Slope rock mass information acquisition method and system based on multi-source remote sensing data fusion

Abstract

The invention discloses a slope rock mass information acquisition method based on multi-source remote sensing data fusion, and the method comprises the steps: multi-source data collection: collecting multi-source data based on an airborne laser radar technology, an unmanned plane oblique photography technology, and a ground three-dimensional laser scanning technology; performing multi-source point cloud fusion on the collected multi-source data; slope rock mass information acquisition: combining the fused multi-source point cloud data with the data advantages of each remote sensing point cloud so as to finely extract the rock mass surface state, geometric parameter information, trailing edge collapse and / or crack information, rock mass surface crack information and structural surface occurrence information of the slope rock mass; and verification of the extraction precision: using the fused multi-source point cloud to perform extraction and precision verification of geometric scale, trailing edge information, structural plane occurrence information and a structural plane part on the slope rock mass. The invention further discloses a corresponding evaluation system and device, electronic equipment and a computer readable storage medium.

Description

Technical Field

[0001] The present invention relates to the technical field of slope rock mass information acquisition, in particular to a method for acquiring slope rock mass information on complex terrain, large rock masses, and dangerous and hidden slopes, and specifically to a slope rock mass information acquisition method and system based on multi-source remote sensing data fusion. Background Art

[0002] The collection and acquisition of slope rock mass information is of great significance for rock slope stability analysis, geological disaster investigation and control, and the construction of bridge and road projects and water conservancy projects in mountainous and hilly areas.

[0003] At present, the main survey method for slope rock masses at home and abroad is to use drone oblique photogrammetry to generate tilt models, and then conduct relevant stability experiments based on the tilt models. Most domestic scholars use single laser scanning technology to obtain slope rock mass target point clouds, and then construct DEM, DOM or tilt models from the point clouds. By analyzing and interpreting the raster or model data, the slope rock mass can be identified. However, single remote sensing technology has certain technical disadvantages in slope rock mass surveys. It is impossible to obtain sufficient remote sensing data for slope rock mass parameter information extraction from a single data source, especially for slope rock mass information with complex terrain, large rock mass, dangerous and hidden characteristics. Summary of the Invention

[0004] The present invention addresses the shortcomings of existing slope rock mass information acquisition technologies and proposes a slope rock mass information acquisition method and system using multi-source remote sensing data fusion. The method comprises: multi-source data acquisition, which uses airborne lidar technology, unmanned aerial vehicle oblique photography technology, and ground-based three-dimensional laser scanning technology to acquire multi-source data; multi-source point cloud fusion, which sequentially performs coarse and fine point cloud registration on the acquired multi-source data, and then fuses and outputs the multi-source point cloud data; slope rock mass information acquisition, which combines the fused multi-source point cloud data with the data advantages of each remote sensing point cloud to perform refined extraction of geometric parameter information, trailing edge fracture information, and structural surface information of the slope rock mass; and extraction accuracy verification, which uses the fused multi-source point cloud to extract and accurately verify the geometric scale, trailing edge information, structural surface geometry, and structural surface geometry of the slope rock mass in the study area. The present invention has the advantages of strong operability, high computational accuracy, and high reliability of extraction results, and can be widely used in detailed information surveys of slope rock masses in vegetation-covered areas.

[0005] The first aspect of the present invention is to provide a method for acquiring slope rock mass information by fusing multi-source remote sensing data, which is used for acquiring slope rock mass information with complex terrain, large rock mass, dangerous and hidden characteristics, and includes: S1, multi-source data acquisition, including: collecting the multi-source data based on airborne laser radar technology, drone oblique photography technology, and ground-based three-dimensional laser scanning technology; S2, performing multi-source point cloud fusion on the collected multi-source data; S3, obtaining slope rock mass information, including: combining the fused multi-source point cloud data with the data advantages of each remote sensing point cloud, thereby finely extracting the rock mass surface state, geometric parameter information, trailing edge collapse and / or crack information, rock mass surface crack information, and structural surface occurrence information of the slope rock mass; S4, verifying the extraction accuracy, including: using the fused multi-source point cloud to perform geometric scale, trailing edge information and structural surface attitude information of the slope rock mass, as well as extraction and accuracy verification of the structural surface portion.

[0006] Preferably, the S1 includes: S11, obtaining high-resolution and high-precision two-dimensional topographic image data based on the airborne laser radar technology; S12, "penetrating" ground vegetation based on the airborne laser radar multiple echo technology, and effectively removing the influence of surface vegetation based on a filtering algorithm to obtain true ground elevation data information; S13, measuring and acquiring three-dimensional ground object information data using a drone tilt photography technique, wherein the three-dimensional ground object information data is represented as a real color point cloud; S14, based on ground-based three-dimensional laser scanning technology, obtains the spatial geometric features of the slope surface, which are represented by three-dimensional point coordinate data; thereby achieving long-distance, non-contact measurement, and can collect high-precision and high-density three-dimensional point coordinate data without any processing of the target object.

[0007] Preferably, the S2 includes: S21, performing point cloud coarse registration and point cloud fine registration on the collected multi-source data in sequence; S22: Based on the point cloud coarse registration and the point cloud fine registration, multi-source point cloud data is fused and output.

[0008] Preferably, the point cloud coarse registration includes: performing point cloud coarse registration using a four-point consistency method, selecting at least four points of the same name in a common area of ​​point clouds from different data sources, and converting the search for the four points of the same name into a search for the intersection of their cross-connecting lines.

[0009] Preferably, the point cloud fine registration includes: on the premise that the point cloud coarse registration obtains good initial registration conditions, using the iterative closest point method ICP algorithm to perform dense fine matching, and at the same time performing point cloud denoising and filtering on the obtained initial point cloud data.

[0010] Preferably, the multi-source point cloud data fusion includes: after completing the ICP algorithm fine alignment of the multi-source remote sensing data, using the drone oblique photography point cloud as a benchmark, repairing the holes in the void areas and the missing rock structure areas generated by the drone oblique photography scanning through the ground laser scanning point cloud and the airborne lidar point cloud, obtaining the real terrain information of the area, and merging and exporting the repaired point clouds to make them into a complete point cloud data, and then the fusion is completed.

[0011] Preferably, the S3 includes: S31, extracting information on the rock mass volume morphology based on the real color point cloud measured using the UAV tilt photography technology; S32, completely filtering out vegetation covering the rock surface based on the airborne laser radar multiple echo technology, and obtaining the surface state of the rock mass under the vegetation cover and the geometric parameter information of the slope rock mass; S33, completely filtering out vegetation covering the rear edge of the high and steep slope using the airborne lidar multiple echo technology, extracting information about the rear edge of the slope rock mass based on the fused point cloud data using a three-dimensional view and a pulled profile, and extracting information about collapse and / or cracks existing on the rear edge of the high and steep slope covered by vegetation; S34, based on the high-resolution and high-density point cloud of the ground laser radar, extracts the rock surface crack information and structural surface information; and directly calculates the normal vector of the point cloud, and automatically obtains the structural surface information of the point cloud through the corresponding relationship between the inclination, dip angle and normal vector, and automatically extracts the structural surface information of the slope rock.

[0012] The second aspect of the present invention is to provide a slope rock mass information acquisition system based on multi-source remote sensing data fusion, comprising: A data acquisition module (101) is used for multi-source data acquisition, including: acquiring the multi-source data based on airborne laser radar technology, unmanned aerial vehicle tilt photography technology, and ground three-dimensional laser scanning technology; A multi-source point cloud fusion module (102), configured to perform multi-source point cloud fusion on the collected multi-source data; A slope rock mass information acquisition module (103) is used to combine the fused multi-source point cloud data with the data advantages of each remote sensing point cloud, thereby finely extracting the rock mass surface state, geometric parameter information, rear edge collapse and / or crack information, rock mass surface crack information and structural surface occurrence information of the slope rock mass; The extraction accuracy verification module (104) is used to use the fused multi-source point cloud to extract and verify the geometric scale, trailing edge information and structural surface attitude information of the slope rock mass, as well as the structural surface part.

[0013] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method described in the first aspect.

[0015] Beneficial effects of the method and system of the present invention: (1) First, the 4PC method is used for coarse registration. Based on the coarse registration, the ICP algorithm is used for fine registration of point clouds, which can achieve a point cloud fusion method with smaller error for multi-source data point clouds.

[0016] (2) Through the fusion of multi-source point cloud data, the advantages of various remote sensing data are complemented, and the missing point clouds of dangerous rock masses on steep slopes are repaired, thus enriching the integrity of the data.

[0017] (3) Using the fused multi-source point cloud, the boundary scale, rear edge information, structural surface geometry information, and part of the structural surface of the dangerous rock mass on the steep slope of the study area were extracted. After verification, it was found to meet the accuracy requirements, and the extraction of geometric characteristic information of the dangerous rock mass was achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A flow chart of a method for acquiring slope rock mass information by fusing multi-source remote sensing data according to an embodiment of the present invention; Figure 2 A schematic flow chart of a method for acquiring slope rock mass information by fusing multi-source remote sensing data according to an embodiment of the present invention; Figure 3 Schematic diagram of the 4PC coarse registration method according to an embodiment of the present invention; Figure 4 A schematic diagram of the ICP fine registration principle provided according to an embodiment of the present invention; Figure 5 A schematic diagram of the multi-source data fusion principle provided according to an embodiment of the present invention; Figure 6 A schematic diagram of obtaining geometric scale volume information and trailing edge information of a slope rock mass according to an embodiment of the present invention; Figure 7 This is an architecture diagram of a slope rock mass information acquisition system using multi-source remote sensing data fusion according to an embodiment of the present invention; Figure 8 A structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0022] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances. Example 1

[0023] like Figure 1 and Figure 2 As shown, the first aspect of the present invention is to provide a method for obtaining slope rock mass information by fusing multi-source remote sensing data, which is used for obtaining slope rock mass information with complex terrain, huge rock mass, dangerous and hidden characteristics, including: S1, multi-source data acquisition, including: collecting the multi-source data based on airborne laser radar technology, drone oblique photography technology, and ground-based three-dimensional laser scanning technology; As a preferred embodiment, the S1 includes: S11, obtaining high-resolution and high-precision two-dimensional topographic image data based on the airborne laser radar technology; S12, "penetrating" ground vegetation based on the airborne laser radar multiple echo technology, and effectively removing the influence of surface vegetation based on a filtering algorithm to obtain true ground elevation data information; In this embodiment, the airborne lidar data has the advantages of high resolution, high precision, and strong penetration, but the point cloud density obtained by the airborne lidar scanning the vertical rock structure is low, and even the point cloud data of the rock area is missing, which cannot reflect the true rock structure characteristics of the vertical dangerous rock mass.

[0024] S13, measuring and acquiring three-dimensional ground object information data using a drone tilt photography technique, wherein the three-dimensional ground object information data is represented as a real color point cloud; In this embodiment, the drone oblique photography data obtained by the drone oblique photography technology not only includes the ditch bottom data of the study area, but also can meet the needs of identifying and acquiring three-dimensional ground feature information. However, the drone photogrammetry technology cannot penetrate vegetation, and there is a point cloud missing for the rock mass in the vegetation-covered area, and the complete rock mass structure characteristics cannot be obtained.

[0025] S14, based on ground-based three-dimensional laser scanning technology, obtains the spatial geometric features of the slope surface, which are represented by three-dimensional point coordinate data; thereby achieving long-distance, non-contact measurement, and can collect high-precision and high-density three-dimensional point coordinate data without any processing of the target object.

[0026] In this embodiment, terrestrial laser radar data acquired using terrestrial 3D laser scanning technology can capture the spatial geometric features of target surfaces, enabling long-distance, contactless measurement. This allows for high-precision, high-density acquisition of 3D point coordinate data without any processing of the target object, making the entire process convenient and efficient. However, 3D laser scanning can only capture data on the leading edge of high-level vertical dangerous rock masses. Terrestrial laser scanning cannot capture data on the trailing edge of the rock mass, including karst collapse, micro-cracks, and vegetation-covered areas.

[0027] S2, performing multi-source point cloud fusion on the collected multi-source data; As a preferred embodiment, the S2 includes: S21, performing point cloud coarse registration and point cloud fine registration on the collected multi-source data in sequence; In this embodiment, point cloud coarse registration involves finding common areas and selecting points of similarity when the relationship between multiple point clouds is unclear. This allows for a good rough registration of the multiple point clouds. This provides a good starting point for subsequent fine registration. The four-point consensus method (4PC coarse registration algorithm) is used for coarse point cloud registration. This method selects at least four points of similarity in the common area of ​​point clouds from different data sources, transforming the search for these four points into a search for the intersection of their connecting lines, thereby reducing algorithm complexity. The 4PC coarse registration algorithm provides a good initial condition for subsequent iterative closest point method fine registration, resulting in better fine registration results.

[0028] like Figure 3 As shown, the four coplanar points a(a'), b(b'), c(c'), and d(d') intersect at e(e'). There are two ratios that are invariant under rigid body changes, namely |de| / |ec|=|d'e'| / |e'c'|. 4PC converts the search for the four points into a search for e, e'. Figure 2 As shown in the schematic diagram of the 4PC coarse registration method, the greater the relative distance between the four points, the more accurate the calculated transformation. Using the 4PC coarse registration algorithm provides good initial conditions for subsequent iterative closest point fine registration, resulting in better fine registration results.

[0029] In this embodiment, the point cloud fine registration includes: on the premise that the point cloud coarse registration obtains good initial registration conditions, iterative closest point method (hereinafter referred to as ICP algorithm) is used to perform dense fine matching, and the initial point cloud data obtained is subjected to point cloud denoising, filtering and other processing to reduce the influence of noise on the registration error value of the ICP algorithm. The registration principle is as follows: Figure 4 shown.

[0030] S22: Based on the point cloud coarse registration and the point cloud fine registration, multi-source point cloud data is fused and output.

[0031] In this embodiment, the fusion of multi-source point cloud data includes: selecting a single remote sensing source data with relatively good data effect as the basic data, and using other remote sensing source data as supplementary data to achieve real repair of the single remote sensing source data using real point cloud data from other data sources, and merging the multi-source point cloud data on this basis.

[0032] Specifically, in this embodiment, after completing the ICP algorithm fine registration of the multi-source remote sensing data, the multi-source point cloud data fusion is performed, and the UAV oblique photography point cloud is used as a reference. The ground laser scanning point cloud and the airborne laser radar point cloud are used to repair the holes in the hollow areas and the missing rock structure areas generated by the UAV oblique photography scanning, and the real terrain information of the area is obtained. The repaired point cloud is then merged and exported to form a complete point cloud data, and the fusion is completed. The schematic diagram of its fusion principle is shown below. Figure 5 shown.

[0033] S3, acquisition of slope rock mass information, including: combining the fused multi-source point cloud data with the data advantages of each remote sensing point cloud, so as to perform refined extraction of the rock surface state, geometric parameter information, trailing edge collapse and / or crack information, as well as rock surface crack information and structural surface attitude information of the slope rock mass.

[0034] As a preferred embodiment, the S3 includes: S31, extracting information on the rock mass volume morphology based on the real color point cloud measured using the UAV tilt photography technology; S32, completely filtering out vegetation covering the rock surface based on the airborne laser radar multiple echo technology, and obtaining the surface state of the rock mass under the vegetation cover and the geometric parameter information of the slope rock mass; S33, completely filtering out vegetation covering the rear edge of the high and steep slope using the airborne lidar multiple echo technology, extracting information about the rear edge of the slope rock mass based on the fused point cloud data using a three-dimensional view and a pulled profile, and extracting information about collapse and / or cracks existing on the rear edge of the high and steep slope covered by vegetation; S34, based on the high-resolution and high-density point cloud of the ground laser radar, extracts the rock surface crack information and structural surface information; and directly calculates the normal vector of the point cloud, and automatically obtains the structural surface information of the point cloud through the corresponding relationship between the inclination, dip angle and normal vector, and automatically extracts the structural surface information of the slope rock.

[0035] S4, verifying the extraction accuracy, including: using the fused multi-source point cloud to perform geometric scale, trailing edge information and structural surface attitude information of the slope rock mass, as well as extraction and accuracy verification of the structural surface portion.

[0036] As a preferred embodiment, the S4 includes: S41, using the fused multi-source point cloud, extracts the geometric scale, trailing edge information, and structural surface geometry and information about the slope rock mass; S42, verify the accuracy of the geometric scale, trailing edge information, structural surface attitude geometry information and structural surface information of the slope rock mass.

[0037] Comparisons of the automatically clustered and extracted structural surface attitude information with actual field measurements are shown in Table 1. The results show that the maximum error in inclination between the structural surface attitudes automatically clustered and extracted using the watershed algorithm and those obtained through field measurements is 2.6, with an average error of 2.0. The maximum error in inclination is 3.3, with an average error of 1.8. Both meet the structural surface identification accuracy requirement of ±5°, demonstrating that the algorithm is effective in identifying vertical rock masses.

[0038] Table 1

[0039] like Figure 7 As shown, this embodiment provides a slope rock mass information acquisition system based on multi-source remote sensing data fusion, including: The data acquisition module 101 is used for multi-source data acquisition, including: acquiring the multi-source data based on airborne laser radar technology, drone oblique photography technology, and ground 3D laser scanning technology; A multi-source point cloud fusion module 102 is used to perform multi-source point cloud fusion on the collected multi-source data; The slope rock mass information acquisition module 103 is used to combine the fused multi-source point cloud data with the data advantages of each remote sensing point cloud, so as to perform refined extraction of the rock mass surface state, geometric parameter information, trailing edge collapse and / or crack information, rock mass surface crack information, and structural surface occurrence information of the slope rock mass; The extraction accuracy verification module 104 is used to use the fused multi-source point cloud to extract and verify the geometric scale, trailing edge information and structural surface attitude information of the slope rock mass, as well as the structural surface part.

[0040] The third aspect of the present invention is to provide an acoustic vibration-based ballastless track void hidden defect detection device for implementing the method of the first aspect. The device includes a track plate void detection intelligent terminal, which is installed on the track plate detection mechanism of the ballastless track and is used to identify the ballastless track void hidden defect in real time; the track plate void detection intelligent terminal includes a processor-based edge computing unit, a signal monitoring unit, a digital-to-analog conversion unit, and a communication unit.

[0041] In this embodiment, the track slab void detection intelligent terminal adopts an ultra-compact edge computing unit (tentative model MCM) based on the ARM Cortex-A9 processor, with a built-in 2-channel 24-bit high-resolution analog input, and can be used as an independent device without a host PC. It is very suitable for 24-hour vibration monitoring of rotating machinery and equipment; the track slab void detection intelligent terminal provides high-precision static and dynamic measurement performance; the digital-to-analog conversion unit of the track slab void detection intelligent terminal is a 24-bit Sigma-Delta ADC that supports anti-aliasing filtering, suppresses modulation and signal out-of-band noise, and provides available signal bandwidth at the Nyquist rate, making it very suitable for high dynamic range signal measurement in machine condition monitoring applications; the communication unit of the track slab void detection intelligent terminal communicates via Gigabit Ethernet to quickly transmit data to the central site, where the communication unit uses dual Ethernet ports to support daisy chain connection, thereby reducing the cost of network equipment and extending the communication distance.

[0042] like Figure 6 As shown, in this embodiment, the signal monitoring unit is a sound and vibration collection unit. In this embodiment, the sound and vibration collection unit is a microphone, and the collected signals are analog signals of sound and vibration. Considering that the outdoor working environment is relatively harsh, this embodiment adopts a high-protection microphone, which has the functions of waterproof and dustproof, IP67 protection level, and is suitable for various harsh outdoor environments.

[0043] A protective shell is set outside the analog-to-digital conversion unit for converting the analog signal of sound into a digital signal and the core control unit for receiving the digital signal of sound and performing signal analysis to obtain the hollow characteristic parameters; the hollow characteristic parameters are connected to the overall system to obtain the cavity situation.

[0044] Features of the ballastless track void and hidden defect detection device based on acoustic vibration: 1. Miniaturization: The flat design structure is compact and portable, suitable for fixed installation in various structures. A modular and lightweight architecture platform is implemented, integrating design images and acoustic vibration detection modules and system hardware and software to manufacture an engineering prototype of the ballastless track damage detection device, achieving accurate detection of hidden ballastless track defects and forming a core equipment. The ballastless track precision inspection speed is no less than 3 km / h. 2. Integration: Integrates collection, analysis, and interaction functions, and outputs the judgment results through the network port; 3. Low power consumption: conventional 12V~24V voltage power supply, the overall system operating power consumption is less than 10W; 4. Intelligence: Common track slab cavities can be automatically collected and identified, supporting secondary development.

[0045] The functions of the ballastless track gap detection system and device based on acoustic vibration include: (1) Hardware design and acquisition of acoustic vibration signals Design the layout, quantity and type of acoustic wave acquisition sensors based on detection performance and efficiency, determine the signal acquisition, storage and processing schemes, and develop special software to achieve automatic acquisition and preprocessing of high-resolution acoustic wave signals.

[0046] (2) Extraction of hollow disease characteristics The spectrum characteristics of the acoustic wave signal of the void defect are analyzed, and the noise removal and feature extraction algorithms of the acoustic wave signal of the void defect of the track bed slab are studied based on wavelet analysis, and a feature extraction method of the void defect is proposed.

[0047] (3) Intelligent identification of hollowing diseases Intelligent identification research, based on the characteristics of the acoustic wave signal of the ballastless track slab void defect, determines the intelligent recognition algorithm of the acoustic wave signal of the void defect based on the machine learning algorithm, and the void area under the slab is ≥0.3m 2 The accuracy rate of hidden defect recognition is ≥90%, and a ballastless track slab void detection technology based on acoustic-vibration characteristics is proposed.

[0048] (4) The device is implemented as a ballastless track status detection vehicle A modular, lightweight architecture platform will be established, along with integrated design of image and acoustic vibration detection modules and system hardware and software. A prototype of a ballastless track damage detection device will be manufactured to accurately detect hidden ballastless track defects, forming a core piece of equipment. The ballastless track precision inspection speed will be no less than 3 km / h.

[0049] Engineering application example: sound feature extraction and simulation calculation The noise removal and feature extraction algorithms of the acoustic wave signal of the track bed slab debonding are studied by using analysis and other technologies, and a feature extraction method for debonding defects is proposed.

[0050] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0051] like Figure 8 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, the memory 302 stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the method as in embodiment 1.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for acquiring slope rock mass information by fusion of multi-source remote sensing data, which is used for acquiring slope rock mass information with complex terrain, huge rock mass, dangerous and hidden features, characterized by: include: S1, multi-source data acquisition, including: collecting the multi-source data based on airborne laser radar technology, drone oblique photography technology, and ground-based three-dimensional laser scanning technology; S2, performing multi-source point cloud fusion on the collected multi-source data; S3, obtaining slope rock mass information, including: combining the fused multi-source point cloud data with the data advantages of each remote sensing point cloud, thereby finely extracting the rock mass surface state, geometric parameter information, trailing edge collapse and / or crack information, rock mass surface crack information, and structural surface occurrence information of the slope rock mass; S4, verifying the extraction accuracy, including: using the fused multi-source point cloud to perform geometric scale, trailing edge information and structural surface attitude information of the slope rock mass, as well as extraction and accuracy verification of the structural surface portion.

2. The method for acquiring slope rock mass information by fusion of multi-source remote sensing data according to claim 1, characterized in that: Said S1 comprises: S11, obtaining high-resolution and high-precision two-dimensional topographic image data based on the airborne laser radar technology; S12, "penetrating" ground vegetation based on the airborne laser radar multiple echo technology, and effectively removing the influence of surface vegetation based on a filtering algorithm to obtain true ground elevation data information; S13, measuring and acquiring three-dimensional ground object information data using a drone tilt photography technique, wherein the three-dimensional ground object information data is represented as a real color point cloud; S14, based on ground-based three-dimensional laser scanning technology, obtains the spatial geometric features of the slope surface, which are represented by three-dimensional point coordinate data; thereby achieving long-distance, non-contact measurement, and can collect high-precision and high-density three-dimensional point coordinate data without any processing of the target object.

3. The method for acquiring slope rock mass information by fusion of multi-source remote sensing data according to claim 2, characterized in that: The S2 includes: S21, performing point cloud coarse registration and point cloud fine registration on the collected multi-source data in sequence; S22: Based on the point cloud coarse registration and the point cloud fine registration, multi-source point cloud data is fused and output.

4. The method for acquiring slope rock mass information by fusion of multi-source remote sensing data according to claim 3, characterized in that: The point cloud coarse registration includes: performing point cloud coarse registration using a four-point consistency method, selecting at least four points of the same name in a common area of ​​point clouds from different data sources, and transforming the search for the four points of the same name into a search for the intersection of their intersecting lines.

5. The method for acquiring slope rock mass information by fusion of multi-source remote sensing data according to claim 4, characterized in that: The point cloud fine registration includes: on the premise that the point cloud coarse registration obtains good initial registration conditions, using the iterative closest point method ICP algorithm to perform dense fine matching, and at the same time performing point cloud denoising and filtering processing on the obtained initial point cloud data.

6. The method for acquiring slope rock mass information by fusion of multi-source remote sensing data according to claim 5, characterized in that: The multi-source point cloud data fusion includes: after completing the ICP algorithm fine alignment of the multi-source remote sensing data, using the drone oblique photography point cloud as a benchmark, repairing the holes in the hollow areas and the missing rock structure surfaces generated by the drone oblique photography scanning through the ground laser scanning point cloud and the airborne laser radar point cloud, obtaining the real terrain information of the area, and merging and exporting the repaired point clouds to make them into a complete point cloud data, and then the fusion is completed.

7. The method for acquiring slope rock mass information by fusion of multi-source remote sensing data according to claim 6, characterized in that: The S3 includes: S31, extracting information on the rock mass volume morphology based on the real color point cloud measured using the UAV tilt photography technology; S32, completely filtering out vegetation covering the rock surface based on the airborne laser radar multiple echo technology, and obtaining the surface state of the rock mass under the vegetation cover and the geometric parameter information of the slope rock mass; S33, completely filtering out vegetation covering the rear edge of the high and steep slope using the airborne lidar multiple echo technology, extracting information about the rear edge of the slope rock mass based on the fused point cloud data using a three-dimensional view and a pulled profile, and extracting information about collapse and / or cracks existing on the rear edge of the high and steep slope covered by vegetation; S34, based on the high-resolution and high-density point cloud of the ground laser radar, extracts the rock surface crack information and structural surface information; and directly calculates the normal vector of the point cloud, and automatically obtains the structural surface information of the point cloud through the corresponding relationship between the inclination, dip angle and normal vector, and automatically extracts the structural surface information of the slope rock.

8. A slope rock mass information acquisition system based on multi-source remote sensing data fusion, used to implement the method according to any one of claims 1 to 7, characterized in that: include: A data acquisition module (101) is used for multi-source data acquisition, including: acquiring the multi-source data based on airborne laser radar technology, unmanned aerial vehicle tilt photography technology, and ground three-dimensional laser scanning technology; A multi-source point cloud fusion module (102), configured to perform multi-source point cloud fusion on the collected multi-source data; A slope rock mass information acquisition module (103) is used to combine the fused multi-source point cloud data with the data advantages of each remote sensing point cloud, thereby finely extracting the rock mass surface state, geometric parameter information, rear edge collapse and / or crack information, rock mass surface crack information and structural surface occurrence information of the slope rock mass; The extraction accuracy verification module (104) is used to use the fused multi-source point cloud to extract and verify the geometric scale, trailing edge information and structural surface attitude information of the slope rock mass, as well as the structural surface part.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method according to any one of claims 1 to 6.

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