set;
s330: traversing the original point cloud data of the rest groups in the point cloud data set, and obtaining a 2PASS (point cloud computing) alignment point cloud data set and a 3PASS alignment point cloud data set through the point cloud data preprocessing and the point cloud registration until N PASS alignment point cloud data sets;
s340: registering the 1PASS registration point cloud data set to the N PASS registration point cloud data set, and performing data cleaning to obtain the registration point cloud data set.
Optionally, the first original point cloud data is any one of N groups of the original point cloud data, including point cloud data of each scanning station in the same acquisition order. The point cloud data preprocessing method comprises a filtering technology and a smoothing technology, and is used for removing irregular distribution of data points caused by abnormal values and noise.
Optionally, the point cloud registration is divided into coarse registration (Coarse Registration) and fine registration (Fine Registration) according to the accuracy of the point cloud registration. The two stages aim to align the point cloud data with different positions and angles so that the point cloud data are correctly corresponding to each other under the same coordinate system. Wherein, in the coarse registration stage, the initial alignment of the point cloud data is realized by estimating the initial value of a rotation translation matrix without prior knowledge. This preliminary alignment aims to minimize the spatial deviation between the different point clouds. The method of coarse registration includes using an exhaustive RANSAC-based algorithm, a 4 PCS-based algorithm based on feature matching, etc. Through coarse registration, an initial transformation is found such that the point clouds are roughly aligned. Further, the fine registration is used to further optimize the registration of the point clouds on the basis of the coarse registration to minimize the spatial differences between them. The algorithm at this stage utilizes optimization techniques to fine tune the initial transformation to align the point cloud data as precisely as possible. Algorithms for fine registration include variations of ICP (Iterative Closest Point) such as robust ICP, point-to-plane ICP, point-to-line ICP, etc., as well as feature-based algorithms such as SAC-IA, FGR algorithms using FPFH features of the point cloud, or AO algorithms based on SHOT features and ICL algorithms using line features. Furthermore, algorithms that also include end-to-end machine learning-based algorithms may play a role in fine registration.
Optionally, the coarse registration is performed by selecting a 4PCS algorithm based on feature matching, so that the method has the technical effects of higher registration accuracy and high registration speed. The 4PCS algorithm based on feature matching is a rapid method for performing coarse registration on a plurality of point cloud data sets in a point cloud data set. This algorithm exploits the affine invariant nature, especially by finding as many approximate coplanar points in the target point cloud as possible, and 4 approximate coplanar points in the source point cloud corresponding to the target point cloud. Such correspondence may be achieved by finding an approximately congruent set of four points. Then, a transformation matrix is calculated by using a least square method, and then a plurality of groups of sets are selected in an iterative mode through a RANSAC algorithm framework. Finally, the optimal transformation is selected according to the evaluation criteria of the largest common point set (LCP).
Illustratively, a set of four points in a source point cloud is determined. Firstly, three points are randomly selected in a source point cloud, the area of a triangle formed by the three points is as large as possible, and the distance between the three points does not exceed a threshold range determined based on a given overlapping rate f of two point clouds. Then, traversing all points in the source point cloud, and carrying out calculation verification on each point to select the optimal fourth point. The fourth point needs to be coplanar with the plane formed by other three points as much as possible, namely the four points are not necessarily required to be coplanar, but the distance from the fourth point to the plane of the other three points is as small as possible, and the distance from the fourth point to the other three points meets the distance threshold range.
Further, based on the above method, four point set b= { B1, B2, B3, B4}, in the source point cloud, is determined, where (B1, B2) determines line segment 1, and (B3, B4) determines line segment 2. Then, the invariant d1= |b1-b2|, d2= |b3-b4| (constraint 1), the invariant ratio r1= |b1-e|/|b1-b2|, r2= |b3-e|/|b3-b4| (constraint 2) are calculated. Because the four points are not necessarily coplanar, nor are the two line segments necessarily intersecting, the center point of the nearest point connecting the two line segments may be used as the "intersection point". Then, in the target point cloud, all the point pairs are traversed, and the point pair sets R1, R2 satisfying the constraint 1 (certain error is allowed) are screened. It is expressed as:
then, all the point pair elements r1i= { (qi, qj) } in the point pair set R1 are traversed, the target intersection point e1i satisfying the invariant ratio R1 on its line segment is calculated, and then all the calculation results e are stored in the search Tree ANN Tree (approximate nearest neighbor search Tree, most commonly K-D Tree algorithm), and the corresponding Map (e 1 i) = r1i= (qi, qj) is constructed.
Then, all the point pair elements r2i= (qi, qj) in the point pair set R2 are traversed, a target intersection e2i satisfying the invariant ratio R2 on the line segment is calculated, and a corresponding Map (e 2 i) =r2i is constructed. Then traversing all e2i points, searching coincident points e1i in an acceptable error range in the ANN Tree constructed before, and if the coincident points e1i can be found, finding a corresponding approximate congruent four-point set Ui= { Map (e 1 i), map (e 2 i) } in Q. Finally, all approximately congruent four-point sets u= { U0, U1..}.
And finally, traversing all approximate congruent four-point sets Ui epsilon U, and calculating a corresponding transformation matrix Ti of each Ui and B by a least square method. And then, transforming the source point cloud P by using the transformation matrix to obtain P ', counting the maximum common point set (LCP) in P' and Q, and recording the transformation matrix of max (LCP) as the optimal transformation matrix T of the iteration and reserving. The process is iterated continuously, recording the optimal transformation. And obtaining a transformation matrix after iteration is finished, namely an optimal transformation matrix. And performing point cloud rough registration based on the optimal transformation matrix.
Optionally, N PASS registration is performed on the first to N-th original point cloud data in the point cloud data set, so as to unify the point cloud data acquired by the plurality of scanning stations in the same acquisition order into the same equipment coordinate system.
Optionally, performing point cloud registration on the 1PASS registration point cloud dataset to the N PASS registration point cloud dataset, for removing noise objects on the target slope that do not belong to the target slope, where the noise objects include impurities, vegetation, animals, and the like on the slope.
Optionally, the data cleaning is performed by analyzing the point cloud data from the 1PASS registration point cloud data set to the N PASS registration point cloud data set after the point cloud registration, and the analysis method includes obtaining euclidean distance, cosine similarity, manhattan distance, and the like of the same scanning points in the N PASS point cloud data set. And cleaning data for outliers or values with excessive position change in multiple scans.
Optionally, the cleaned point cloud data is verified and corrected to ensure accuracy and integrity of registration. Including checking coordinate system, unit, data quality, etc.
Further, performing point cloud registration on the point cloud data in the point cloud data set to construct a registered point cloud data set, and step S300 further includes:
s350: the laser radar device is provided with a CCD coaxial camera;
s360: based on the CCD coaxial camera, synchronous color images are acquired in the integral scanning, and a color image set is acquired;
s370: extracting color information in the color image set, and matching the color information to a point cloud data set of each measured point;
s380: and performing data cleaning according to a preset color threshold value to obtain the registration point cloud data set.
Optionally, the CCD coaxial camera works together with the laser radar and synchronously collects information, and the CD coaxial camera is used for collecting color images. Color information is extracted from the color image set and matched with the point cloud data set of each measured point. The color information is associated with the point cloud data. The matching is determined based on the projection relation between the position of the point cloud in the instrument coordinate system and the position of the color image in the point cloud.
Optionally, the color threshold is a color value set of the target slope or a color value set of a noise object not belonging to the target slope. And reserving the data points of the color value sets belonging to the target slope, and removing the data points of the color value sets belonging to the noise objects of the target slope. And cleaning the data according to a preset color threshold. The method can remove or correct the data points which do not meet the color standard more accurately, ensures the quality and consistency of the point cloud data, and is more suitable for subsequent analysis and application.
S400: the interactive slope history monitoring log set is used for generating a comparison point cloud data set based on the preamble slope monitoring log;
the slope history monitoring log set comprises a plurality of slope monitoring logs, and slope monitoring information in a past period of time may comprise data such as point cloud data, displacement, deformation, temperature and the like, wherein the plurality of slope monitoring logs have monitoring time identifiers.
Optionally, the comparison point cloud data set includes a displacement value, a shape variation, point cloud information, a slope shape, and the like of a monitoring period on each data point of the target slope. The comparison point cloud data set provides data support for analysis of subsequent slope change conditions.
S500: based on the registration point cloud data set and the comparison point cloud data set, slope comparison is carried out, and a slope deformation result is obtained;
optionally, performing historical monitoring analysis according to the alignment point cloud data set and the comparison point cloud data set. Including assessing slope stability, trend of change, possible risk, etc.
Optionally, first, the alignment point cloud data set and the comparison point cloud data set are preprocessed. Including cleaning, denoising, and registration of the data to ensure that both data sets are in the same coordinate system and that the data quality is good. Next, the set of registration point cloud data is compared to the set of comparison point cloud data. And matching and comparing corresponding points in the two point cloud data sets. Matching is performed based on the relative locations between the point clouds to determine that they represent the same geographic feature. Then, deformation analysis was performed. The method comprises the steps of calculating information such as displacement, shape change, volume change and the like between point cloud data between two time points, and is used for knowing deformation conditions of the slope.
S600: activating a slope deformation evaluator according to the target slope characteristic parameters, mapping the slope deformation result to generate a target slope monitoring result, and performing slope deformation early warning according to the target slope monitoring result.
Further, according to the target slope characteristic parameter, activating a slope deformation estimator to generate a target slope monitoring result, where step S600 further includes:
s610: the slope engineering design database is interacted to obtain a slope evaluation parameter set;
s620: the method comprises the steps of connecting the device with a big data slope monitoring platform in a communication way, and obtaining a sample deformation evaluation result library;
s630: constructing the slope deformation evaluator based on the slope evaluation parameter set and the sample deformation evaluation result library;
s640: and activating the slope deformation estimator to estimate the slope deformation according to the target slope characteristic parameters and combining the slope deformation result.
Optionally, the slope evaluation parameters include horizontal displacement, vertical displacement, horizontal displacement-time curve, vertical displacement-time curve, crack dislocation, and the like. The sample deformation evaluation result library comprises a plurality of groups of slope evaluation parameter values and corresponding slope evaluation results and is used for training a slope deformation evaluator.
Further, based on the slope evaluation parameter set and the sample deformation evaluation result library, the slope deformation evaluator is constructed, and step S630 further includes:
s631: traversing the slope evaluation parameter set based on the slope type to obtain a classification parameter set, wherein the classification parameter set comprises a plurality of groups of slope evaluation parameters corresponding to the slope type;
s632: traversing the sample deformation evaluation result library based on the slope type to obtain a classification sample set, wherein the classification sample set comprises a plurality of groups of sample deformation evaluation results corresponding to the slope type;
s633: matching the mapping relation between the classification parameter set and the classification sample set to construct a plurality of category deformation estimators, wherein the category deformation estimators are in one-to-one correspondence with a plurality of side slope types;
s634: constructing an evaluator head node based on the slope type, wherein the evaluator head node comprises a receiving port and a plurality of transmitting ports, and the transmitting ports are provided with slope type identifiers;
s635: and the plurality of transmitting ports are connected with the plurality of class deformation estimators in a matching way, so that the slope deformation estimators are generated.
Optionally, the slope types are classified into soil slopes and rock slopes according to stratum lithology; the method is divided into the following steps according to the rock stratum structure: layered structure side slopes, block structure side slopes and net structure side slopes; the relationship between the stratum tendency and the slope direction is divided into: forward slope, reverse slope and vertical slope. The plurality of category deformation estimators are in one-to-one correspondence with the plurality of slope types, so that slope deformation estimation based on the slope types is realized.
Optionally, the evaluator head node is configured to input the slope deformation result into the corresponding class deformation evaluator based on the slope class. The multiple transmitting ports of the head node of the evaluator are respectively connected with the input ends of multiple category deformation evaluators in a matching mode.
Optionally, the target slope monitoring result includes a slope deformation result, a slope monitoring early warning level, a slope early warning instruction and the like.
In summary, the slope monitoring and early warning method based on the laser radar provided by the invention has the following technical effects:
extracting characteristic parameters of the target slope by connecting the slope database; according to the characteristic parameters of the target slope, the laser radar equipment is used for carrying out integral scanning on the target slope, and a point cloud data set is obtained; performing point cloud registration on the point cloud data in the point cloud data set to construct a registration point cloud data set; the interactive slope history monitoring log set is used for generating a comparison point cloud data set based on the preamble slope monitoring log; based on the matching point cloud data set and the comparison point cloud data set, slope comparison is carried out, and a slope deformation result is obtained; activating a slope deformation estimator according to the characteristic parameters of the target slope, mapping the slope deformation result to generate a target slope monitoring result, and carrying out slope deformation early warning according to the target slope monitoring result. And further, the technical effects of high efficiency, high accuracy and good noise filtering capability on the side slope for the curved surface target are achieved.
Example two
Based on the same conception as the slope monitoring and early warning method based on the laser radar in the embodiment, as shown in fig. 3, the application also provides a slope monitoring and early warning system based on the laser radar, which comprises:
the parameter acquisition module 11 is used for connecting the slope database and extracting the characteristic parameters of the target slope;
the laser scanning module 12 is configured to perform overall scanning on the target slope by using laser radar equipment according to the target slope characteristic parameters, so as to obtain a point cloud data set;
the point cloud registration module 13 is configured to perform point cloud registration on the point cloud data in the point cloud data set, and construct a registration point cloud data set;
the history point cloud acquisition module 14 is configured to generate a comparison point cloud data set based on the preamble slope monitoring log;
the deformation comparison module 15 is used for performing slope comparison based on the registration point cloud data set and the comparison point cloud data set to obtain a slope deformation result;
and the evaluation and early warning module 16 is used for activating a slope deformation evaluator according to the target slope characteristic parameters, mapping the slope deformation result to generate a target slope monitoring result, and carrying out slope deformation early warning according to the target slope monitoring result.
Further, the laser scanning module 12 further includes:
the task acquisition unit is used for interacting a first user and acquiring a first monitoring task;
the scheme generating unit is used for determining a laser radar equipment scanning scheme by taking the first monitoring task as a constraint condition and combining the target slope characteristic parameters, wherein the scanning scheme comprises a laser radar equipment model, a scanning station measuring sequence, a scanning interval and scanning times;
and the scanning execution unit is used for carrying out integral scanning on the target slope according to the scanning scheme, obtaining N groups of original point cloud data and generating the point cloud data set, wherein the N groups of original point cloud data correspond to the scanning times for N times.
Further, the point cloud registration module 13 further includes:
the preprocessing unit is used for extracting first original point cloud data in the point cloud data set and preprocessing the point cloud data of the first original point cloud data;
the N PASS registration unit is used for carrying out point cloud registration on the preprocessed first original point cloud data based on a point cloud registration algorithm to obtain a 1PASS registration point cloud data set; traversing the original point cloud data of the rest groups in the point cloud data set, and obtaining a 2PASS (point cloud computing) alignment point cloud data set and a 3PASS alignment point cloud data set through the point cloud data preprocessing and the point cloud registration until N PASS alignment point cloud data sets;
and the denoising registration unit is used for registering the 1PASS registration point cloud data set to the NPASS registration point cloud data set, and performing data cleaning to obtain the registration point cloud data set.
Further, the evaluation early warning module 16 further includes:
the parameter extraction unit is used for interacting the slope engineering design database to obtain a slope evaluation parameter set;
the sample acquisition unit is used for being in communication connection with the big data slope monitoring platform to acquire a sample deformation evaluation result library;
the evaluator setting unit is used for constructing the slope deformation evaluator based on the slope evaluation parameter set and the sample deformation evaluation result library;
and the deformation evaluation unit is used for activating the slope deformation evaluator to evaluate the slope deformation by combining the slope deformation result according to the target slope characteristic parameters.
It should be understood that the embodiments mentioned in this specification focus on the differences from other embodiments, and the specific embodiment in the first embodiment is equally applicable to a slope monitoring and early warning system based on a laser radar described in the second embodiment, which is not further developed herein for brevity of description.
It should be understood that the embodiments disclosed herein and the foregoing description may enable one skilled in the art to utilize the present application. While the present application is not limited to the above-mentioned embodiments, obvious modifications and variations of the embodiments mentioned herein are possible and are within the principles of the present application.
Claims (7)
1. A slope monitoring and early warning method based on a laser radar is characterized by comprising the following steps:
connecting a slope database, and extracting target slope characteristic parameters;
according to the characteristic parameters of the target slope, the laser radar equipment is used for carrying out integral scanning on the target slope, and a point cloud data set is obtained;
performing point cloud registration on the point cloud data in the point cloud data set to construct a registration point cloud data set;
the interactive slope history monitoring log set is used for generating a comparison point cloud data set based on the preamble slope monitoring log;
based on the registration point cloud data set and the comparison point cloud data set, slope comparison is carried out, and a slope deformation result is obtained;
activating a slope deformation evaluator according to the target slope characteristic parameters, mapping the slope deformation result to generate a target slope monitoring result, and performing slope deformation early warning according to the target slope monitoring result.
2. The method of claim 1, wherein based on the target slope characteristic parameters, using a lidar device to perform an overall scan of the target slope to obtain a point cloud data set, comprising:
the method comprises the steps of interacting a first user to obtain a first monitoring task;
determining a laser radar equipment scanning scheme by taking the first monitoring task as a constraint condition and combining the target slope characteristic parameter, wherein the scanning scheme comprises a laser radar equipment model, a scanning station measuring sequence, a scanning interval and scanning times;
and according to the scanning scheme, carrying out overall scanning on the target slope, obtaining N groups of original point cloud data, and generating the point cloud data set, wherein the N groups of original point cloud data correspond to the N times of scanning.
3. The method of claim 2, wherein performing point cloud registration on the point cloud data in the point cloud data set to construct a registered point cloud data set comprises:
extracting first original point cloud data in the point cloud data set, and carrying out point cloud data preprocessing on the first original point cloud data;
performing point cloud registration on the preprocessed first original point cloud data based on a point cloud registration algorithm to obtain a 1PASS registration point cloud data set;
traversing the original point cloud data of the rest groups in the point cloud data set, and obtaining a 2PASS (point cloud computing) alignment point cloud data set and a 3PASS alignment point cloud data set through the point cloud data preprocessing and the point cloud registration until N PASS alignment point cloud data sets;
registering the 1PASS registration point cloud data set to the N PASS registration point cloud data set, and performing data cleaning to obtain the registration point cloud data set.
4. The method of claim 1 wherein activating a slope deformation estimator based on the target slope characteristic parameters to generate target slope monitoring results comprises:
the slope engineering design database is interacted to obtain a slope evaluation parameter set;
the method comprises the steps of connecting the device with a big data slope monitoring platform in a communication way, and obtaining a sample deformation evaluation result library;
constructing the slope deformation evaluator based on the slope evaluation parameter set and the sample deformation evaluation result library;
and activating the slope deformation estimator to estimate the slope deformation according to the target slope characteristic parameters and combining the slope deformation result.
5. The method of claim 4, wherein constructing the slope deformation estimator based on the set of slope estimation parameters and the sample deformation estimation result library comprises:
traversing the slope evaluation parameter set based on the slope type to obtain a classification parameter set, wherein the classification parameter set comprises a plurality of groups of slope evaluation parameters corresponding to the slope type;
traversing the sample deformation evaluation result library based on the slope type to obtain a classification sample set, wherein the classification sample set comprises a plurality of groups of sample deformation evaluation results corresponding to the slope type;
matching the mapping relation between the classification parameter set and the classification sample set to construct a plurality of category deformation estimators, wherein the category deformation estimators are in one-to-one correspondence with the side slope types;
constructing an evaluator head node based on the slope type, wherein the evaluator head node comprises a receiving port and a plurality of transmitting ports, and the transmitting ports are provided with slope type identifiers;
and the plurality of transmitting ports are connected with the plurality of class deformation estimators in a matching way, so that the slope deformation estimators are generated.
6. A method as claimed in claim 3, wherein the method further comprises:
the laser radar device is provided with a CCD coaxial camera;
based on the CCD coaxial camera, synchronous color images are acquired in the integral scanning, and a color image set is acquired;
extracting color information in the color image set, and matching the color information to a point cloud data set of each measured point;
and performing data cleaning according to a preset color threshold value to obtain the registration point cloud data set.
7. A laser radar-based slope monitoring and early warning system, the system comprising:
the parameter acquisition module is used for connecting the slope database and extracting characteristic parameters of the target slope;
the laser scanning module is used for integrally scanning the target slope by using laser radar equipment according to the characteristic parameters of the target slope to obtain a point cloud data set;
the point cloud registration module is used for carrying out point cloud registration on the point cloud data in the point cloud data set to construct a registration point cloud data set;
the historical point cloud acquisition module is used for interacting a slope historical monitoring log set and generating a comparison point cloud data set based on the preamble slope monitoring log;
the deformation comparison module is used for carrying out slope comparison based on the registration point cloud data set and the comparison point cloud data set to obtain a slope deformation result;
and the evaluation and early warning module is used for activating a slope deformation evaluator according to the target slope characteristic parameters, mapping the slope deformation result to generate a target slope monitoring result, and carrying out slope deformation early warning according to the target slope monitoring result.
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| CN118537381A (en) * | 2024-07-19 | 2024-08-23 | 水利部交通运输部国家能源局南京水利科学研究院 | Method for jointly calibrating water-to-water slope ratio of upstream dam slope of earth-rock dam in operation period |
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| CN118537381A (en) * | 2024-07-19 | 2024-08-23 | 水利部交通运输部国家能源局南京水利科学研究院 | Method for jointly calibrating water-to-water slope ratio of upstream dam slope of earth-rock dam in operation period |
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