Method and system for identifying and evaluating stability of high and steep slope dangerous rock mass

Abstract

The present application provides a high and steep slope dangerous rock mass identification and stability evaluation method and system. According to the high and steep slope drop point cloud data, a dangerous rock mass identification model is established based on the point cloud density mutation detection algorithm and genetic neural network to identify the three-dimensional boundary of the dangerous rock mass. According to the three-dimensional boundary of the dangerous rock mass, an arbitrary dangerous rock mass contour point cloud set is formed, a dangerous rock mass geometric feature parameter extraction model is established, and the dangerous rock mass geometric feature parameters are extracted. A dangerous rock mass stability evaluation model including a rock mass parameter set, an environment data set, and a stability algorithm method set is established. The dangerous rock mass geometric feature parameters, the rock mass parameter set, and the environment data set are coupled as input conditions and input into the stability algorithm method set to evaluate the stability of the dangerous rock mass. The present application greatly improves the data precision, identification efficiency, analysis accuracy, and response real-time performance, and provides a theoretical basis for early identification and stability evaluation of high and steep environment slope dangerous rock mass in water conservancy and hydropower engineering.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy and hydropower engineering technology, and relates to a method and system for identifying and assessing the stability of unstable rock masses. Background Technology

[0002] Early identification and stability assessment of unstable rock masses on steep slopes in water conservancy and hydropower projects are crucial for preventing high-altitude landslides. However, current identification and stability assessment methods suffer from several problems due to the influence of topography and landforms: First, the complex terrain of steep slopes makes it difficult to obtain high-precision three-dimensional models using traditional manual surveying or ordinary aerial surveying, and point cloud models are prone to data distortion under kilometer-level elevation drops. Second, relying on manual experience to determine the boundaries of unstable rock masses is time-consuming and prone to omissions, failing to meet the need for rapid screening of unstable rock masses on large-scale slopes. Third, geometric parameters such as the surface area, dip angle, and volume of unstable rock masses are often estimated using simplified two-dimensional models, failing to reflect three-dimensional spatial characteristics and leading to large errors in stability analysis. Fourth, stability assessment relies on offline calculations, making it impossible to dynamically respond to changes in rock mass mechanical parameters or environmental factors such as rainfall and vibration. In summary, current methods for identifying and assessing the stability of unstable rock masses on steep slopes suffer from several drawbacks: low precision of point cloud model data under a drop of over 1,000 meters; low efficiency and accuracy in identifying unstable rock masses; coarse calculation of the geometric parameters of unstable rock masses; and poor real-time performance in stability assessment. Summary of the Invention

[0003] To address the problems described in the background art, such as low accuracy of point cloud model data, low efficiency in identifying dangerous rock masses, low accuracy in identifying dangerous rock masses, coarse calculation of geometric parameters of dangerous rock masses, and poor real-time performance of stability assessment under a drop of 1,000 meters on steep slopes, this invention provides a method and system for identifying and assessing the stability of dangerous rock masses on steep slopes.

[0004] The method of the present invention includes:

[0005] Based on the slope gradient, lighting conditions, topography, combination of ground features and distribution of obstacles of steep slopes, the flight path of the UAV is planned, and the UAV collects point cloud data of the drop of steep slopes according to the planned flight path.

[0006] Based on the point cloud data of the drop of steep slopes, a dangerous rock mass identification model is established based on the point cloud density mutation detection algorithm and genetic neural network to identify the three-dimensional boundary of the dangerous rock mass;

[0007] Based on the three-dimensional boundary of the unstable rock mass, an arbitrary set of unstable rock mass contour points is formed, and a model for extracting geometric feature parameters of the unstable rock mass is established to extract the geometric feature parameters of the unstable rock mass.

[0008] A stability assessment model for unstable rock masses is established, which includes a rock mass parameter set, an environmental dataset, and a stability algorithm set. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions, which are then input into the stability algorithm set to assess the stability of the unstable rock mass.

[0009] Furthermore, in the flight path planning of the UAV, the target point cloud density is used as a basis. r target With real-time point cloud density r target Differences, target point cloud density r target Real-time point cloud density r crurrent slope angle i Adjust the drone's flight altitude to ground level in real time. H， As shown in equation (1):

[0010] (1),

[0011] In the formula, H This refers to the real-time ground contact height. H 0 Used as the reference altitude; i The slope angle; r target Target point cloud density; r crurrent This represents the real-time point cloud density.

[0012] Furthermore, in the dangerous rock mass identification model, based on the point cloud data of the high and steep slope drop, the local point cloud density difference coefficient K is obtained through the point cloud density mutation detection algorithm, as shown in Equation (2). The closer the point cloud density difference coefficient K of adjacent areas is, the greater the possibility that the point cloud is located on the same structural surface. Conversely, it is located on different structural surfaces. Then, based on the spatial topological relationship of different structural surfaces, the potential boundary contour of the dangerous rock mass is initially identified.

[0013] Based on the point cloud data of the drop of high and steep slopes, a genetic neural network algorithm is established to continuously optimize the weight of the neural network and establish the point cloud density difference coefficient K and the outline size of the dangerous rock mass. The preliminary identified potential boundary outline of the dangerous rock mass is used as the input condition, and the final three-dimensional boundary of the dangerous rock mass is identified through iterative calculation by the genetic neural network.

[0014] (2),

[0015] In the formula, s local The local density standard deviation; s global The global density standard deviation; rmax , r min , r avg These represent the maximum, minimum, and average densities, respectively.

[0016] Furthermore, in the geometric feature parameter extraction model of the dangerous rock mass, based on the identified three-dimensional boundary of the dangerous rock mass, an arbitrary dangerous rock mass contour point cloud is formed, and the point cloud is formed into several triangular facets; through the established intelligent extraction model of geometric feature parameters of the dangerous rock mass, the dip angle, area and total volume of the rear edge structural surface of the dangerous rock mass are automatically extracted based on the triangular facets, as shown in Equation (3):

[0017] (3),

[0018] In the formula, α The dip angle of the structural surface at the rear edge of the unstable rock mass; α i The first structural plane at the rear edge of the unstable rock mass i The angle between the normal vector of each triangular facet and the vertical direction; A The area of ​​the structural surface at the rear edge of the unstable rock mass; S i The rear edge structural surface of the unstable rock mass i Area of ​​each triangular facet; V The volume of the unstable rock mass; A (Z) Let z be the horizontal projected area of ​​the unstable rock mass at point z. Z min and Z max These are the minimum and maximum elevations of the unstable rock mass, respectively.

[0019] Furthermore, the unstable rock mass stability assessment model also includes a set of reinforcement and stabilization measures. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions and input into the stability algorithm method set. First, the stability safety factor of the unstable rock mass in the unreinforced state is evaluated. If it does not meet the engineering requirements, the set of reinforcement and stabilization measures is added. Through continuous iteration until the stability safety factor of the unstable rock mass meets the engineering requirements, the corresponding stabilization and reinforcement scheme is obtained.

[0020] Furthermore, in the stability assessment model of the dangerous rock mass, the rock mass parameter set includes the rock mass elastic modulus, internal friction angle, and cohesion parameters; the environmental dataset includes rainfall, groundwater level, and earthquake conditions; the stability algorithm method set includes the rigid body limit equilibrium algorithm; and the reinforcement and stabilization measures set includes anchor cables, anchor piles, and drainage hole reinforcement measures. The expression of the stability assessment model of the dangerous rock mass is shown in equation (4):

[0021] (4),

[0022] In the formula,M data For rock mass parameter sets and environmental datasets, and M method For a set of stability algorithm methods, M reinforcement To strengthen the set of stability measures;

[0023] Based on the geometric feature parameter extraction model of the unstable rock mass, the dip angle, area and total volume of the rear structural surface of the unstable rock mass are obtained. The rock mass parameter set and the environmental dataset are dynamically coupled and input into the stability algorithm method set. First, the stability safety factor of the unstable rock mass in the unreinforced state is evaluated. If it does not meet the engineering requirements, the set of reinforcement and stabilization measures is added. Through continuous iteration until the stability safety factor of the unstable rock mass meets the engineering requirements, the corresponding stabilization and reinforcement scheme is obtained.

[0024] Based on the above method, this invention proposes a system for identifying and assessing the stability of dangerous rock masses on steep slopes, including a steep slope drop point cloud data acquisition module, a dangerous rock mass identification module, a dangerous rock mass geometric feature parameter extraction module, and a dangerous rock mass stability assessment module.

[0025] The steep slope drop point cloud data acquisition module is used to plan the flight path of the UAV based on the slope gradient, lighting conditions, topography, landform combination and obstacle distribution of the steep slope, and to collect the steep slope drop point cloud data by the UAV according to the planned flight path.

[0026] The dangerous rock mass identification module is used to establish a dangerous rock mass identification model based on the point cloud data of the steep slope drop, the point cloud density mutation detection algorithm and the genetic neural network, and to identify the three-dimensional boundary of the dangerous rock mass.

[0027] The unstable rock mass geometric feature parameter extraction module is used to form an arbitrary unstable rock mass contour point cloud based on the three-dimensional boundary of the unstable rock mass, establish an unstable rock mass geometric feature parameter extraction model, and extract the unstable rock mass geometric feature parameters.

[0028] The unstable rock mass stability assessment module is used to establish an unstable rock mass stability assessment model that includes a rock mass parameter set, an environmental dataset, and a stability algorithm method set. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions and input into the stability algorithm method set to assess the stability of the unstable rock mass.

[0029] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for identifying and assessing the stability of dangerous rock masses on steep slopes as described above.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] (1) High data acquisition accuracy: By combining UAV with adaptive flight path planning, the data distortion problem of point cloud model of high and steep slope under a drop of 1,000 meters was solved, which significantly improved the accuracy and integrity of the three-dimensional model of high and steep slope, and provided a reliable data foundation for subsequent analysis;

[0032] (2) Intelligent and efficient identification of dangerous rock mass: The dangerous rock mass identification model coupled with point cloud density mutation detection algorithm and genetic neural network realizes intelligent, automated and rapid screening of dangerous rock mass boundary, overcomes the defects of low efficiency and high missed detection rate of traditional manual experience judgment, and is extremely suitable for the engineering needs of high and steep slopes and large-scale slopes.

[0033] (3) Accurate extraction of geometric feature parameters: Based on the three-dimensional boundary of the dangerous rock mass, an arbitrary dangerous rock mass contour point aggregator is formed, and a model for extracting geometric feature parameters of the dangerous rock mass is established. This breaks through the limitations of traditional two-dimensional simplified estimation, and can accurately calculate the geometric feature parameters of the dangerous rock mass, significantly reducing the roughness and error of geometric analysis.

[0034] (4) Dynamic real-time stability assessment: By integrating rock mass parameters, environmental data and stability algorithms into a stable rock mass stability assessment model, online dynamic real-time assessment of the unstable rock mass can be realized, which solves the problem of lag in traditional offline calculation and the problem of poor real-time stability assessment, thereby enabling the adoption of corresponding reinforcement schemes and improving the timeliness of disaster early warning.

[0035] In summary, this invention realizes an intelligent closed-loop design for the entire process from data acquisition, unstable rock mass identification, geometric feature parameter extraction to stability assessment. It breaks through the limitations of traditional segmented processing methods and achieves integrated "acquisition-analysis-decision" for unstable rock mass prevention and control. This greatly improves data accuracy, identification efficiency, analysis accuracy, and real-time response, providing a theoretical basis for the early identification and stability assessment of unstable rock masses on steep slopes in water conservancy and hydropower projects, and providing reliable technical support for the prevention and control of high-altitude landslide disasters. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention.

[0037] Figure 2 This is a system architecture diagram of the present invention. Detailed Implementation

[0038] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0039] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0040] Example 1

[0041] The flowchart for the identification and stability assessment of unstable rock masses on steep slopes is as follows: Figure 1 As shown, the specific steps are as follows.

[0042] Based on the slope gradient, lighting conditions, topography, combination of ground features and distribution of obstacles of steep slopes, the flight path of UAVs is planned, and the UAVs collect point cloud data of the elevation difference of steep slopes according to the planned flight path.

[0043] Specifically, in the flight path planning of drones, based on the target point cloud density... r target With real-time point cloud density r target Differences, target point cloud density r target Real-time point cloud density r crurrent slope angle i Adjust the drone's flight altitude to ground level in real time. H This solves the problem of point cloud model distortion caused by terrain undulation in traditional aerial surveying, and achieves point cloud model accuracy optimization under a slope drop of a kilometer or more, as shown in the following formula (1):

[0044] (1),

[0045] In the formula, H The real-time ground contact height is in meters (m). H 0 Reference altitude, in meters (m); i The slope angle is expressed in °. r target For the target point cloud density, points / m 3 ; r crurrent For real-time point cloud density, points / m 3 .

[0046] Based on the point cloud data of the drop of steep slopes, a dangerous rock mass identification model is established using a point cloud density mutation detection algorithm and a genetic neural network to identify the three-dimensional boundary of the dangerous rock mass.

[0047] Specifically, based on the point cloud data of the high and steep slope drop, the local point cloud density difference coefficient K is obtained by the point cloud density mutation detection algorithm, as shown in Equation (2). The closer the point cloud density difference coefficient K of adjacent areas is, the greater the possibility that the point cloud is located on the same structural surface. Conversely, it is located on different structural surfaces. Then, based on the spatial topological relationship of different structural surfaces, the potential boundary contour of the dangerous rock mass is initially identified.

[0048] Based on the point cloud data of the drop of high and steep slopes, a genetic neural network algorithm is established to continuously optimize the weight of the neural network and establish the point cloud density difference coefficient K and the outline size of the dangerous rock mass. The preliminary identified potential boundary outline of the dangerous rock mass is used as the input condition, and the final three-dimensional boundary of the dangerous rock mass is identified through iterative calculation by the genetic neural network.

[0049] (2),

[0050] In the formula, s local The local density standard deviation; s global The global density standard deviation; r max , r min , r avg These represent the maximum, minimum, and average densities, in units per m³. 3 .

[0051] Based on the three-dimensional boundary of the unstable rock mass, an arbitrary set of unstable rock mass contour points is formed, and a model for extracting the geometric feature parameters of the unstable rock mass is established to extract the geometric feature parameters of the unstable rock mass.

[0052] Specifically, based on the identified three-dimensional boundary of the unstable rock mass, an arbitrary unstable rock mass contour point cloud is formed, and the point cloud is formed into several triangular facets; through the established intelligent extraction model of the geometric feature parameters of the unstable rock mass, the dip angle, area and total volume of the rear edge structural surface of the unstable rock mass are automatically extracted based on the triangular facets, as shown in Equation (3):

[0053] (3),

[0054] In the formula, α The dip angle of the structural surface at the rear edge of the unstable rock mass is given in °. α i The first structural plane at the rear edge of the unstable rock mass i The angle between the normal vector of each triangular facet and the vertical direction is °; A The area of ​​the structural surface at the rear edge of the unstable rock mass is m. 2 ; S i The rear edge structural surface of the unstable rock mass i Area of ​​each triangular facet, m2 ; V The volume of the unstable rock mass is in meters. 3 ; A (Z) Let z be the horizontal projected area of ​​the unstable rock mass at point z, in meters. 2 ; Z min and Z max These are the minimum and maximum elevations of the unstable rock mass, respectively.

[0055] A stability assessment model for unstable rock masses is established, which includes a rock mass parameter set, an environmental dataset, and a stability algorithm set. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions, which are then input into the stability algorithm set to assess the stability of the unstable rock mass.

[0056] Specifically, the unstable rock mass stability assessment model also includes a set of reinforcement and stabilization measures. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions and input into the stability algorithm method set. First, the stability safety factor of the unstable rock mass in the unreinforced state is evaluated. If it does not meet the engineering requirements, the set of reinforcement and stabilization measures is added. Through continuous iteration, the stability safety factor of the unstable rock mass meets the engineering requirements, and the corresponding stabilization and reinforcement scheme is obtained.

[0057] More specifically, in the rock mass stability assessment model, the rock mass parameter set includes the rock mass elastic modulus, internal friction angle, and cohesion parameters, which can be determined by geological engineers. The environmental dataset includes rainfall, groundwater level, and earthquake conditions. The above environmental data are updated dynamically on a regular basis according to the actual situation. The stability algorithm method set includes the rigid body limit equilibrium algorithm. The reinforcement and stabilization measures set includes anchor cables, anchor piles, and drainage hole reinforcement measures. The expression of the unstable rock mass stability assessment model is shown in equation (4):

[0058] (4),

[0059] In the formula, M data For rock mass parameter sets and environmental datasets, and M method For a set of stability algorithm methods, M reinforcement To strengthen the set of stability measures;

[0060] Based on the geometric feature parameter extraction model of the unstable rock mass, the dip angle, area and total volume of the rear structural surface of the unstable rock mass are obtained. The rock mass parameter set and the environmental dataset are dynamically coupled and input into the stability algorithm method set. First, the stability safety factor of the unstable rock mass in the unreinforced state is evaluated. If it does not meet the engineering requirements, the set of reinforcement and stabilization measures is added. Through continuous iteration until the stability safety factor of the unstable rock mass meets the engineering requirements, the corresponding stabilization and reinforcement scheme is obtained.

[0061] Example 2

[0062] The system architecture diagram for identifying and assessing the stability of unstable rock masses on steep slopes is shown below. Figure 2 As shown, it consists of a high and steep slope drop point cloud data acquisition module, a dangerous rock mass identification module, a dangerous rock mass geometric feature parameter extraction module, and a dangerous rock mass stability assessment module.

[0063] The high and steep slope drop point cloud data acquisition module is used to plan the flight path of the UAV based on the slope gradient, lighting conditions, topography, landform combination and obstacle distribution of the high and steep slope, and to collect the high and steep slope drop point cloud data by the UAV according to the planned flight path.

[0064] The unstable rock mass identification module is used to establish an unstable rock mass identification model based on the point cloud data of the drop of steep slopes, and on the point cloud density mutation detection algorithm and genetic neural network, so as to identify the three-dimensional boundary of the unstable rock mass.

[0065] The module for extracting geometric feature parameters of unstable rock masses is used to form a cloud of arbitrary unstable rock mass contour points based on the three-dimensional boundary of the unstable rock mass, establish a model for extracting geometric feature parameters of the unstable rock mass, and extract the geometric feature parameters of the unstable rock mass.

[0066] The unstable rock mass stability assessment module is used to establish an unstable rock mass stability assessment model that includes a rock mass parameter set, an environmental dataset, and a stability algorithm method set. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions, which are then input into the stability algorithm method set to assess the stability of the unstable rock mass.

[0067] The specific implementation methods of each module in this system are the same as those described in Example 1, and will not be repeated here.

[0068] Example 3

[0069] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for identifying and assessing the stability of dangerous rock masses on steep slopes as described in Embodiment 1 above, and the system for identifying and assessing the stability of dangerous rock masses on steep slopes as described in Embodiment 2.

[0070] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as object-oriented programming languages ​​like Java, C++, Python, and interpreted scripting languages ​​like JavaScript.

[0071] This application is described with reference to flowchart illustrations and / or block diagrams of methods, electronic devices (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing electronic device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing electronic device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure one One or more processes and / or boxes Figure one A device that provides the functions specified in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing electronic device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure one One or more processes and / or boxes Figure one The function specified in one or more boxes.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing electronic device to cause a series of operational steps to be performed on the computer or other programmable electronic device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable electronic device for implementing the process. Figure one One or more processes and / or boxes Figure one The steps of the function specified in one or more boxes.

[0074] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0075] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for identifying and assessing the stability of unstable rock masses on steep slopes, characterized in that, include: Based on the slope gradient, lighting conditions, topography, combination of ground features and distribution of obstacles of steep slopes, the flight path of the UAV is planned, and the UAV collects point cloud data of the drop of steep slopes according to the planned flight path. Based on the point cloud data of the drop of steep slopes, a dangerous rock mass identification model is established based on the point cloud density mutation detection algorithm and genetic neural network to identify the three-dimensional boundary of the dangerous rock mass; In the dangerous rock mass identification model, based on the point cloud data of the high and steep slope drop, the local point cloud density difference coefficient K is obtained by the point cloud density mutation detection algorithm, as shown in Equation (2). The closer the point cloud density difference coefficient K of adjacent areas is, the greater the possibility that the point cloud is located on the same structural surface. Conversely, it is located on different structural surfaces. Then, based on the spatial topological relationship of different structural surfaces, the potential boundary contour of the dangerous rock mass is initially identified. Based on the point cloud data of the drop of high and steep slopes, a genetic neural network algorithm is established to continuously optimize the weight of the neural network and establish the point cloud density difference coefficient K and the outline size of the dangerous rock mass. The preliminary identified potential boundary outline of the dangerous rock mass is used as the input condition, and the final three-dimensional boundary of the dangerous rock mass is identified through iterative calculation by the genetic neural network. （2）， In the formula, σ local The local density standard deviation; σ global The global density standard deviation; ρ max , ρ min , ρ avg These represent the maximum, minimum, and average densities, respectively. Based on the three-dimensional boundary of the unstable rock mass, an arbitrary set of unstable rock mass contour points is formed, and a model for extracting geometric feature parameters of the unstable rock mass is established to extract the geometric feature parameters of the unstable rock mass. A stability assessment model for unstable rock masses is established, which includes a rock mass parameter set, an environmental dataset, and a stability algorithm set. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions, which are then input into the stability algorithm set to assess the stability of the unstable rock mass.

2. The method for identifying and assessing the stability of unstable rock masses on steep slopes according to claim 1, characterized in that: In the flight path planning of the UAV, the target point cloud density is used as a basis. ρ target With real-time point cloud density ρ target Differences, target point cloud density ρ target Real-time point cloud density ρ crurrent slope angle θ Adjust the drone's flight altitude to ground level in real time. H， As shown in equation (1): （1）， In the formula, H This refers to the real-time ground contact height. H 0 Used as the reference altitude; θ The slope angle; ρ target Target point cloud density; ρ crurrent This represents the real-time point cloud density.

3. The method for identifying and assessing the stability of unstable rock masses on steep slopes according to claim 2, characterized in that: In the aforementioned model for extracting geometric feature parameters of dangerous rock mass, an arbitrary dangerous rock mass contour point cloud is formed based on the identified three-dimensional boundary of the dangerous rock mass, and the point cloud is formed into several triangular facets; through the established intelligent extraction model for geometric feature parameters of dangerous rock mass, the dip angle, area and total volume of the rear edge structural surface of the dangerous rock mass are automatically extracted based on the triangular facets, as shown in Equation (3): （3）， In the formula, α The dip angle of the structural surface at the rear edge of the unstable rock mass; α i The first structural plane at the rear edge of the unstable rock mass i The angle between the normal vector of each triangular facet and the vertical direction; A The area of ​​the structural surface at the rear edge of the unstable rock mass; S i The rear edge structural surface of the unstable rock mass i Area of ​​each triangular facet; V The volume of the unstable rock mass; A(Z) Let z be the horizontal projected area of ​​the unstable rock mass at point z. Z min and Z max These are the minimum and maximum elevations of the unstable rock mass, respectively.

4. The method for identifying and assessing the stability of unstable rock masses on steep slopes according to claim 3, characterized in that: The unstable rock mass stability assessment model also includes a set of reinforcement and stabilization measures. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions and input into the stability algorithm method set. First, the stability safety factor of the unstable rock mass in the unreinforced state is evaluated. If it does not meet the engineering requirements, the set of reinforcement and stabilization measures is added. The model is iterated until the stability safety factor of the unstable rock mass meets the engineering requirements, and the corresponding stabilization and reinforcement scheme is obtained.

5. The method for identifying and assessing the stability of unstable rock masses on steep slopes according to claim 4, characterized in that: In the stability assessment model of the dangerous rock mass, the rock mass parameter set includes the rock mass elastic modulus, internal friction angle, and cohesion parameters; the environmental dataset includes rainfall, groundwater level, and earthquake conditions; the stability algorithm method set includes the rigid body limit equilibrium algorithm; and the reinforcement and stabilization measures set includes anchor cables, anchor piles, and drainage hole reinforcement measures. The expression of the stability assessment model of the dangerous rock mass is shown in equation (4): （4）， In the formula, M data For rock mass parameter sets and environmental datasets, and M method For a set of stability algorithm methods, M reinforcement To strengthen the set of stability measures; Based on the geometric feature parameter extraction model of the unstable rock mass, the dip angle, area and total volume of the rear structural surface of the unstable rock mass are obtained. The rock mass parameter set and the environmental dataset are dynamically coupled and input into the stability algorithm method set. First, the stability safety factor of the unstable rock mass in the unreinforced state is evaluated. If it does not meet the engineering requirements, the set of reinforcement and stabilization measures is added. Through continuous iteration until the stability safety factor of the unstable rock mass meets the engineering requirements, the corresponding stabilization and reinforcement scheme is obtained.

6. A system for identifying and assessing the stability of unstable rock masses on steep slopes, implementing the method described in any one of claims 1-5, characterized in that: It includes a high and steep slope drop point cloud data acquisition module, a dangerous rock mass identification module, a dangerous rock mass geometric feature parameter extraction module, and a dangerous rock mass stability assessment module; The steep slope drop point cloud data acquisition module is used to plan the flight path of the UAV based on the slope, lighting conditions, topography, combination of ground features and distribution of obstacles of the steep slope, and to collect the steep slope drop point cloud data by the UAV according to the planned flight path. The dangerous rock mass identification module is used to establish a dangerous rock mass identification model based on the point cloud data of the drop of high and steep slopes, and based on the point cloud density mutation detection algorithm and genetic neural network, to identify the three-dimensional boundary of the dangerous rock mass; In the dangerous rock mass identification model, based on the point cloud data of the high and steep slope drop, the local point cloud density difference coefficient K is obtained by the point cloud density mutation detection algorithm, as shown in Equation (2). The closer the point cloud density difference coefficient K of adjacent areas is, the greater the possibility that the point cloud is located on the same structural surface. Conversely, it is located on different structural surfaces. Then, based on the spatial topological relationship of different structural surfaces, the potential boundary contour of the dangerous rock mass is initially identified. Based on the point cloud data of the drop of high and steep slopes, a genetic neural network algorithm is established to continuously optimize the weight of the neural network and establish the point cloud density difference coefficient K and the outline size of the dangerous rock mass. The preliminary identified potential boundary outline of the dangerous rock mass is used as the input condition, and the final three-dimensional boundary of the dangerous rock mass is identified through iterative calculation by the genetic neural network. （2）， In the formula, σ local The local density standard deviation; σ global The global density standard deviation; ρ max , ρ min , ρ avg These represent the maximum, minimum, and average densities, respectively. The unstable rock mass geometric feature parameter extraction module is used to form an arbitrary unstable rock mass contour point cloud based on the three-dimensional boundary of the unstable rock mass, establish a unstable rock mass geometric feature parameter extraction model, and extract the unstable rock mass geometric feature parameters. The unstable rock mass stability assessment module is used to establish an unstable rock mass stability assessment model that includes a rock mass parameter set, an environmental dataset, and a stability algorithm method set. The geometric characteristic parameters of the unstable rock mass are coupled with the rock mass parameter set and the environmental dataset as input conditions and input into the stability algorithm method set to assess the stability of the unstable rock mass.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the method for identifying and assessing the stability of dangerous rock masses on steep slopes as described in any one of claims 1-5.

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