Slope remote video monitoring stability analysis system and method

Abstract

The invention relates to a slope remote video monitoring stability analysis system and method, and the method comprises the following steps: S1, constructing a three-dimensional unsteady viscoelastic-plastic shear creep model, and obtaining slope stability data under the action of rainfall; s2, calculating the shear creep rate on the landslide mass, defining a main slide line and a slide part, and determining an anti-slide section; s3, determining a nonlinear mapping relation between the slope earth surface displacement or crack width and the shear creep rate at the corresponding anti-slip section; S4, setting a sensor and a target based on the main slip line; s5, acquiring the displacement or crack width of the target; s6, outputting to a remote computer; s7, the shear creep rate of the target corresponding to the anti-sliding section is obtained; and S8, judging the slope stability based on the shear creep rate of the target corresponding anti-sliding section. Compared with the prior art, the method has the advantages that the soil landslide stability is dynamically evaluated, and the instability prediction precision is improved.

Description

Technical Field

[0001] The invention relates to the field of soil landslide and slope engineering, and in particular to a slope remote video monitoring stability analysis system and method. Background Art

[0002] At present, some relevant research has been conducted on landslide disaster risk warning during rainfall:

[0003] (1) Based on rainfall data, a landslide risk warning model was established. A macro-hazard warning and forecasting model for regional landslide disasters was established based on the effective rainfall model. According to the risk zoning results of different scales and the three types of rainfall critical values, warning indicators and warning levels were set respectively, and a regional loess landslide risk warning calculation and analysis model was developed.

[0004] (2) Landslide risk warning is carried out based on meteorological information data such as rainfall. Based on the landslide disaster risk prediction map based on WEBGIS, the dynamic risk forecast of landslide disaster is carried out in combination with the real-time rainfall information of the region. The rainfall landslide probability of the region is calculated according to the spatial probability of landslide and the time probability of rainfall-induced landslide, and risk warning is carried out according to the risk zoning. Based on the matter-element theory in the extension theory and referring to the geological disaster meteorological risk warning classification, the landslide risk warning level table is determined.

[0005] (3) Conduct landslide risk warning based on measured displacement data. Use Markov chain theory to establish a landslide warning model based on dynamic prediction of future velocity state. Integrate in-situ monitoring data into the engineering risk rate analysis method, and propose a real-time risk rate quantification model and warning method for reservoir bank slope operation. Establish a landslide trend speed ratio warning criterion, and use the trend speed ratio and displacement speed ratio fusion method to judge the change of landslide risk.

[0006] (4) Use artificial intelligence, machine learning and deep learning algorithms to conduct landslide disaster risk warning. Establish a landslide disaster meteorological risk warning method based on BP neural network. Establish a fuzzy neural network risk identification model to identify the risk level of soft rock slopes based on meteorological conditions, topographic factors, rock and soil characteristics and measured horizontal displacement. Combine spatial convolutional neural network, fuzzy neural network and other technologies to achieve geological disaster warning.

[0007] At present, some relevant research has been conducted at home and abroad on the intelligent early warning and forecasting technology of slope hazards: real-time monitoring is carried out based on the Beidou high-precision displacement deformation monitoring system, mainly monitoring rainfall and soil moisture content, as well as the deformation of the landslide body; a GPS one-machine multi-antenna monitoring system for highway slope deformation has been established, achieving the goals of high-precision, automatic and all-weather monitoring of highway slope disasters; the slope disaster information processing system based on "3S" (remote sensing RS, geographic information system GIS, global positioning system GPS) realizes all-weather automatic monitoring; research has been conducted on the generation of slope disaster warning information; according to the characteristics of the slope, usage conditions and the economic impact of the damage, based on the evaluation of the expected deformation failure and critical reading frequency of the slope, a reliability diagram of the slope monitoring system was constructed, and the effectiveness and reliability of the monitoring system in warning critical slope damage were evaluated; using the rainfall index of the radial basis function, an early warning system for debris flow and slope damage was established; combining genetic algorithm and least squares method, an evolutionary polynomial regression method for predicting the stability of soil and rock slopes was proposed. At present, in terms of disaster warning technology, based on the principle of fuzzy hierarchical analysis, a prediction model for the instability of highway cutting slopes has been established, and corresponding slope instability prediction risk assessment software has been developed. The gray correlation principle is used to analyze the weights of factors affecting roadbed stability under rainfall conditions, and corresponding preventive measures are proposed. High-precision sensors are used to monitor key indicators such as surface displacement, rainfall, and soil moisture content of the slope in real time. Once the monitoring data exceeds the preset safety threshold, the system will automatically trigger the warning mechanism. These methods require a lot of expert experience, and there are shortcomings such as insufficient basis for the arrangement of slope monitoring points, unreasonable stability evaluation, immature instability prediction, low precision, and cumbersome implementation. Therefore, it is particularly urgent to develop a slope remote video monitoring stability analysis system and method with high precision, high accuracy, and high reliability.

[0008] The above method has defects:

[0009] The indicators used in the above methods rely on expert experience and cannot reliably and accurately evaluate the time of slope instability, that is, the stability of the slope. They cannot accurately define the potential main sliding line and sliding surface or sliding belt of the slope and the anti-slip section of the slope. As a result, the slope monitoring points do not detect key data for predicting potential landslides, and the instability prediction accuracy is not high. Summary of the invention

[0010] The purpose of the present invention is to provide a slope remote video monitoring stability analysis system and method in order to realize the dynamic evaluation of soil landslide stability and improve the accuracy of instability prediction. By calculating the shear creep rate change rate of the slope soil, the potential main sliding line and sliding surface or sliding belt of the slope and the slope anti-slip section are defined according to the shear creep rate change rate, and the key data used to predict potential landslides are detected, so that the obtained data can better reflect the changes in slope stability, and the shear creep rate change rate at the corresponding anti-slip section is calculated, which more accurately reflects the actual situation of the dynamic changes in soil slope stability, which is conducive to timely dynamic evaluation of slope stability under rainfall conditions.

[0011] The purpose of the present invention can be achieved by the following technical solutions:

[0012] A slope remote video monitoring stability analysis method, the method comprising the following steps:

[0013] S1: Construct a three-dimensional unsteady viscoelastic-plastic shear creep model to obtain slope stability data under rainfall.

[0014] S2: Calculate the shear creep rate on the landslide body based on the slope stability data under rainfall, define the potential main sliding line and sliding part based on the shear creep rate, and define the last penetrated section in the sliding section as the anti-sliding section;

[0015] S3: Determine the nonlinear mapping relationship between the slope surface displacement or crack width at the projection position of the anti-slip segment in the sliding part on the ground surface and the shear creep rate at the corresponding anti-slip segment:

[0016] S4: Set sensors and targets based on the main slide line;

[0017] S5: Obtain the displacement of the target or the crack width;

[0018] S6: outputting the displacement or crack width to a remote computer;

[0019] S7: The remote computer obtains the shear creep rate at the anti-slip section corresponding to the target based on the displacement or crack width of the target and the nonlinear mapping relationship;

[0020] S8: The slope stability is determined based on the shear creep rate at the anti-slip section corresponding to the target, and the slope stability analysis result is obtained.

[0021] Furthermore, the specific steps of S1 are:

[0022] S1-1: Construct a three-dimensional unsteady viscoelastic-plastic shear creep model;

[0023] S1-2: Obtain the data required for the model through triaxial creep tests on unsaturated / saturated soil;

[0024] S1-3: Based on the model and the parameters required by the model, a three-dimensional finite element numerical simulation of the slope stability under rainfall is performed to obtain the slope stability data under rainfall.

[0025] Furthermore, the data required for the model include permeability coefficient, suction, cohesion, internal friction angle, long-term strength, elastic modulus, viscosity coefficient, soil yield stress σ f , the ultimate strength of soil in the flowing state τ T and creep calculation parameters.

[0026] Furthermore, the specific steps of S2 are:

[0027] S2-1: The shear creep rate is calculated based on the stability data of the lower slope. The longitudinal line with the fastest change in shear creep rate on the landslide body is defined as the potential main sliding line, which represents the main sliding direction of the overall sliding of the slope;

[0028] S2-2: The through-surface or through-zone in the landslide body where the rate of change of shear creep rate is greater than 0 is defined as the sliding surface or sliding zone;

[0029] S2-3: The last penetrated section in the sliding part is defined as the slope anti-sliding section;

[0030] S2-4: Determine the anti-slip section in the sliding part and its projection position on the ground surface, the potential main sliding line and the scope of possible slope instability and damage.

[0031] Furthermore, the shear creep rate is:

[0032]

[0033] Among them, in the formula, is the shear creep rate, σ is the current stress, σ f is the yield stress of soil, η 2 is the viscosity coefficient of unsteady shear creep, A 1 , A 2 , m and n are creep calculation parameters, τ T is the ultimate strength of soil in the flowing state; t is the creep time history.

[0034] Furthermore, the specific steps of S4 are:

[0035] S4-1: Install non-contact intelligent perception sensors and machine vision deformation meters within a certain range of the slope;

[0036] S4-2: Install targets on the projected position of the ground surface in the slope anti-sliding section of the sliding part on the potential main sliding line and within a certain range near it.

[0037] Furthermore, the specific steps of S5 are:

[0038] S5-1: Capture the real-time preliminary frame image of slope deformation based on non-contact intelligent sensing sensor;

[0039] S5-2: Establish an optimization model of the Bayesian-convolutional neural network and input the preliminary frame image into the optimization model;

[0040] S5-3: Optimize the model to obtain the best feature point sequence and output a high-resolution image;

[0041] S5-4: Based on machine vision deformometer, the displacement or crack width of the target and video data are obtained from high-resolution images.

[0042] Furthermore, the specific steps of S6 are:

[0043] S6-1: A data collection station powered by solar energy is set at a certain position of the machine vision deformer, and the data collection station is connected to the machine vision deformer via an optical fiber cable;

[0044] S6-2: Transmit the displacement of the target or the crack width to a remote computer in the form of a mobile signal.

[0045] Furthermore, the specific steps of S8 are:

[0046] S8-1: If the change rate of the shear creep rate at the anti-slip section corresponding to the target is less than 0, the slope is considered to be stable;

[0047] S8-2: If the change rate of the shear creep rate at the anti-slip section corresponding to the target is equal to 0, it is considered that the slope will tend to be destroyed;

[0048] S8-3: If the rate of change of the shear creep rate at the anti-slip section corresponding to the target is greater than 0, it is considered that the slope is about to become unstable and fail.

[0049] Another aspect of the present invention provides a slope remote video monitoring stability analysis system, comprising a memory, a processor, and a program stored in the memory, wherein the processor implements the above method when executing the program.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) The present invention establishes a three-dimensional unsteady viscoelastic-plastic shear creep model, calculates the shear creep rate change rate of the slope soil, defines the potential main sliding line and sliding surface or sliding zone of the slope and the anti-slip section of the slope according to the shear creep rate change rate, and performs a dynamic evaluation of the stability of the soil landslide, so that the evaluation result of the present invention is more accurate and effective, providing strong technical support for predicting the occurrence of landslide disasters.

[0052] (2) The present invention defines the potential main sliding line and sliding surface or sliding belt of the slope and the anti-slip section of the slope according to the shear creep rate change rate. Based on this, a machine vision deformometer equipped with a non-contact intelligent sensing sensor and integrating machine vision and video monitoring technology is installed and targets are arranged, so that the obtained data can better reflect the changes in slope stability.

[0053] (3) The present invention calculates the rate of change of the shear creep rate at the corresponding anti-slip section based on the target displacement or crack width obtained by a machine vision deformer equipped with a non-contact intelligent sensing sensor and integrating machine vision and video monitoring technology, so as to more accurately reflect the actual situation of the dynamic change of soil slope stability, and facilitate timely dynamic evaluation of slope stability under rainfall conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the process of the present invention;

[0055] Figure 2 This is an internal calculation flow chart of the three-dimensional unsteady viscoelastic-plastic shear creep model of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0057] The present invention proposes a slope remote video monitoring stability analysis system and method. The flowchart of the present invention is as follows: Figure 1 As shown, the present invention includes: three-dimensional unsteady viscoelastic-plastic shear creep finite element numerical simulation; determining the anti-slip segment in the potential sliding surface and the projection position of the anti-slip segment on the ground surface, the potential main sliding line and the range of possible instability and damage of the slope; establishing a nonlinear mapping relationship between the surface displacement or crack width of the slope at the projection position of the anti-slip segment in the potential sliding surface on the ground surface and the shear creep rate at the corresponding anti-slip segment; installing a machine vision deformer and a target equipped with a non-contact intelligent sensing sensor and integrating machine vision and video monitoring technology; obtaining the displacement or crack width of the target; automatically transmitting the obtained displacement or crack width and video data to the computer of the remote client in real time through the virtual IP technology of the Internet of Things; calculating the shear creep rate at the anti-slip segment corresponding to the target; and dynamically evaluating the stability of the slope. Compared with the prior art, the present invention has the advantages of high precision, high accuracy, and high reliability.

[0058] The method of the present invention comprises the following steps:

[0059] S1: Construct a three-dimensional unsteady viscoelastic-plastic shear creep model to obtain slope stability data under rainfall.

[0060] S2: Calculate the shear creep rate on the landslide body based on the slope stability data under rainfall, define the potential main sliding line and sliding part based on the shear creep rate, and define the last penetrated section in the sliding section as the anti-sliding section;

[0061] S3: Determine the nonlinear mapping relationship between the slope surface displacement or crack width at the projection position of the anti-slip segment in the sliding part on the ground surface and the shear creep rate at the corresponding anti-slip segment:

[0062] S4: Set sensors and targets based on the main slide line;

[0063] S5: Obtain the displacement of the target or the crack width;

[0064] S6: outputting the displacement or crack width to a remote computer;

[0065] S7: The remote computer obtains the shear creep rate at the anti-slip section corresponding to the target based on the displacement or crack width of the target and the nonlinear mapping relationship;

[0066] S8: The slope stability is determined based on the shear creep rate at the anti-slip section corresponding to the target, and the slope stability analysis result is obtained.

[0067] In S1, by developing a dedicated program module for NGXJCRM (the program module internal calculation Figure 2 3D unsteady viscoelastic-plastic shear creep finite element numerical simulation embedded in ABAQUS software;

[0068] S1-1: Use the creep element model theory to develop a special module for the 3D unsteady viscoelastic-plastic shear creep model material subroutine NGXJCRM of ABAQUS software, and embed the special module NGXJCRM into ABAQUS software;

[0069] S1-2: Obtain the permeability coefficient, suction, adhesion, internal friction angle, long-term strength, and elastic modulus, viscosity coefficient, and soil yield stress σ required by the NGXJCRM special module through triaxial creep tests of unsaturated / saturated soil. f , the ultimate strength of soil in the flowing state τ T 、Creep calculation parameters (A 1 , A 2 , m and n);

[0070] S1-3: The ABAQUS finite element software embedded with the NGXJCRM special module developed in S1-1 is used to perform three-dimensional finite element numerical simulation of slope stability under rainfall.

[0071] In S2, the anti-slip section in the potential sliding surface and its projection position on the ground surface, the potential main sliding line and the range of possible slope instability and damage are determined through S1.

[0072] S2-1: The longitudinal line where the shear creep rate on the landslide body changes fastest is defined as the potential main sliding line, which represents the main sliding direction of the overall sliding of the slope:

[0073] Shear creep rate ε VP Calculation formula:

[0074]

[0075] In the formula, is the shear creep rate, σ is the current stress, σ f is the yield stress of soil, η 2 is the viscosity coefficient of unsteady shear creep, A 1 , A 2 , m and n are all creep calculation parameters, which can be determined based on indoor creep tests; τ T is the ultimate strength of soil in the flowing state; t is the creep time history.

[0076] S2-2: The through-surface or through-zone in the landslide body where the rate of change of shear creep rate is greater than 0 is defined as the sliding surface or sliding zone;

[0077] S2-3: The last penetrated section of the sliding surface or sliding belt obtained in S2-2 is defined as the slope anti-sliding section;

[0078] S2-4: Determine the anti-slip section in the potential sliding surface and its projection position on the ground surface, the potential main sliding line and the scope of possible slope instability and damage through S1 and S2-3.

[0079] In S3, a nonlinear mapping relationship between the slope surface displacement or crack width at the projection position of the anti-slip segment in the potential sliding surface on the ground surface and the shear creep rate at the corresponding anti-slip segment is established through S1 and S2.

[0080] In S4, a machine vision deformer and target equipped with a non-contact intelligent perception sensor and integrating machine vision and video surveillance technology are installed through S2;

[0081] S4-1: Install a machine vision deformation instrument equipped with a non-contact intelligent perception sensor and integrating machine vision and video monitoring technology at a location within 10 to 500 meters from the monitored slope;

[0082] S4-2: Install targets at the projected position on the ground surface on the potential main sliding line of the slope determined in S2 and in the anti-slip section of the potential sliding surface within 5 to 30 m nearby.

[0083] In S5, the displacement of the target or the crack width is obtained through S4;

[0084] S5-1: Real-time capture of preliminary frame images of slope deformation through S4;

[0085] S5-2: Establish an optimization model of the Bayesian-convolutional neural network through S5-1 to perform mapping learning on the captured real-time preliminary frame image;

[0086] S5-3: Obtain the best feature point sequence through S5-2 to generate a higher resolution image;

[0087] S5-4: The displacement or crack width and video data of the slope target are calculated by using visual deformation monitoring technology through S5-3.

[0088] In S6, the displacement or crack width and video data obtained in S5 are automatically transmitted in real time to the computer of the remote client through the virtual IP technology of the Internet of Things;

[0089] S6-1: A data collection station powered by solar energy is set up 1 to 10 meters away from the machine vision deformer installed in S4-1. The data collection station and the machine vision deformer are connected by an optical fiber cable.

[0090] S6-2: The displacement or crack width of the slope target and the video data obtained by S5 are automatically transmitted in real time to the computer of the remote client through the virtual IP technology of the Internet of Things in the form of mobile signals.

[0091] In S7, the shear creep rate at the anti-slip section corresponding to the target is calculated through S2 and S6.

[0092] In S8, the slope stability is dynamically evaluated through S7.

[0093] S8-1: If the change rate of the shear creep rate at the anti-slip section corresponding to the target obtained in S7 is less than 0, it is considered that the slope will tend to be stable;

[0094] S8-2: If the change rate of the shear creep rate at the anti-slip section corresponding to the target obtained in S7 is equal to 0, it is considered that the slope will tend to be destroyed;

[0095] S8-3: If the rate of change of the shear creep rate at the anti-slip section corresponding to the target obtained in S7 is greater than 0, it is considered that the slope is about to become unstable and fail.

[0096] The super-high fill slope supported by a reinforced earth retaining wall has a total length of 150m and a width of 26m. Taking the height of 15m as an example, it is divided into two levels, the first level wall is 7m high, the second level wall is 8m high, the step width between the upper and lower walls is 2m, the wall slope is 1:0.5, and the horseway width is 2m. The geogrid reinforcement length is 10m and the vertical spacing is 1m. In order to monitor the stability of the slope and ensure the safety of the slope, the slope remote video monitoring stability analysis system and method of the present invention are now used. The physical and mechanical parameters of the slope soil are shown in Table 1.

[0097] Table 1 Physical and mechanical calculation parameters of slope soil

[0098]

[0099]

[0100] Firstly, according to the calculation parameters shown in Table 1, a three-dimensional unsteady viscoelastic-plastic shear creep model is established. The shear creep rate change rate of the slope soil is calculated through finite element numerical simulation. The potential main sliding line and sliding surface or sliding zone of the slope and the anti-slip section of the slope are defined according to the shear creep rate change rate, and the stability of the soil landslide is dynamically evaluated.

[0101] Then, according to the potential main sliding line and sliding surface or sliding belt of the slope defined by the shear creep rate change rate and the anti-slip section of the slope, a machine vision deformer equipped with a non-contact intelligent sensing sensor and integrating machine vision and video monitoring technology is installed and targets are arranged.

[0102] Finally, according to the target displacement or crack width obtained by the machine vision deformation instrument equipped with non-contact intelligent sensing sensors and integrating machine vision and video monitoring technology, the change rate of the shear creep rate at the corresponding anti-slip section is calculated, which more accurately reflects the actual situation of the dynamic change of soil slope stability and is more conducive to the dynamic evaluation of slope stability in a timely manner under rainfall conditions. In this way, the evaluation results are more accurate and effective, and the obtained data can more accurately reflect the changes in slope stability.

[0103] The advantages of the present invention are:

[0104] (1) The present invention establishes a three-dimensional unsteady viscoelastic-plastic shear creep model, calculates the shear creep rate change rate of the slope soil, defines the potential main sliding line and sliding surface or sliding zone of the slope and the anti-slip section of the slope according to the shear creep rate change rate, and performs a dynamic evaluation of the stability of the soil landslide, so that the evaluation result of the present invention is more accurate and effective, providing strong technical support for predicting the occurrence of landslide disasters.

[0105] (2) The present invention defines the potential main sliding line and sliding surface or sliding belt of the slope and the anti-slip section of the slope according to the shear creep rate change rate. Based on this, a machine vision deformometer equipped with a non-contact intelligent sensing sensor and integrating machine vision and video monitoring technology is installed and targets are arranged, so that the obtained data can better reflect the changes in slope stability.

[0106] (3) The present invention calculates the rate of change of the shear creep rate at the corresponding anti-slip section based on the target displacement or crack width obtained by a machine vision deformer equipped with a non-contact intelligent sensing sensor and integrating machine vision and video monitoring technology, so as to more accurately reflect the actual situation of the dynamic change of soil slope stability, and facilitate timely dynamic evaluation of slope stability under rainfall conditions.

[0107] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A slope remote video monitoring stability analysis method, characterized in that: The method comprises the following steps: S1: Construct a three-dimensional unsteady viscoelastic-plastic shear creep model to obtain slope stability data under rainfall. S2: Calculate the shear creep rate on the landslide body based on the slope stability data under rainfall, define the potential main sliding line and sliding part based on the shear creep rate, and define the last penetrated section in the sliding section as the anti-sliding section; S3: Determine the nonlinear mapping relationship between the slope surface displacement or crack width at the projection position of the anti-slip segment in the sliding part on the ground surface and the shear creep rate at the corresponding anti-slip segment: S4: Set sensors and targets based on the main slide line; S5: Obtain the displacement of the target or the crack width; S6: outputting the displacement or crack width to a remote computer; S7: The remote computer obtains the shear creep rate at the anti-slip section corresponding to the target based on the displacement or crack width of the target and the nonlinear mapping relationship; S8: The slope stability is determined based on the shear creep rate at the anti-slip section corresponding to the target, and the slope stability analysis result is obtained.

2. A slope remote video monitoring stability analysis method according to claim 1, characterized in that: The specific steps of S1 are: S1-1: Construct a three-dimensional unsteady viscoelastic-plastic shear creep model; S1-2: Obtain the data required for the model through triaxial creep tests on unsaturated / saturated soil; S1-3: Based on the model and the parameters required by the model, a three-dimensional finite element numerical simulation of the slope stability under rainfall is performed to obtain the slope stability data under rainfall.

3. A slope remote video monitoring stability analysis method according to claim 2, characterized in that: The data required for the model include permeability coefficient, suction, cohesion, internal friction angle, long-term strength, elastic modulus, viscosity coefficient, soil yield stress σ f , the ultimate strength of soil in the flowing state τ T and creep calculation parameters.

4. A slope remote video monitoring stability analysis method according to claim 1, characterized in that: The specific steps of S2 are: S2-1: The shear creep rate is calculated based on the stability data of the lower slope. The longitudinal line with the fastest change in shear creep rate on the landslide body is defined as the potential main sliding line, which represents the main sliding direction of the overall sliding of the slope; S2-2: The through-surface or through-zone in the landslide body where the rate of change of shear creep rate is greater than 0 is defined as the sliding surface or sliding zone; S2-3: The last penetrated section in the sliding part is defined as the slope anti-sliding section; S2-4: Determine the anti-slip section in the sliding part and its projection position on the ground surface, the potential main sliding line and the scope of possible slope instability and damage.

5. A slope remote video monitoring stability analysis method according to claim 4, characterized in that: The shear creep rate is: Among them, in the formula, is the shear creep rate, σ is the current stress, σ f is the yield stress of soil, η2 is the viscosity coefficient of unsteady shear creep, A1, A2, m and n are all creep calculation parameters, τ T is the ultimate strength of soil in the flowing state; t is the creep time history.

6. A slope remote video monitoring stability analysis method according to claim 1, characterized in that: The specific steps of S4 are: S4-1: Install non-contact intelligent perception sensors and machine vision deformation meters within a certain range of the slope; S4-2: Install targets on the projected position of the ground surface in the slope anti-sliding section of the sliding part on the potential main sliding line and within a certain range near it.

7. A slope remote video monitoring stability analysis method according to claim 1, characterized in that: The specific steps of S5 are: S5-1: Capture the real-time preliminary frame image of slope deformation based on non-contact intelligent sensing sensor; S5-2: Establish an optimization model of the Bayesian-convolutional neural network and input the preliminary frame image into the optimization model; S5-3: Optimize the model to obtain the best feature point sequence and output a high-resolution image; S5-4: Based on machine vision deformometer, the displacement or crack width of the target and video data are obtained from high-resolution images.

8. A slope remote video monitoring stability analysis method according to claim 1, characterized in that: The specific steps of S6 are: S6-1: A data collection station powered by solar energy is set at a certain position of the machine vision deformer, and the data collection station is connected to the machine vision deformer via an optical fiber cable; S6-2: Transmit the displacement of the target or the crack width to a remote computer in the form of a mobile signal.

9. A slope remote video monitoring stability analysis method according to claim 1, characterized in that: The specific steps of S8 are: S8-1: If the change rate of the shear creep rate at the anti-slip section corresponding to the target is less than 0, the slope is considered to be stable; S8-2: If the change rate of the shear creep rate at the anti-slip section corresponding to the target is equal to 0, it is considered that the slope will tend to be destroyed; S8-3: If the rate of change of the shear creep rate at the anti-slip section corresponding to the target is greater than 0, it is considered that the slope is about to become unstable and fail.

10. A slope remote video monitoring stability analysis system, characterized in that: The method comprises a memory, a processor, and a program stored in the memory, wherein the processor implements the method according to any one of claims 1 to 8 when executing the program.

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