A method for back-calculating landslide critical shear strength parameters combined with bionic optimization algorithm

Abstract

The present application provides a method for inverting the critical shear strength parameters of a landslide in combination with a bionic optimization algorithm, which relates to the field of landslide monitoring. The method includes: obtaining landslide surface deformation data and performing 2D decomposition; based on the principle of conservation of mass, combining the decomposed landslide surface deformation data, establishing a sliding surface model to obtain the optimal landslide depth value; using an improved particle swarm optimization algorithm, combining the sliding surface model and the optimal landslide depth value, inverting to obtain the optimal sliding surface; based on the optimal sliding surface, establishing a landslide stability model; using the whale optimization algorithm, combined with the landslide stability model, inverting to obtain the optimal shear strength parameters. By coupling the sliding surface data with the landslide stability model and using the global search capability of the whale optimization algorithm, the critical shear strength parameters are accurately inverted. A stable and effective landslide stability inversion analysis model is constructed, which improves the accuracy of parameter inversion and provides a reliable tool for landslide risk assessment.

Description

Technical Field

[0001] The present application relates to the field of landslide monitoring, and in particular to a landslide critical shear strength parameter inversion method combined with a bionic optimization algorithm. Background Art

[0002] In recent years, with the development of remote sensing technology, the application of synthetic aperture radar interferometry (InSAR) in landslide monitoring has become increasingly widespread. InSAR technology can monitor surface deformation with high precision and over a large area without relying on on-site observations, thus providing an important basis for deformation analysis and stability assessment of landslides. Compared with traditional monitoring methods, InSAR technology has the advantages of all-weather, long-term series monitoring, and low cost, making it particularly suitable for landslide monitoring in inaccessible and dangerous areas. However, the current use of InSAR technology for landslide monitoring is mostly applied to monitor deformation data of the landslide surface, revealing the deformation characteristics of the landslide body before the disaster, and is less used for the inversion of geotechnical engineering parameters of the landslide body. The main problems lie in the complex nonlinear relationship between deformation information and landslide mechanical parameters, the lack of physical models, and the multi-solution problem in the inversion process. Summary of the Invention

[0003] The purpose of the present invention is to provide a landslide critical shear strength parameter inversion method combined with a bionic optimization algorithm in order to solve the problems of the existing method of monitoring landslides using InSAR technology, such as the complex nonlinear relationship between deformation information and landslide mechanical parameters, the lack of physical models, and the multi-solution problem in the inversion process.

[0004] The above-mentioned purpose of this application is achieved through the following technical solutions:

[0005] S1: Obtain landslide surface deformation data;

[0006] S2: 2D decomposition of landslide surface deformation data;

[0007] S3: Based on the principle of mass conservation and combined with the landslide surface deformation data after 2D decomposition, a sliding surface model is established to obtain the optimal landslide depth value;

[0008] S4: The optimal sliding surface is obtained by inversion using the improved particle swarm optimization algorithm, combined with the sliding surface model and the optimal landslide depth value;

[0009] S5: Establish a landslide stability model based on the optimal sliding surface;

[0010] S6: Using the Whale optimization algorithm and combining it with the landslide stability model, the optimal shear strength parameters are obtained by inversion.

[0011] This application adopts the above-mentioned technical solution, uses InSAR technology to obtain the displacement field data of the landslide area (landslide surface deformation data), constructs an accurate sliding surface model and landslide stability analysis model, and uses a heuristic optimization algorithm to invert the critical shear strength parameters (cohesion, internal friction angle) to achieve efficient and accurate estimation of the critical shear strength of the landslide.

[0012] Optionally, step S1 includes: acquiring landslide surface deformation data based on Sentinel-1A ascending and descending orbit images; the landslide surface deformation data includes: landslide surface deformation displacement and landslide surface deformation rate.

[0013] Optionally, step S2 includes:

[0014] Establish a slope coordinate system ABC to form a right-handed spiral coordinate system. The slope coordinate system ABC includes: along the slope axis OA, along the normal axis OC, and along the perpendicular slope axis OB;

[0015] It is defined that downward movement along the slope axis is positive, and movement out of the sliding plane along the normal axis is positive;

[0016] The three-dimensional deformation of the slope coordinate system and the line-of-sight deformation of the landslide surface deformation data satisfy the following relationship:

[0017]

[0018] Where: Represents the landslide surface deformation data of the landslide body; a, b, and c are the projection coefficients of the deformation along the slope direction, along the perpendicular slope direction, and along the normal direction, respectively; Represent the deformation along the OA, OB, and OC directions respectively; and represents the average slope and aspect angle of the landslide; θ and φ represent the radar satellite incident angle and flight direction angle;

[0019] To solve the deformation along the slope direction and along the normal direction of the landslide surface deformation data, formula (1) is simplified to formula (2), as follows:

[0020] .

[0021] Optionally, step S3 includes:

[0022] S31: Assume that the landslide body is incompressible and the density remains constant. Consider the entire landslide body as a continuum composed of various control bodies. Perform a perpendicular integral between the base sliding surface and the upper sliding surface of the landslide to construct a sliding surface model as follows:

[0023]

[0024] in is the depth of the control volume, represents the average deformation component, , represents the average deformation component along the slope direction, represents the average deformation component along the normal direction;

[0025] S32: Assuming that the sliding of the landslide body per unit time is 0, then convert formula (3) into , that is, the rate of change of thickness is equal to the normal deformation rate. Combining the kinematic boundary conditions with the fluid power law function, we can obtain:

[0026]

[0027] in represents the normal deformation rate; , Indicates the surface movement speed of the slope; represents rheological parameters;

[0028] S33: According to the central difference and divergence calculation rules, expand formula (4) into formula (5):

[0029]

[0030] in Indicates the Rank Normal deformation rate of the column; Indicates the data sampling interval along the slope aspect and perpendicular to the slope aspect; and represents the slope deformation rate and normal deformation rate; Indicates the Rank The deformation rate of the column along the slope direction; Indicates the Rank The depth value of the sliding surface of the column;

[0031] S34: Rewrite formula (5) as ,in represents the coefficient matrix including sampling interval, rheological parameters and slope surface deformation rate; represents the sliding surface depth matrix;

[0032] Combined with the regularized one-two paradigm, the objective function is set as: Perform inverse calculation to obtain the optimal landslide depth value.

[0033] Optionally, step S4 includes:

[0034] Taking the sliding surface model as the target equation, the particle update rule is designed. The update rule is as follows

[0035]

[0036] in Represents the updated inertia weight; is the inertia weight, which is used to balance the global and local search capabilities; Represents a random number from 0 to 1; represents the minimum inertia weight; represents the maximum inertia weight; Represents particles exist The rate of time, and is the learning factor; Indicates the historical optimal point; Represents particles arrive The historical optimal position at the moment; Represents particles exist The sliding surface depth value at the moment; and for Pseudo-random number; represents the global optimal position, that is, the optimal sliding surface.

[0037] Optionally, step S5 includes:

[0038] The Morgenstern-Price slope stability analysis method is used to construct a landslide stability model, as follows:

[0039] The landslide body is divided into n vertical strips, and the width of each strip is ;

[0040] Assume that the sliding surface of the optimal sliding surface is an arc with the center being , the radius is R, and the coordinates of the vertical bar on the sliding surface are expressed as ;

[0041] The gravity of each vertical bar is , acting on the center of gravity of the vertical bar;

[0042] The normal force is expressed as , the shear force is expressed as ,in is the angle between the bottom edge of the vertical bar and the horizontal line, usually ;

[0043] Considering pore water pressure Horizontal force between vertical bars , shear force between strips ;

[0044] Assuming that the interaction force between the bars is distributed as a half-sine function, the anti-slip torque between the bars is composed of the friction torque of the normal force and the cohesion torque, and the total anti-slip torque is

[0045]

[0046] Where R is the moment arm, represents the tangent value of the friction angle within the soil; c represents the cohesion of the soil;

[0047] According to the horizontal shear force balance, the driving torque of each vertical bar is , which is composed of its gravity component and the shear force between the bars, the total sliding moment is

[0048]

[0049] The safety factor SF is obtained by dividing the total anti-slip torque by the total sliding torque and is expressed as

[0050] .

[0051] Optionally, step S6 includes:

[0052] Based on the landslide stability model, the objective equation is constructed;

[0053] By initializing the whale group, calculating the fitness value of the objective equation, and gradually updating the position using the spiral update rule in the whale optimization algorithm, the optimal shear strength parameters are obtained.

[0054] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs a landslide critical shear strength parameter inversion method combined with a bionic optimization algorithm.

[0055] A computer-readable storage medium stores instructions. When the instructions are executed, a landslide critical shear strength parameter inversion method combined with a bionic optimization algorithm is executed.

[0056] The beneficial effects of the technical solution provided by this application are:

[0057] 1. A new sliding surface inversion method is proposed, which combines InSAR technology with an improved particle swarm optimization algorithm to invert the landslide sliding surface. This method obtains continuous sliding surface data in a non-contact manner, improving the accuracy and continuity of the inverted sliding surface, which has obvious advantages in particular for high-lying and long-range landslides.

[0058] 2. Establish a geotechnical inversion model for landslide shear strength. Based on the optimal sliding surface after inversion, the Whale Optimization Algorithm can rapidly optimize the estimation of landslide critical shear strength parameters, reducing the time-consuming and labor-intensive traditional methods and the uncertainties caused by artificial assumptions and empirical judgments.

[0059] 3. By combining remote sensing monitoring with biomimetic optimization algorithms, accurate inversion of the critical shear strength of large-scale slope areas can be achieved. This avoids the time-consuming, labor-intensive, and costly nature of on-site landslide exploration or indoor physical and mechanical testing. This allows for efficient inversion of the critical shear strength of landslides during landslide stability analysis, improving landslide risk assessment capabilities and providing a new technical path for landslide disaster prediction and prevention. This will contribute to the automated and intelligent development of landslide monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The present application will be further described below with reference to the accompanying drawings and embodiments, in which:

[0061] Figure 1 It is a step diagram in the embodiment of the present application;

[0062] Figure 2 This is a time-space baseline diagram of the lifting and lowering orbit interference connection pair in the embodiment of the present application;

[0063] Figure 3 is a graph of the average annual deformation rate of the lifting rail in the embodiment of the present application;

[0064] Figure 4 It is a two-dimensional decomposition into a deformation rate diagram along the slope normal in the embodiment of the present application;

[0065] Figure 5 is the optimal sliding surface depth map after inversion in the embodiment of the present application;

[0066] Figure 6 is a schematic diagram of the structure of an electronic device in an embodiment of the present application;

[0067] Figure 7 It is a schematic diagram of modeling verification in an embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to have a clearer understanding of the technical features, purposes and effects of this application, the specific implementation methods of this application are now described in detail with reference to the accompanying drawings.

[0069] The embodiments of the present application provide a method for inverting landslide critical shear strength parameters in combination with a bionic optimization algorithm.

[0070] Please refer to Figure 1 , Figure 1This is a step diagram of a method for inverting landslide critical shear strength parameters in combination with a bionic optimization algorithm in an embodiment of the present application, comprising:

[0071] S1: Obtain landslide surface deformation data;

[0072] S2: 2D decomposition of landslide surface deformation data;

[0073] S3: Based on the principle of mass conservation and combined with the landslide surface deformation data after 2D decomposition, a sliding surface model is established to obtain the optimal landslide depth value;

[0074] S4: The optimal sliding surface is obtained by inversion using the improved particle swarm optimization algorithm, combined with the sliding surface model and the optimal landslide depth value;

[0075] S5: Establish a landslide stability model based on the optimal sliding surface;

[0076] S6: Using the Whale optimization algorithm and combining it with the landslide stability model, the optimal shear strength parameters are obtained by inversion.

[0077] Specifically, this application also obtains ALOS_DEM data for removing flat land effects and performing terrain correction.

[0078] Specifically, as a global optimization algorithm, the bionic algorithm has shown great potential in solving complex nonlinear problems. By simulating the evolution, foraging and cooperative behavior of organisms in nature, the bionic algorithm searches for the optimal solution in a huge solution space in a group collaborative manner, which can effectively solve the complex nonlinear calculations involved in the inversion problem. The critical shear strength parameter inversion method that combines InSAR technology with a variety of bionic optimization algorithms (improved particle swarm optimization algorithm, whale optimization algorithm) is based on the inverted sliding surface data and the safety factor landslide stability model constructed by the Morgenstern-Price method for inversion. This method, combined with the safety factor landslide stability model constructed by the Morgenstern-Price method, ensures the rationality and accuracy of the inversion results.

[0079] Step S1 includes: acquiring landslide surface deformation data based on Sentinel-1A ascending and descending orbit images; the landslide surface deformation data includes: landslide surface deformation displacement and landslide surface deformation rate.

[0080] This application provides an embodiment as follows: using Sentinel-1 satellite SAR data provided by the European Space Agency's Copernicus program, SBAS-InSAR technology is used to obtain landslide surface deformation displacement and rate. In addition, Radar Topography Mission (SRTM) DEM data with a ground resolution of 30m is used to remove flat ground effects and perform terrain correction. To ensure the reliability of data processing, the present invention selects short-baseline interferometer pairs, adaptively determines the coherence coefficient threshold, and performs filtering and phase unwrapping to remove orbital errors, atmospheric errors, and terrain errors to obtain high-quality annual average deformation rate values for slope ascending and descending orbits.

[0081] Step S2 includes:

[0082] Establish a slope coordinate system ABC to form a right-handed spiral coordinate system. The slope coordinate system ABC includes: along the slope axis OA, along the normal axis OC, and along the perpendicular slope axis OB;

[0083] It is defined that downward movement along the slope axis is positive, and movement out of the sliding plane along the normal axis is positive;

[0084] The three-dimensional deformation of the slope coordinate system and the line-of-sight deformation of the landslide surface deformation data satisfy the following relationship:

[0085]

[0086] Where: Represents the landslide surface deformation data of the landslide body; a, b, and c are the projection coefficients of the deformation along the slope direction, along the perpendicular slope direction, and along the normal direction, respectively; Represent the deformation along the OA, OB, and OC directions respectively; and represents the average slope and aspect angle of the landslide; θ and φ represent the radar satellite incident angle and flight direction angle;

[0087] To solve the deformation along the slope direction and along the normal direction of the landslide surface deformation data, formula (1) is simplified to formula (2), as follows:

[0088] .

[0089] The present application provides an embodiment as follows. Since SAR (Synthetic Aperture Radar) is a side-mounted imaging system, the deformation information obtained by monitoring is the radar line of sight (LOS) direction, which has certain limitations in accurately reflecting the actual surface deformation. In order to improve the accuracy of deformation measurement and interpretation effect, and provide more realistic surface deformation information, the present invention performs a two-dimensional decomposition of the LOS deformation information obtained based on Sentinel-1A ascending and descending orbit images to obtain deformation information along the slope and perpendicular to the slope. That is, the slope coordinate system is established using the slope and direction information of the slope body, and the decomposition is combined with information such as the satellite incident angle and azimuth. When a landslide slides under gravity, the deformation amount moving downward along the sliding surface is much greater than the deformation amount perpendicular to the slope. Therefore, the present invention combines the Sentinel-1A ascending and descending orbit to calculate the deformation along the slope and normal direction.

[0090] Step S3 includes:

[0091] S31: Assume that the landslide body is incompressible and the density remains constant. Consider the entire landslide body as a continuum composed of various control bodies. Perform a perpendicular integral between the base sliding surface and the upper sliding surface of the landslide to construct a sliding surface model as follows:

[0092]

[0093] in is the depth of the control volume, represents the average deformation component, , represents the average deformation component along the slope direction, represents the average deformation component along the normal direction;

[0094] S32: Assuming that the sliding of the landslide body per unit time is 0, then convert formula (3) into , that is, the rate of change of thickness is equal to the normal deformation rate. Combining the kinematic boundary conditions with the fluid power law function, we can obtain:

[0095]

[0096] in represents the normal deformation rate; , Indicates the surface movement speed of the slope; represents rheological parameters;

[0097] S33: According to the central difference and divergence calculation rules, expand formula (4) into formula (5):

[0098]

[0099] in Indicates the Rank Normal deformation rate of the column; Indicates the data sampling interval along the slope aspect and perpendicular to the slope aspect; and represents the slope deformation rate and normal deformation rate; Indicates the Rank The deformation rate of the column along the slope direction; Indicates the Rank The depth value of the sliding surface of the column;

[0100] S34: Rewrite formula (5) as ,in represents the coefficient matrix including sampling interval, rheological parameters and slope surface deformation rate; represents the sliding surface depth matrix;

[0101] Combined with the regularized one-two paradigm, the objective function is set as: Perform inverse calculation to obtain the optimal landslide depth value.

[0102] This application provides an embodiment as follows: a sliding surface model is established based on the principle of mass conservation, which assumes no mass loss per unit time. For soil landslides with creeping sliding, it can be assumed that the landslide is incompressible and the density remains constant. Therefore, applying mass conservation to landslide modeling is feasible. The landslide is considered a continuum composed of multiple control volumes, and a perpendicular integral is performed between the base sliding surface and the upper sliding surface to construct the model.

[0103] Step S4 includes:

[0104] Taking the sliding surface model as the target equation, the particle update rule is designed. The update rule is as follows

[0105]

[0106] in Represents the updated inertia weight; is the inertia weight, which is used to balance the global and local search capabilities; Represents a random number from 0 to 1; represents the minimum inertia weight; represents the maximum inertia weight; Represents particles exist The rate of time, and is the learning factor; Indicates the historical optimal point; Represents particles arrive The historical optimal position at the moment; Represents particles exist The sliding surface depth value at the moment; and for Pseudo-random number; represents the global optimal position, that is, the optimal sliding surface.

[0107] The present application provides an embodiment as follows: In a 2-dimensional solution space, there are N particles forming a group, and each particle moves at a certain speed. Each particle moves towards the previous individual best position ( ) and the global optimal position ( ) to obtain the optimal solution. This paper constructs a sliding surface model based on mass conservation as the objective equation and designs a particle update rule to better overcome the limitations of local optimal solutions and obtain a global optimal solution. This rule improves on the traditional particle swarm update rule by reducing the weight ratio and balancing global and local search capabilities. and The learning factor represents the ability of the particle to learn from itself and the excellent particles in the group; and to move towards the historical optimal point. The particles are updated through multiple iterations to get closer to the optimal solution, and the final global optimal solution is obtained. .

[0108] Step S5 includes:

[0109] The Morgenstern-Price slope stability analysis method is used to construct a landslide stability model, as follows:

[0110] The landslide body is divided into n vertical strips, and the width of each strip is ;

[0111] Assume that the sliding surface of the optimal sliding surface is an arc with the center being , the radius is R, and the coordinates of the vertical bar on the sliding surface are expressed as ;

[0112] The gravity of each vertical bar is , acting on the center of gravity of the vertical bar;

[0113] The normal force is expressed as , the shear force is expressed as ,in is the angle between the bottom edge of the vertical bar and the horizontal line, usually ;

[0114] Considering pore water pressure Horizontal force between vertical bars , inter-strip shear force ;

[0115] Assuming that the interaction force between the bars is distributed as a half-sine function, the anti-slip torque between the bars is composed of the friction torque of the normal force and the cohesion torque, and the total anti-slip torque is

[0116]

[0117] Where R is the moment arm, represents the tangent value of the friction angle within the soil; c represents the cohesion of the soil;

[0118] According to the horizontal shear force balance, the driving torque of each vertical bar is , which is composed of its gravity component and the shear force between the bars, the total sliding moment is

[0119]

[0120] The safety factor SF is obtained by dividing the total anti-slip torque by the total sliding torque and is expressed as

[0121] .

[0122] The present application provides an embodiment as follows, which uses the Morgenstern-Price slope stability analysis method to construct a landslide stability model. This method assumes that the slip surface is a curve of arbitrary shape when the slope is destroyed, and divides the sliding body into several blocks along the slip surface. By solving the force balance and moment balance of each block, while considering the interaction forces (horizontal force and shear force) between the blocks, the safety factor (SF) of the entire sliding body is finally obtained through iteration, thereby evaluating the stability of the slope.

[0123] Step S6 includes:

[0124] Based on the landslide stability model, the objective equation is constructed;

[0125] By initializing the whale group, calculating the fitness value of the objective equation, and gradually updating the position using the spiral update rule in the whale optimization algorithm, the optimal shear strength parameters are obtained.

[0126] The present invention utilizes Sentinel-1 SAR data to generate short-baseline interferometry pairs, performs short-baseline time series processing, and obtains a curve diagram of the deformation rate of the ascending and descending orbit. Figure 2 It is the space-time baseline diagram of the interferometric connection of the ascending and descending orbits. Figure 3 is the average annual deformation rate of the ascending and descending orbit.

[0127] Combining satellite parameters and landslide slope and aspect, the LOS deformation rate is decomposed into along-slope and normal deformation in two dimensions. A sliding surface model is established based on the principle of mass conservation, and the optimal sliding surface depth is inverted using an improved particle swarm optimization algorithm. Combined with the surface digital elevation model, the optimal sliding surface is obtained. Figure 4 It is a two-dimensional decomposition into the deformation rate along the slope, such as Figure 4 (a), the deformation rate along the normal direction is as follows Figure 4 (b) Figure 5 is the optimal sliding surface depth after inversion.

[0128] Based on the optimal sliding surface data obtained by the above inversion, the safety factor landslide stability model constructed by the Morgenstern-Price method was combined with the whale optimization algorithm for inversion to obtain the critical shear strength parameters.

[0129] Table 1 shows the distribution of parameters and safety factors after inversion.

[0130] Table 1 Inversion parameters

[0131]

[0132] The reliability of the critical shear strength parameter values (cohesion, internal friction angle) obtained by the proposed method was tested by using the Slop / W module of Geostudio software in geotechnical engineering to model the structure. It was found that the safety factor obtained by the modeling in Geostudio was 1.069 ( Figure 7 ), with a safety factor close to that of the method proposed in the present invention, differing by only 0.016, demonstrating that the proposed method can effectively invert the critical shear strength parameters of landslides. By combining remote sensing monitoring with an optimization algorithm, the present invention achieves accurate inversion of the critical shear strength of large slope areas, avoiding the time-consuming, labor-intensive, and costly challenges of on-site landslide exploration or indoor physical and mechanical testing. This allows for efficient inversion of the critical shear strength of landslides during landslide stability analysis, improving landslide risk assessment capabilities and providing a new technical approach for landslide disaster prediction and prevention. This contributes to the automated and intelligent development of landslide monitoring and early warning.

[0133] This application also discloses an electronic device. Figure 6 , Figure 6 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0134] The communication bus 502 is used to implement the connection and communication between these components.

[0135] The user interface 503 may include a display screen, and the optional user interface 503 may also include a standard wired interface or a wireless interface.

[0136] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0137] The present application also discloses a computer-readable storage medium storing a plurality of instructions suitable for loading by a processor to execute the above-mentioned landslide critical shear strength parameter inversion method combined with a bionic optimization algorithm.

[0138] The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. In other words, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure.

[0139] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A method for inverting landslide critical shear strength parameters combined with a bionic optimization algorithm, characterized in that: The method comprises the following steps: S1: Obtain landslide surface deformation data; S2: 2D decomposition of landslide surface deformation data; S3: Based on the principle of mass conservation and combined with the landslide surface deformation data after 2D decomposition, a sliding surface model is established to obtain the optimal landslide depth value; Step S3 includes: S31: Assume that the landslide body is incompressible and the density remains constant. Consider the entire landslide body as a continuum composed of various control bodies. Perform a perpendicular integral between the base sliding surface and the upper sliding surface of the landslide to construct a sliding surface model as follows: Where h is the depth of the control volume, represents the average deformation component, v x represents the average deformation component along the slope direction, v y represents the average deformation component along the normal direction; S32: Assuming that the sliding of the landslide body per unit time is 0, then convert formula (3) into That is, the rate of change of thickness is equal to the normal deformation rate. Combining the kinematic boundary conditions with the fluid power law function, we get: where v z represents the normal deformation rate; represents the surface movement velocity of the slope; f represents the rheological parameter; S33: According to the central difference and divergence calculation rules, expand formula (4) into formula (5): where v z (i, j) represents the normal deformation rate of the i-th row and j-th column; Δx represents the data sampling interval along the slope and perpendicular to the slope; v x and v z represents the slope deformation rate and normal deformation rate; v x (i,j) represents the deformation rate along the slope of the i-th row and j-th column; h (i,j) Indicates the depth value of the sliding surface in row i and column j; S34: Rewrite formula (5) as in represents the coefficient matrix including sampling interval, rheological parameters and slope surface deformation rate; represents the sliding surface depth matrix; Combined with the regularized one-two paradigm, the objective function is set as: Perform inverse calculation to obtain the optimal landslide depth value; S4: The optimal sliding surface is obtained by inversion using the improved particle swarm optimization algorithm, combined with the sliding surface model and the optimal landslide depth value; S5: Establish a landslide stability model based on the optimal sliding surface; S6: Using the Whale optimization algorithm and combining it with the landslide stability model, the optimal shear strength parameters are obtained by inversion.

2. The method for inverting landslide critical shear strength parameters combined with a bionic optimization algorithm according to claim 1, characterized in that: Step S1 includes: acquiring landslide surface deformation data based on Sentinel-1A ascending and descending orbit images; the landslide surface deformation data includes: landslide surface deformation displacement and landslide surface deformation rate.

3. The method for inverting landslide critical shear strength parameters combined with a bionic optimization algorithm according to claim 1, characterized in that: Step S2 includes: Establish a slope coordinate system ABC to form a right-handed spiral coordinate system. The slope coordinate system ABC includes: along the slope axis OA, along the normal axis OC, and along the perpendicular slope axis OB; It is defined that downward movement along the slope axis is positive, and movement out of the sliding plane along the normal axis is positive; The three-dimensional deformation of the slope coordinate system and the line-of-sight deformation of the landslide surface deformation data satisfy the following relationship: Where: D LOS represents the landslide surface deformation data of the landslide body; a, b, c are the projection coefficients of the deformation along the slope direction, along the vertical slope direction, and along the normal direction, respectively; D A 、D B 、D C represent the deformation along the OA, OB, and OC directions respectively; α and β represent the average slope and aspect angle of the landslide; θ and Represents the radar satellite incident angle and flight direction angle; To solve the deformation along the slope direction and along the normal direction of the landslide surface deformation data, formula (1) is simplified to formula (2), as follows:

4. The method for inverting landslide critical shear strength parameters in combination with a bionic optimization algorithm according to claim 1, characterized in that: Step S4 includes: Taking the sliding surface model as the target equation, the particle update rule is designed. The update rule is as follows Where ω′ represents the updated inertia weight; ω is the inertia weight, which is used to balance the global and local search capabilities; σ * N(0.1) represents a random number between 0 and 1; w min represents the minimum inertia weight; w max represents the maximum inertia weight; represents the velocity of particle i at time t+1, c1 and c2 are learning factors; p best Indicates the historical optimal point; represents the historical optimal position of particle i at time t; represents the sliding surface depth value of particle i at time t; r1 and r2 are pseudo-random numbers in [0,1]; represents the global optimal position, that is, the optimal sliding surface.

5. The method for inverting landslide critical shear strength parameters combined with a bionic optimization algorithm according to claim 1, characterized in that: Step S5 includes: The Morgenstern-Price slope stability analysis method is used to construct a landslide stability model, as follows: Divide the landslide into n vertical strips, each strip has a width of b i ; Assume that the sliding surface of the optimal sliding surface is an arc with the center at (x c ,y c ), the radius is R, and the coordinates of the vertical bar on the sliding surface are expressed as (x i ,y i ); The weight of each vertical bar is W i , acting on the center of gravity of the vertical bar; Normal force is expressed as N i =W i cos(α i ), the shear force is expressed as T i =W i sin(α i ), where α i is the angle between the bottom edge of the vertical bar and the horizontal line, usually Considering the pore water pressure u i Horizontal force X between vertical bars i,i+1 , shear force between strips T i,i+1 ; Assuming that the interaction force between the bars is distributed as a half-sine function, the anti-slip torque between the bars is composed of the friction torque of the normal force and the cohesion torque, and the total anti-slip torque is Where R is the moment arm, tan(φ) represents the tangent of the internal friction angle of the soil; c represents the cohesion of the soil; According to the horizontal shear force balance, the driving torque M of each vertical bar is D,i , which is composed of its gravity component and the shear force between the bars, the total sliding moment is The safety factor SF is obtained by dividing the total anti-slip torque by the total sliding torque and is expressed as 6. The method for inverting landslide critical shear strength parameters in combination with a bionic optimization algorithm according to claim 1, characterized in that: Step S6 includes: Based on the landslide stability model, the objective equation is constructed; By initializing the whale group, calculating the fitness value of the objective equation, and gradually updating the position using the spiral update rule in the whale optimization algorithm, the optimal shear strength parameters are obtained.

7. An electronic device, characterized in that: The electronic device comprises a processor (501), a memory (505), a user interface (503) and a network interface (504), wherein the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device executes the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed by a computer, the method according to any one of claims 1 to 6 is executed.

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