Landslide critical shear strength parameter inversion method combined with bionic optimization algorithm

Abstract

The invention provides a landslide critical shear strength parameter inversion method combined with a bionic optimization algorithm, and relates to the field of landslide monitoring, and the method comprises the steps: obtaining landslide surface deformation data, and carrying out the 2D decomposition; based on the principle of mass conservation, combining the decomposed landslide surface deformation data, establishing a sliding surface model, and obtaining an optimal landslide depth value; through an improved particle swarm optimization algorithm, in combination with the sliding surface model and the optimal landslide depth value, performing inversion to obtain an optimal sliding surface; based on the optimal sliding surface, a landslide stability model is established; the whale optimization algorithm is utilized, and the landslide stability model is combined, so that an optimal shear strength parameter is obtained through inversion. The critical shear strength parameter is accurately inverted through the global search capability of the whale optimization algorithm by utilizing the coupling of the sliding surface data and the landslide stability model. A stable and effective landslide stability inversion analysis model is constructed, the accuracy of parameter inversion is improved, and a reliable tool is provided 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 more and more extensive. InSAR technology can monitor surface deformation with high precision and over a large range 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, and is particularly suitable for landslide monitoring in difficult-to-access dangerous areas. However, the current use of InSAR technology to monitor landslides is mostly used to monitor the deformation data of the landslide surface, revealing the deformation characteristics of the landslide body before the disaster, and less application in the inversion of geotechnical engineering parameters of the landslide body. The main problems are 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 InSAR technology for monitoring landslides, such as the complex nonlinear relationship between deformation information and landslide mechanical parameters, the lack of a physical model, and the multi-solution problem in the inversion process.

[0004] The above-mentioned purpose of the present application is achieved through the following technical solutions: 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, the sliding surface model is established to obtain the optimal landslide depth value; S4: The optimal sliding surface is obtained by inversion through 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 the landslide stability model, the optimal shear strength parameters are inverted.

[0005] This application adopts the above-mentioned technical solution, obtains the displacement field data (landslide surface deformation data) of the landslide area by using InSAR technology, 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.

[0006] Optionally, step S1 includes: acquiring landslide surface deformation data based on Sentinel-1A lift-orbit images; the landslide surface deformation data includes: landslide surface deformation displacement and landslide surface deformation rate.

[0007] Optionally, 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 vertical slope axis OB; Define downward movement along the slope axis as positive, and movement outside the sliding surface along the normal axis as 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:

[0008] Where: 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; Respectively represent the deformation along the OA, OB, and OC directions; and represents the average slope and aspect angle of the landslide; θ and φ represent the incident angle and flight direction angle of the radar satellite; 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: .

[0009] Optionally, step S3 includes: S31: Assuming that the landslide body is incompressible and the density remains unchanged, the entire landslide body is regarded as a continuum composed of various control bodies. The vertical integral is performed between the base sliding surface and the upper sliding surface of the landslide to construct the sliding surface model as follows:

[0010] in is the control volume depth, represents the average deformation component, , represents the average deformation component along the slope direction, 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 can obtain:

[0011] in represents the normal deformation rate; , Indicates the speed of slope surface movement; represents rheological parameters; S33: According to the central difference and divergence calculation rules, formula (4) is expanded into formula (5):

[0012] in Indicates Line The normal deformation rate of the column; Indicates the data sampling spacing along the slope aspect and perpendicular to the slope aspect; and represents the slope deformation rate and normal deformation rate; Indicates Line The deformation rate of the column along the slope direction; Indicates Line The depth value of the slide surface of the column; 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 to: The inverse calculation is performed to obtain the optimal landslide depth value.

[0013] Optionally, step S4 includes: Taking the sliding surface model as the target equation, the particle update rule is designed. The update rule is as follows:

[0014] 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 optimum point; Represents particles arrive The historical best position at the moment; Represents particles exist The sliding surface depth value at the moment; and for Pseudo-random numbers; represents the global optimal position, that is, the optimal sliding surface.

[0015] Optionally, step S5 includes: The Morgenstern-Price slope stability analysis method is used to construct a landslide stability model, as follows: The landslide body is divided into n vertical strips, and the width of each strip is ; 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 ; The gravity of each vertical bar is , acting on the center of gravity of the vertical bar; 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 ; Considering pore water pressure Horizontal force between vertical bars , inter-strip shear force ; Assuming that the interaction force between the bars is distributed as a semi-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

[0016] Where R is the moment arm, represents the tangent value of the friction angle within the soil; c represents the cohesion of the soil; 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

[0017] The safety factor SF is obtained by dividing the total anti-slip torque by the total sliding torque and is expressed as .

[0018] Optionally, step S6 includes: Based on the landslide stability model, the objective equation is constructed; By initializing the whale population, 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.

[0019] An electronic device comprises a processor, a memory, a user interface and a network interface, wherein 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 executes a landslide critical shear strength parameter inversion method combined with a bionic optimization algorithm.

[0020] 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.

[0021] The beneficial effects of the technical solution provided by this application are: 1. A new sliding surface inversion method is proposed, which combines InSAR technology with an improved particle swarm optimization algorithm to invert the sliding surface of the landslide. This method obtains continuous sliding surface data in a non-contact manner, improves the accuracy and continuity of the inverted sliding surface, and has obvious advantages especially for high-level remote landslides.

[0022] 2. Establish a geoscience model for the inversion of landslide shear strength. Based on the optimal sliding surface after inversion, the whale optimization algorithm can be used to quickly optimize the estimation of the critical shear strength parameters of the landslide, reducing the time-consuming and labor-intensive traditional methods and the uncertainties caused by artificial assumptions and empirical judgments.

[0023] 3. Through the combination of remote sensing monitoring and bionic optimization algorithm, the critical shear strength of a large range of slopes can be accurately inverted, avoiding the time-consuming, labor-intensive and high-cost problems of on-site landslide exploration or indoor physical and mechanical tests. It can achieve efficient inversion of the critical shear strength of landslides in the process of landslide stability analysis, improve the ability to assess landslide risks, and thus provide a new technical path for the prediction and prevention of landslide disasters. It is conducive to the automation and intelligent development of landslide monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present application will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 is a step diagram in an embodiment of the present application; Figure 2 It is a time-space baseline diagram of the lifting and lowering rail interference connection pair in the embodiment of the present application; Figure 3 is a graph of the average annual deformation rate of the lifting rail in the embodiment of the present application; Figure 4 It is a two-dimensional decomposition into a deformation rate diagram along the slope normal in the embodiment of the present application; Figure 5 is the optimal sliding surface depth map after inversion in the embodiment of the present application; Figure 6 is a schematic diagram of the structure of an electronic device in an embodiment of the present application; Figure 7 It is a schematic diagram of modeling verification in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to have a clearer understanding of the technical features, purposes and effects of the present application, the specific implementation methods of the present application are now described in detail with reference to the accompanying drawings.

[0026] The embodiment of the present application provides a method for inverting the critical shear strength parameters of a landslide in combination with a bionic optimization algorithm.

[0027] Please refer to Figure 1 , Figure 1 It is a step diagram of a method for inverting critical shear strength parameters of a landslide combined with a bionic optimization algorithm in an embodiment of the present application, comprising: 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, the sliding surface model is established to obtain the optimal landslide depth value; S4: The optimal sliding surface is obtained by inversion through 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 the landslide stability model, the optimal shear strength parameters are inverted.

[0028] Specifically, the present application also obtains ALOS_DEM data for removing flat land effects and performing terrain correction.

[0029] Specifically, as a global optimization algorithm, the bionic algorithm has shown great potential in solving complex nonlinear problems. The bionic algorithm simulates the evolution, foraging and cooperation behavior of organisms in nature, searches for the optimal solution in a huge solution space in a group collaborative manner, and can effectively solve the complex nonlinear calculations involved in the inversion problem. The critical shear strength parameter inversion method combines InSAR technology with a variety of bionic optimization algorithms (improved particle swarm optimization algorithm, whale optimization algorithm). This method 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 combines the safety factor landslide stability model constructed by the Morgenstern-Price method to ensure the rationality and accuracy of the inversion results.

[0030] Step S1 includes: acquiring landslide surface deformation data based on Sentinel-1A lift-orbit images; the landslide surface deformation data includes: landslide surface deformation displacement and landslide surface deformation rate.

[0031] The present application provides an embodiment as follows, using the Sentinel-1 satellite SAR data provided by the European Space Agency's Copernicus program (Copernicus), and using SBAS-InSAR technology to obtain the surface deformation displacement and rate of the landslide. In addition, the Radar Topography Mission (SRTM) DEM data is used with a ground resolution of 30m to remove the flat ground effect and perform terrain correction. In order to ensure the reliability of data processing, the present invention selects a short baseline interferometer pair, adaptively determines the coherence coefficient threshold, and performs filtering, phase unwrapping, and removes orbit errors, atmospheric errors, terrain errors, etc. to obtain high-quality slope ascending and descending annual average deformation rate values.

[0032] 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 vertical slope axis OB; Define downward movement along the slope axis as positive, and movement outside the sliding surface along the normal axis as 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:

[0033] Where: 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; Respectively represent the deformation along the OA, OB, and OC directions; and represents the average slope and aspect angle of the landslide; θ and φ represent the incident angle and flight direction angle of the radar satellite; 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: .

[0034] The present application provides an embodiment as follows. Since SAR (Synthetic Aperture Radar) is a side imaging system, the deformation information monitored is the deformation information of the radar line of sight (LOS), which has certain limitations in accurately reflecting the real surface deformation. In order to improve the accuracy of deformation measurement and the interpretation effect, and provide more practical surface deformation information, the present invention is based on the Sentinel-1A ascending and descending orbit images, and the LOS deformation information is two-dimensionally decomposed to obtain deformation information along the slope and perpendicular to the slope, that is, the slope and slope information of the slope body is used to establish a slope coordinate system, and the decomposition is combined with information such as the satellite incident angle azimuth. When the landslide slides due to 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 solve the deformation along the slope and normal.

[0035] Step S3 includes: S31: Assuming that the landslide body is incompressible and the density remains unchanged, the entire landslide body is regarded as a continuum composed of various control bodies. The vertical integral is performed between the base sliding surface and the upper sliding surface of the landslide to construct the sliding surface model as follows:

[0036] in is the control volume depth, represents the average deformation component, , represents the average deformation component along the slope direction, 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 can obtain:

[0037] in represents the normal deformation rate; , Indicates the speed of slope surface movement; represents rheological parameters; S33: According to the central difference and divergence calculation rules, formula (4) is expanded into formula (5):

[0038] in Indicates Line The normal deformation rate of the column; Indicates the data sampling spacing along the slope aspect and perpendicular to the slope aspect; and represents the slope deformation rate and normal deformation rate; Indicates Line The deformation rate of the column along the slope direction; Indicates Line The depth value of the slide surface of the column; 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 to: The inverse calculation is performed to obtain the optimal landslide depth value.

[0039] The present application provides an embodiment as follows: a sliding surface model based on the mass conservation principle is established, and the principle is that there is no mass loss per unit time. For a soil landslide with creep sliding, it can be assumed that the landslide is incompressible and the density remains unchanged. Therefore, it is feasible to apply mass conservation to landslide modeling. The landslide is regarded as a continuum composed of multiple control bodies, and a vertical integral is performed between the base sliding surface and the upper sliding surface to construct a model.

[0040] Step S4 includes: Taking the sliding surface model as the target equation, the particle update rule is designed. The update rule is as follows:

[0041] 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 optimum point; Represents particles arrive The historical best position at the moment; Represents particles exist The sliding surface depth value at the moment; and for Pseudo-random numbers; represents the global optimal position, that is, the optimal sliding surface.

[0042] 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 best position ( ) moves to obtain the optimal solution. The present invention constructs a sliding surface model based on mass conservation as the target equation and designs a particle update rule to better break through the limitations of the local optimal solution and obtain the global optimal solution. This rule improves the traditional particle swarm update rule, reduces the weight ratio, and balances the global search and local search capabilities. and is the learning factor, which represents the ability of particles to learn from themselves and the excellent particles in the group; and move towards the historical optimal point The particles are updated through multiple iterations to get closer to the optimal solution, and finally the global optimal solution is obtained. .

[0043] Step S5 includes: The Morgenstern-Price slope stability analysis method is used to construct a landslide stability model, as follows: The landslide body is divided into n vertical strips, and the width of each strip is ; 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 ; The gravity of each vertical bar is , acting on the center of gravity of the vertical bar; 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 ; Considering pore water pressure Horizontal force between vertical bars , inter-strip shear force ; Assuming that the interaction force between the bars is distributed as a semi-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

[0044] Where R is the moment arm, represents the tangent value of the friction angle within the soil; c represents the cohesion of the soil; 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

[0045] The safety factor SF is obtained by dividing the total anti-slip torque by the total sliding torque and is expressed as .

[0046] The present application provides an embodiment as follows: a landslide stability model is constructed by using the Morgenstern-Price slope stability analysis method. This method assumes that the sliding surface is a curve of arbitrary shape when the slope is destroyed, and divides the sliding body into a number of strips along the sliding surface. The force balance and moment balance of each strip are solved, while the interaction forces (horizontal force and shear force) between the strips are considered. Finally, the safety factor (Safety Factor, SF) of the entire sliding body is obtained through iteration, thereby evaluating the stability of the slope.

[0047] Step S6 includes: Based on the landslide stability model, the objective equation is constructed; By initializing the whale population, 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.

[0048] The present invention utilizes Sentinel-1 SAR data to generate short baseline interferometric pairs, performs short baseline time series processing, and obtains an ascending and descending orbit deformation rate curve. Figure 2 It is the space-time baseline diagram of the interference connection between the ascending and descending orbits. Figure 3 is the average annual deformation rate of the ascending and descending orbit.

[0049] Combined with satellite parameters, the landslide slope and direction decompose the LOS deformation rate of the ascending and descending orbit into deformation along the slope and normal direction in two dimensions. Based on the principle of mass conservation, a sliding surface model is established, and the optimal sliding surface depth is inverted using the improved particle swarm optimization algorithm. Combined with the surface digital elevation model, the optimal sliding surface is obtained. Figure 4 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 Figure 4 (b) Figure 5 is the optimal sliding surface depth after inversion.

[0050] 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, and the whale optimization algorithm was used for inversion to obtain the critical shear strength parameters.

[0051] Table 1 shows the distribution of parameters and safety factors after inversion.

[0052] Table 1 Inversion parameters

[0053] The modeling was carried out using the Slop / W module of Geostudio software in geotechnical engineering to test the reliability of the critical shear strength parameter values ​​(cohesion, internal friction angle) obtained by the method proposed in the present invention. It can be found that the safety factor obtained by modeling in Geostudio is 1.069 ( Figure 7 ), which is close to the safety factor of the method proposed in the present invention, with a difference of only 0.016, indicating that the method proposed in the present invention can effectively invert the critical shear strength parameters of the landslide. The present invention realizes the accurate inversion of the critical shear strength of a large area of ​​slope area by combining remote sensing monitoring with optimization algorithm, avoiding the time-consuming, laborious and high-cost problems of on-site landslide exploration or indoor physical and mechanical tests, and can realize the efficient inversion of the critical shear strength of the landslide in the process of landslide stability analysis, improve the ability to assess landslide risks, and thus provide a new technical path for the prediction and prevention of landslide disasters. It is conducive to the automation and intelligent development of landslide monitoring and early warning.

[0054] The present application also discloses an electronic device. Figure 6 , Figure 6 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 .

[0055] The communication bus 502 is used to realize the connection and communication between these components.

[0056] 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.

[0057] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0058] 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.

[0059] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure.

[0060] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for inverting critical shear strength parameters of landslides 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, the sliding surface model is established to obtain the optimal landslide depth value; S4: The optimal sliding surface is obtained by inversion through 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 the landslide stability model, the optimal shear strength parameters are inverted.

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

3. The method for inversion of critical shear strength parameters of landslide combined with 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 vertical slope axis OB; Define downward movement along the slope axis as positive, and movement outside the sliding surface along the normal axis as 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: 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; Respectively represent the deformation along the OA, OB, and OC directions; and represents the average slope and aspect angle of the landslide; θ and φ represent the incident angle and flight direction angle of the radar satellite; 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 inversion of critical shear strength parameters of landslide combined with bionic optimization algorithm according to claim 1, characterized in that: Step S3 includes: S31: Assuming that the landslide body is incompressible and the density remains unchanged, the entire landslide body is regarded as a continuum composed of various control bodies. The vertical integral is performed between the base sliding surface and the upper sliding surface of the landslide to construct the sliding surface model as follows: in is the control volume depth, represents the average deformation component, , represents the average deformation component along the slope direction, 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 can obtain: in represents the normal deformation rate; , Indicates the movement speed of the slope surface; represents rheological parameters; S33: According to the central difference and divergence calculation rules, formula (4) is expanded into formula (5): in Indicates Line The normal deformation rate of the column; Indicates the data sampling spacing along the slope aspect and perpendicular to the slope aspect; and represents the slope deformation rate and normal deformation rate; Indicates Line The deformation rate of the column along the slope direction; Indicates Line The depth value of the slide surface of the column; 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 to: The inverse calculation is performed to obtain the optimal landslide depth value.

5. The method for inversion of landslide critical shear strength parameters combined with a bionic optimization algorithm as claimed in 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: 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 optimum point; Represents particles arrive The historical best position at the moment; Represents particles exist The sliding surface depth value at the moment; and for Pseudo-random numbers; represents the global optimal position, that is, the optimal sliding surface.

6. The method for inversion of critical shear strength parameters of landslide combined with 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: The landslide body is divided into n vertical strips, and the width of each strip is ; 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 ; The gravity of each vertical bar is , acting on the center of gravity of the vertical bar; 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 ; Considering pore water pressure Horizontal force between vertical bars , inter-strip shear force ; Assuming that the interaction force between the bars is distributed as a semi-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, represents the tangent value of the friction angle within the soil; c represents the cohesion of the soil; 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 The safety factor SF is obtained by dividing the total anti-slip torque by the total sliding torque and is expressed as 。 7. The method for inversion of critical shear strength parameters of landslide combined with 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 population, 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.

8. 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 7.

9. 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 7 is executed.

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