Slope stability data analysis method, analysis system and electronic equipment

Abstract

The present invention discloses a slope stability data analysis method, analysis system, and electronic equipment. The method steps include: obtaining a geometric model of the slope, assigning slope material parameters, and combining acoustic wave statistical information at different depths and regions of the slope site to obtain the distribution characteristics and laws of the slope rock mechanical parameters; performing data processing on the rock mechanical parameters to obtain the corresponding normal distribution probability density function and sinusoidal increasing function, calculating the corresponding rock mechanical parameters at different depths based on the function, and forming a rock mechanical parameter distribution result; using multi-physics field simulation software to perform data processing on the rock mechanical parameter distribution result to obtain the spatial variability distribution of the slope material, and finally performing a slope stability analysis and obtaining a result. The present invention proposes a method for simulating the spatial variability of slope strength and material parameters, and introduces this method into the numerical calculation of 2D and 3D slopes, providing a feasible method and theoretical basis for the stability analysis of complex slopes.

Description

Technical Field

[0001] The present invention relates to a data analysis method, an analysis system and an electronic device, and in particular to a slope stability data analysis method, an analysis system and an electronic device. Background Art

[0002] Rock mechanics research involves a variety of research subjects, such as slope stability. In order to reduce the economic losses, casualties, and other hazards caused by slope instability, accurate slope stability analysis is extremely important. Slopes are complex structures, involving extremely complex mechanical properties and spatial variability in the distribution of materials. Simplified slope analysis often has a significant impact on the results, which greatly inconveniences slope support and the prevention of slope instability. Therefore, to maximize economic benefits and minimize harmful effects, it is necessary to consider the spatial variability of slope geotechnical properties and simulate more realistic mechanical properties of slopes. This is of great significance for the rational and accurate analysis of slope stability. Summary of the Invention

[0003] The purpose of the present invention is to provide a slope stability data analysis method, analysis system and electronic equipment. The technical problem to be solved is to avoid the influence of slope simplified analysis on the slope stability data analysis results, fully consider the spatial variability characteristics of slope rock and soil properties, and provide reasonable and accurate data support for the preliminary processing work of finite element numerical simulation.

[0004] The present invention provides the following solutions:

[0005] A slope stability data analysis method based on Bayesian probability distribution is applied to the occurrence analysis of spatial variability of slope strength and material parameters, including:

[0006] Obtain the geometric model of the slope, assign slope material parameters, and calculate the corresponding rock mass mechanical parameters based on the acoustic wave information at the slope site;

[0007] Performing data processing on the rock mass mechanics parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, and calculating corresponding rock mass mechanics parameters at different depths based on the functions to form rock mass mechanics parameter distribution results;

[0008] The rock mass mechanical parameter distribution results are processed using multi-physics field simulation software to obtain the spatial variability distribution of the slope material, and the slope stability data analysis results are obtained based on the spatial variability distribution of the slope material.

[0009] Furthermore, the rock mass mechanical parameters are specifically rock mass cohesion and internal friction angle;

[0010] The obtaining of the geometric model of the slope further comprises: obtaining the geometric model of the slope according to a GIS remote sensing method, and / or a geological survey method, and / or a simplified model method;

[0011] The calculation is performed in combination with the acoustic wave information at the slope site, specifically: the acoustic wave information is the acoustic wave information extracted from the acoustic wave test at the slope site;

[0012] The rock mass mechanics parameter distribution results specifically include: numerical values ​​of cohesion and internal friction angle at different depths calculated by programming.

[0013] Furthermore, data processing is performed on the rock mass mechanical parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, and the corresponding rock mass mechanical parameters at different depths are calculated according to the functions to form rock mass mechanical parameter distribution results, further comprising:

[0014] The compressive strength and tensile strength of the rock mass are determined by drilling and extracting cores, and the cohesion and internal friction angle are determined by Mohr's circle curve;

[0015] Through acoustic wave testing, the acoustic wave velocity at different depths is extracted, and the changes of cohesion and internal friction angle with depth are mapped;

[0016] According to the normal distribution probability density function and the sinusoidal increasing function, the corresponding values ​​of cohesion and internal friction angle at different depths are statistically analyzed and the corresponding data are derived.

[0017] Furthermore, the acoustic wave test was conducted to extract the acoustic wave velocity at different depths and map the changes in cohesion and internal friction angle with depth, specifically:

[0018] Based on the Hoek-Brown criterion, the changes in cohesion and internal friction angle with depth are mapped to obtain the material parameters and acoustic wave data of the rock mass in the Hoek-Brown criterion. The acoustic wave data includes: the acoustic wave velocity of the rock mass without being affected by the disturbance, and the acoustic wave velocity of the rock mass after being affected by the disturbance;

[0019] Mapping the distribution of cohesion and internal friction angle according to the acoustic wave data to obtain Bayesian probability-sine increasing functions of cohesion and internal friction angle corresponding to different depths;

[0020] Based on the Bayesian probability-sine increasing function, a function is written to calculate the cohesion and internal friction angle at different depths and export the data, where:

[0021] The Bayesian probability-sine increasing function c′ of cohesion is:

[0022]

[0023] In the formula, A, k, b, and c are the fitting coefficients of the single-interval sinusoidal increasing function, x is the variable, σ is the variance, and e is the base of the natural logarithm.

[0024] Furthermore, the method of using multi-physics field simulation software to process the rock mass mechanical parameter distribution results to obtain the spatial variability distribution of slope materials, and obtaining slope stability data analysis results based on the spatial variability distribution of slope materials, further includes:

[0025] The multi-physics simulation software is COMSOL, and interpolation functions corresponding to cohesion and internal friction angle are newly created in the global definition tree in the CMOSOL hierarchy, and data obtained by programming based on the Bayesian probability-sine increasing function is input;

[0026] Create a variable in the model tree component bar. The variable serves as a conditional expression of an if statement, and is used to execute or not execute the corresponding if statement execution body according to the current actual value of the variable.

[0027] Furthermore, a variable is created in the model tree component column. The variable is used as a conditional expression of an if statement to execute or not execute the corresponding if statement execution body according to the current actual value of the variable. Specifically:

[0028] The variables created in the model tree component column include a first variable and a second variable. The first variable corresponds to cohesion, and the second variable corresponds to the internal friction angle. The first and second variables are placed in the conditional expressions of different if statements respectively.

[0029] The if statement execution body part includes the calculation of at least one of the interpolation functions, and different if statement execution body parts are executed according to different judgment results of the if statement conditional expression.

[0030] Furthermore, the method of using multi-physics field simulation software to process the rock mass mechanical parameter distribution results to obtain the spatial variability distribution of slope materials, and obtaining slope stability data analysis results based on the spatial variability distribution of slope materials, further includes:

[0031] Locate the geometry function in the model component bar, obtain the parameterized curve method, establish the slope model based on the parameterized curve method, and obtain a graphical spatial variability distribution coordinate diagram by specifying the stretching length.

[0032] The present invention also provides a slope stability data analysis system based on Bayesian probability distribution, which is used to implement a slope stability data analysis method based on Bayesian probability distribution, including:

[0033] The rock mass mechanics parameter generation and acquisition module obtains the geometric model of the slope, assigns the slope material parameters, and calculates the corresponding rock mass mechanics parameters based on the acoustic wave information at the slope site;

[0034] A rock mass mechanics parameter data processing module processes the rock mass mechanics parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, calculates the corresponding rock mass mechanics parameters at different depths based on the functions, and forms rock mass mechanics parameter distribution results;

[0035] The spatial variability distribution simulation processing module uses multi-physics field simulation software to process the rock mass mechanical parameter distribution results to obtain the spatial variability distribution of the slope material, and obtains the slope stability data analysis results based on the spatial variability distribution of the slope material.

[0036] The present invention also provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method.

[0037] The present invention also provides a computer-readable storage medium storing a computer program executable by an electronic device, wherein when the computer program runs on the electronic device, the electronic device executes the steps of the method.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] The present invention proposes a method for simulating the spatial variability of slope strength and material parameters, and can introduce this method into the numerical calculation of 2D and 3D slopes, avoiding the problem in the existing technology that the analysis results are not accurate and reasonable due to the analysis based on simplified models. It fully considers the spatial variability of the rock and soil properties of the slope, can simulate more realistic mechanical properties, and obtain reasonable and accurate spatial variability distribution characteristics and slope stability data analysis results.

[0040] When analyzing slope stability data, the present invention combines the fitting of actual acoustic wave test data, introduces the Bayesian probability normal distribution, and establishes a Bayesian probability-sine increasing model for the distribution of slope shear strength parameters, which can better reflect the spatial distribution characteristics of the shear strength parameters.

[0041] The present invention uses Python to write probability functions and Bayesian probability-sine increasing functions, and brings in the slope depth for evaluation. The numerical values ​​are then imported into multi-physics field simulation software to write interpolation, establish target parameter variables, and bring in interpolation and setting conditions to form geometric spatial differences. Then, through parameterized curve surface drawing, the geometric distribution of 2D and 3D slope parameters are drawn according to needs, completing the presence of spatial variability of slope strength and material parameters, and graphically displaying the slope stability data analysis results. On the basis of obtaining reasonable and accurate slope stability data analysis results, data visualization and graphics are used to allow people to intuitively feel the key information in the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 It is a flow chart of the slope stability data analysis method.

[0044] Figure 1a It is a flow chart for data processing of rock mechanics parameters.

[0045] Figure 2 This is the architecture diagram of the slope stability data analysis system.

[0046] Figure 3 It is a technical roadmap for implementing the embodiments of the present invention in specific application scenarios.

[0047] Figure 4 It is a curve graph of sound wave changes.

[0048] Figure 5a It is a graph of the cohesion distribution at different depths.

[0049] Figure 5b It is a graph of the distribution of internal friction angle at different depths.

[0050] Figure 6 This is the interface operation diagram for interpolation import in COMSOL simulation software.

[0051] Figure 7 It is the human-computer interaction interface of the cohesion probability distribution.

[0052] Figure 8a It is the effect diagram of the spatial variability distribution of 2D slope materials (spatial distribution of cohesion).

[0053] Figure 8b It is the effect diagram of the spatial variability distribution of 2D slope materials (spatial distribution of internal friction angle).

[0054] Figure 9a It is the effect diagram of the spatial variability distribution of 3D slope materials (spatial distribution of cohesion).

[0055] Figure 9b It is the effect diagram of the spatial variability distribution of 3D slope materials (spatial distribution of internal friction angle).

[0056] Figure 10 It is a structural diagram of an electronic device. DETAILED DESCRIPTION

[0057] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0058] like Figure 1 The slope stability data analysis method based on Bayesian probability distribution is applied to the occurrence analysis of spatial variability of slope strength and material parameters, including:

[0059] Step S1: Obtain the geometric model of the slope, assign the slope material parameters, and calculate the corresponding rock mechanical parameters based on the acoustic wave information at the slope site. In this step, the geometric model of the slope and the material parameters of the slope are obtained to achieve spatial variability in slope strength and material parameters. The acoustic wave information in this step can be extracted through acoustic wave tests at the slope site.

[0060] Step S2, performing data processing on the rock mass mechanical parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, and calculating corresponding rock mass mechanical parameters at different depths according to the functions to form rock mass mechanical parameter distribution results;

[0061] Step S3, using multi-physics field simulation software to process the rock mass mechanical parameter distribution results to obtain the spatial variability distribution of the slope material, and obtain the slope stability data analysis results based on the spatial variability distribution of the slope material.

[0062] Steps S1 to S3 achieve accurate acquisition and analysis of the mechanical parameters of the slope rock mass by comprehensively applying geological engineering, acoustic wave testing technology, and multi-physics field simulation technology, thereby predicting the spatial variability of the slope material and providing a scientific basis for slope stability assessment and design.

[0063] Taking step S1 as an example, this is a key step: Step S1 implements geometric model acquisition and material parameter assignment. High-precision measurement equipment is used to obtain the slope's geometric model, and combined with geological exploration data, the physical and mechanical parameters of the slope material are assigned. Step S1 also utilizes acoustic wave testing technology to extract acoustic wave information from the slope site. This extracted acoustic wave information can reflect the internal structure and mechanical properties of the rock mass, providing a foundation for data processing and function modeling. By processing the acoustic wave information, a normal distribution function and a sinusoidal increasing function of the rock mass mechanical parameters are established, and the rock mass mechanical parameters at different depths are calculated. Finally, multi-physics field simulation software is used to further process and analyze the rock mass mechanical parameters, resulting in the spatial variability distribution of the slope material.

[0064] Steps S1 to S3 obtain the mechanical parameters of the rock mass through the propagation characteristics of sound waves in the rock mass, and use mathematical modeling methods to convert the sound wave information into the distribution function of the rock mass mechanical parameters, that is, normal distribution and sinusoidal increasing function modeling. Then, simulation software is used to simulate the spatial distribution of the rock mass mechanical parameters, and the spatial variability of the slope material is predicted and graphically displayed.

[0065] Steps S1 to S3 not only obtain basic rock mechanical parameters based on traditional geological exploration, but also add acoustic wave extraction technology, overcoming the defect of existing technologies that cannot fully reflect the mechanical properties of the rock mass. The rock mechanical parameters can be obtained more accurately through acoustic wave testing technology, and the distribution characteristics of the rock mechanical parameters can be more comprehensively analyzed through mathematical modeling and multi-physics field simulation technology.

[0066] Steps S1 to S3 are based on the accurate rock mechanical parameters obtained through acoustic wave testing technology, and then through mathematical modeling and spatial variability analysis based on the normal distribution function and the sinusoidal increasing function. This can more scientifically evaluate the stability of the slope, realize dynamic monitoring and analysis of the rock mechanical parameters, help to timely discover the potential risks of the slope, and improve the accuracy and timeliness of early warning.

[0067] It is worth noting that the slope stability data analysis method provided by steps S1 to S3 can be used not only for spatial variability analysis of cohesion and internal friction angle, but also for other rock mechanics parameters. For example, other rock mechanics parameters may include elastic modulus, shear modulus, tensile strength, compressive strength, porosity, density, fracture toughness, etc. Without expending any creative effort, those skilled in the art can make non-substantial modifications to the slope stability data analysis method provided by steps S1 to S3 and perform similar data analysis on the above-mentioned other rock mechanics parameters based on the technical means of steps S1 to S3.

[0068] Preferably, in step S1, the rock mass mechanical parameters are specifically rock mass cohesion and internal friction angle. Exemplarily, in step S1, the rock mass cohesion and internal friction angle may vary with depth.

[0069] In the optimization technical solution of step S1, the obtaining of the geometric model of the slope further comprises: obtaining the geometric model of the slope according to a GIS remote sensing method, and / or a geological survey method, and / or a simplified model method;

[0070] The calculation is performed in combination with the acoustic wave information at the slope site, specifically: the acoustic wave information is the acoustic wave information extracted from the acoustic wave test at the slope site;

[0071] The rock mass mechanics parameter distribution results specifically include: numerical values ​​of cohesion and internal friction angle at different depths calculated by programming.

[0072] Through further analysis of the optimization technical solution of step S1, it can be seen that the optimization technical solution of step S1 involves the acquisition of the geometric model of the slope engineering, the use of acoustic wave information and the calculation of rock mechanical parameters. The geometric model of the slope can be obtained through traditional geological engineering methods, for example: using geographic information system (GIS) technology to obtain the terrain and landform information of the slope through remote sensing images.

[0073] GIS remote sensing method: using high-resolution images obtained from satellites or aerial photography, and generating a three-dimensional geometric model of the slope through image processing technology;

[0074] Geological exploration method: Through geological exploration, such as drilling and sampling, the geological structure of the slope and the physical properties of the rock and soil are obtained to assist in constructing a more accurate slope geometric model.

[0075] Simplified model method: In order to facilitate the calculation process, a simplified geometric model can be used to approximate the actual shape of the slope, which is suitable for preliminary design or rapid evaluation.

[0076] Through the optimization technology solution of step S1, the stability of the slope can be evaluated more comprehensively and a scientific basis can be provided for design and construction.

[0077] like Figure 1a As shown, step S2 can be further optimized: data processing is performed on the rock mass mechanical parameters to obtain the corresponding normal distribution probability density function and sinusoidal increasing function, and the corresponding rock mass mechanical parameters at different depths are calculated according to the function to form the rock mass mechanical parameter distribution result, further including:

[0078] Step S21, extracting rock cores by drilling to determine the compressive strength and tensile strength of the rock mass, and determining the cohesion and internal friction angle using the Mohr circle curve based on the Mohr Coulomb criterion;

[0079] Step S22, extracting the acoustic wave velocity at different depths through acoustic wave testing, and mapping the changes of cohesion and internal friction angle with depth based on the Hoek-Brown criterion;

[0080] Step S23 , performing statistics on the corresponding values ​​of cohesion and internal friction angle at different depths according to the normal distribution probability density function and the sinusoidal increasing function, and deriving corresponding data.

[0081] Steps S21 to S23 use different technical means to determine the rock mass mechanical parameters (cohesion and internal friction angle) and analyze their changes at different depths. Steps S21, S22, and S23 are three key steps in slope engineering stability analysis. Each of these steps uses different technical means to determine the rock mass mechanical parameters and analyze their changes at different depths. The following is a summary and analysis of these steps:

[0082] Step S21 involves obtaining data through core drilling. This process directly measures the physical and mechanical properties of the rock mass. Core samples are obtained in step S21, and laboratory testing is then used to determine the rock mass's compressive and tensile strengths. Step S21 applies the Mohr-Coulomb criterion. By analyzing the Mohr circle curve and combining it with the rock mass's mechanical properties, the cohesion and internal friction angle can be determined to a certain extent, yielding the rock mass's basic mechanical properties. This provides accurate baseline data for subsequent analysis and provides the original foundational data for slope stability analysis.

[0083] Step S22 is a step of an acoustic wave test. Step S22 utilizes the propagation characteristics of acoustic waves in the rock mass to indirectly evaluate the mechanical properties of the rock mass, and reflects the density and uniformity of the rock mass by measuring the acoustic wave velocity.

[0084] Step S22 applies the Hoek-Brown criterion, which links the acoustic wave velocity with the cohesion and internal friction angle of the rock mass, mapping the mechanical parameters that vary with depth. Without destroying the rock mass, it can quickly evaluate the mechanical properties of the rock mass at different depths, and perform dynamic monitoring and stability analysis of the slope without destroying the rock mass.

[0085] Step S23 performs probability function modeling and statistical analysis. By performing statistical analysis on the cohesion and internal friction angle at different depths, more accurate parameter values ​​can be derived. Based on the above parameter values, the normal distribution probability density function and the sinusoidal increasing function are used to perform statistics and fit the data.

[0086] Step S23 combines probability statistics, mathematical models and traditional rock mass data measurement to improve the accuracy and reliability of parameter estimation, and reduces random errors by processing and analyzing a large amount of data, thereby improving the stability of parameter estimation. Finally, the data is fitted through a mathematical model to obtain a smoother and more reliable trend of rock mass mechanical parameter changes.

[0087] Steps S21 to S23 comprehensively utilize technical means such as geological exploration, acoustic wave testing, probability statistics, and function modeling to comprehensively evaluate the mechanical properties of the slope rock mass, thereby ensuring the stability and safety of the slope engineering. Non-destructive testing is achieved by directly measuring the mechanical properties of the rock mass by obtaining rock samples and indirectly evaluating the mechanical properties of the rock mass through acoustic wave testing. The test data is then deeply processed to improve the accuracy of parameter estimation, thereby realizing a comprehensive, multi-dimensional slope stability analysis method that can more accurately predict the stability of the slope and provide a scientific basis for engineering design and construction.

[0088] For example, in steps S21 to S23, the acoustic wave test is performed to extract the acoustic wave velocity at different depths and map the changes of cohesion and internal friction angle with depth, specifically:

[0089] Based on the Hoek-Brown criterion, the variations of cohesion and internal friction angle with depth are mapped, and the material parameters and acoustic wave data of the rock mass in the Hoek-Brown criterion are obtained. The acoustic wave data include the acoustic wave velocity of the rock mass without disturbance and the acoustic wave velocity of the rock mass after disturbance.

[0090] Mapping the distribution of cohesion and internal friction angle according to the acoustic wave data to obtain Bayesian probability-sine increasing functions of cohesion and internal friction angle corresponding to different depths;

[0091] Programming is performed based on the Bayesian probability-sine increasing function, and a function is written to calculate the cohesion and internal friction angle at different depths and export the data.

[0092] Preferably, in step S3, the multi-physics field simulation software is used to perform data processing on the rock mass mechanical parameter distribution results to obtain the spatial variability distribution of the slope material, and the slope stability data analysis results are obtained according to the spatial variability distribution of the slope material, further comprising:

[0093] The multi-physics simulation software is COMSOL. Interpolation functions corresponding to cohesion and internal friction angle are newly created in the global definition tree in the CMOSOL hierarchy, and data obtained by programming based on the Bayesian probability-sine increasing function is input.

[0094] Create a variable in the model tree component bar of the COMSOL software. The variable serves as a conditional expression of an if statement, and is used to execute or not execute the corresponding if statement execution body according to the current actual value of the variable.

[0095] COMSOL, or COMSOL Multiphysics, is an advanced multiphysics simulation software widely used in scientific research and engineering computing. It can simulate and simulate various physical phenomena, especially multiphysics coupling problems. The core advantage of COMSOL software lies in its ability to solve partial differential equations (PDEs), which are suitable for mathematical models that describe various physical phenomena.

[0096] COMSOL software offers a wide range of physics applications, covering a variety of physical fields, including fluid flow, heat conduction, structural mechanics, and electromagnetic analysis. Users can quickly build models and flexibly define the model's material properties, source terms, and boundary conditions. Based on the finite element method, COMSOL software simulates real physical phenomena by solving partial differential equations in single or multiple fields.

[0097] It is worth noting that, in the embodiments of the present application, COMSOL software is used for multi-physics simulation processing, but this does not mean that only COMSOL software can be used for processing, that is, the technical means for performing multi-physics simulation processing are not limited to COMSOL. Those skilled in the art, relying on their common technical knowledge in this field, can use a variety of multi-physics simulation software similar to COMSOL as tools for processing, exemplified by: finite element analysis software ANSYS, finite element analysis software Abaqus, simulation tool Altair, Dassault Systemes for 3D simulation and material testing, MSC Software for structural analysis and fatigue analysis, etc., which will not be described in detail due to space limitations.

[0098] In the optimization technology solution of step S3, the advanced simulation software COMSOL is used, combined with Bayesian probability and sinusoidal increasing functions, and Python programming is adopted to conduct precise spatial variability analysis of the rock mechanical parameters of the slope, so as to achieve an in-depth understanding and prediction of the slope material properties.

[0099] The optimization technology solution of step S3 uses COMSOL software to simulate and analyze rock mechanical parameters, establishes a Bayesian probability-sine increasing function through Python programming language, and combines probability statistics and mathematical models to program and process rock mechanical parameters.

[0100] Interpolation functions can be defined in COMSOL to simulate the spatial distribution of cohesion and internal friction angle. Interpolation functions can be used to smooth and approximate actual measured data for use in simulation models.

[0101] For more accurate data analysis, variables are created in the COMSOL model tree component, and if statements are used to execute or skip specific calculation steps based on the actual values ​​of the variables. Utilizing COMSOL's powerful multi-physics simulation capabilities, complex rock mechanics simulations are performed, and the conditional probability Bayesian method is used to quantify the uncertain raw data to improve the accuracy of parameter estimation.

[0102] The optimization technology solution of step S3 uses conditional probability (Bayesian method), sinusoidal increasing function and interpolation technology to enable the simulation model to more accurately reflect the distribution of actual rock mechanical parameters, thereby improving the accuracy of the simulation. By using the conditional expression of the if statement in the COMSOL simulation software, the model can dynamically adjust the calculation process according to real-time data, thereby improving the flexibility and adaptability of the model.

[0103] Step S3 optimizes the geological scheme design scheme and improves the feasibility and reliability of geological construction decisions. Through the data analysis and simulation module of the spatial variability of slope materials, it provides a more scientific basis for engineering design and construction, helps to improve the safety and economy of slope engineering, effectively solves the simulation problem of the spatial variability of slope rock mechanical parameters, and provides strong technical support for the stability analysis and design of slope engineering.

[0104] Exemplarily, a variable is created in the model tree component column. The variable is used as a conditional expression of an if statement, and is used to execute or not execute the corresponding if statement execution body according to the current actual value of the variable, specifically:

[0105] The variables created in the model tree component column include a first variable and a second variable. The first variable corresponds to cohesion, and the second variable corresponds to the internal friction angle. The first and second variables are placed in the conditional expressions of different if statements respectively.

[0106] The if statement execution body part includes the calculation of at least one of the interpolation functions, and different if statement execution body parts are executed according to different judgment results of the if statement conditional expression.

[0107] Preferably, in step S3, the multi-physics field simulation software is used to perform data processing on the rock mass mechanical parameter distribution results to obtain the spatial variability distribution of the slope material, and the slope stability data analysis results are obtained according to the spatial variability distribution of the slope material, further comprising:

[0108] Locate the geometry function in the model component bar, obtain the parameterized curve method, establish the slope model based on the parameterized curve method, and obtain a graphical spatial variability distribution coordinate diagram by specifying the stretching length.

[0109] For the method steps disclosed in the above embodiments, for the purpose of simple description, the method steps are expressed as a series of action combinations. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0110] Any process or method description described in a flowchart or other manner can be understood as: a module, fragment or part of a code that includes one or more executable instructions for implementing a specific logical function or process step, and the scope of the preferred embodiment of the present invention includes alternative implementations, in which the order shown or discussed may not be followed, including executing and implementing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved, or executing computer instructions and implementing the corresponding functions according to program structures such as loops and branches, which can naturally be understood by those skilled in the art when implementing the embodiments of the present invention.

[0111] like Figure 2 The slope stability data analysis system based on Bayesian probability distribution shown in the figure is applied to implement the slope stability data analysis method based on Bayesian probability distribution. The system includes:

[0112] The rock mass mechanics parameter generation and acquisition module obtains the geometric model of the slope, assigns the slope material parameters, and calculates the corresponding rock mass mechanics parameters based on the acoustic wave information at the slope site;

[0113] A rock mass mechanics parameter data processing module processes the rock mass mechanics parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, calculates the corresponding rock mass mechanics parameters at different depths based on the functions, and forms rock mass mechanics parameter distribution results;

[0114] The spatial variability distribution simulation processing module uses multi-physics field simulation software to process the rock mass mechanical parameter distribution results to obtain the spatial variability distribution of the slope material, and obtains the slope stability data analysis results based on the spatial variability distribution of the slope material.

[0115] The above-described system implementations are merely illustrative. For example, the various functional modules, units, or subsystems in the system may or may not be physically separate, or may or may not be physical units. They may be located in the same location or distributed across multiple different systems and their subsystems or modules. Those skilled in the art may select some or all of the functional modules, units, or subsystems to achieve the objectives of the embodiments of the present invention based on actual needs. In these cases, those of ordinary skill in the art can understand and implement them without inventive effort.

[0116] like Figure 3 The technical roadmap of the embodiment of the present invention in a specific application scenario is shown. This embodiment proposes a method for simulating the spatial variability of slope strength and material parameters, and introduces it into the numerical calculation of 2D and 3D slopes, providing a feasible method and theoretical basis for the stability analysis of complex slopes. This embodiment combines the fitting of actual acoustic wave test data, introduces the Bayesian probability normal distribution, and proposes a Bayesian probability-sine increasing model for the distribution of slope shear strength parameters. This model can better reflect the spatial distribution characteristics of shear strength parameters. This embodiment is completed with the help of Python language and COMSOL's parameterized curve surface geometry generation. The probability function and Bayesian probability-sine increasing function are written in Python in advance, and the slope depth is brought in for evaluation. The numerical value is then imported into COMSOL to write interpolation, establish the target parameter variable, and bring in the interpolation and setting conditions to form geometric spatial differences. Then, through the parameterized curve surface drawing method, the geometric distribution of 2D and 3D slope parameters are drawn according to needs, completing the distribution of slope strength and material parameter spatial variability. This embodiment provides a feasible method and theoretical basis for the spatial variability of geotechnical properties in slope stability analysis, and has important reference value and significance for the pre-processing of finite element numerical simulation.

[0117] It is worth noting that although Python is used to write the normal distribution probability function and the Bayesian probability-sine increasing function in this embodiment, this does not mean that the programming language for writing the functions is limited to Python. In fact, those skilled in the art, with their ordinary technical knowledge in this field, can use a variety of programming languages ​​to implement the normal distribution probability function and the Bayesian probability-sine increasing function, such as C#, R language, C language or C++, JAVA, JavaScript, TypeScript, Perl, PHP, etc. Due to space limitations, this will not be repeated here.

[0118] like Figure 4As shown, in this embodiment, the geometric model of the slope can be obtained based on GIS remote sensing technology and geological survey data, or a simplified geometric model can be directly used. After the geometric model of the slope is obtained, the slope material parameters are assigned to realize the method of storing the spatial variability of the slope strength and material parameters. The specific steps are as follows:

[0119] The material parameters and rock properties of the slope are determined through outdoor and indoor experiments. First, the rock core can be extracted through drilling, and then the compressive strength and tensile strength of the rock mass can be determined by triaxial compression test and Brazilian splitting method. Based on the Mohr Coulomb criterion, the cohesion and internal friction angle are determined by the Mohr circle curve. Secondly, the sonic wave velocity at different depths is extracted through sonic wave testing. Based on the Hoek-Brown criterion, the cohesion and internal friction angle are mapped with depth to determine m. b , s, a, first need to satisfy the following formula:

[0120]

[0121] Where m b , s, a are the material parameters of the rock mass involved in the Heok-Brown criterion, C p and C p ′ refer to the acoustic wave velocity of the rock mass without disturbance and the acoustic wave velocity of the rock mass after disturbance, respectively.

[0122] Determine m b After , s, and a are obtained, the cohesion and internal friction angle can be calculated using the nonlinear strength criterion linearization method, which specifically satisfies the following formula:

[0123]

[0124] where c is the cohesion, φ is the internal friction angle, σ 3n =σ 3max / σ c ,and Where γ represents the bulk density of the rock mass, H s is the height of the relevant slope.

[0125] Step 2: Map the distribution of cohesion and internal friction angle based on the on-site acoustic wave data, then perform statistics on the corresponding values ​​at each depth and meet certain normal distribution requirements. The variance σ and expected value u are determined based on the measured values. Taking cohesion as an example, the specific normal distribution probability function is:

[0126]

[0127] u is the expected cohesion at the corresponding depth of the acoustic wave variation curve. According to the curve of the acoustic wave variation with depth, it can be seen that it satisfies a single-interval sinusoidal distribution.

[0128] Therefore, the change of the cohesion expectation u corresponding to different depths satisfies:

[0129] u=A·sin(k(x+b))+c………(8)

[0130] The Bayesian probability-sine increasing function of cohesion is:

[0131] c′=P(C)·(A·sin(k(x+b))+c)………(9)

[0132]

[0133] Where the parameters A, k, b, and c are the fitting coefficients of the single-interval sinusoidal increasing function, x is the variable, σ is the variance, and e is the base of the natural logarithm.

[0134] like Figure 5a and Figure 5b Step 3 shown: After determining the Bayesian probability-sine increasing function of cohesion and internal friction angle respectively, programming is performed in Python to write functions to calculate cohesion and internal friction angle at different depths and export the data.

[0135] In order to further understand the functions written in Python, the pseudo code is given as follows:

[0136] Import related libraries, such as numpy, pandas, and matplotlib;

[0137] Write a sine function, naming all variables;

[0138] Import normal distribution function;

[0139] Write a Bayesian probability-sine increasing function and write a for loop to traverse it;

[0140] Convert the expected cohesion and internal friction angle at the corresponding depth into a sine function, output the result, and merge it into the matrix list;

[0141] Finally, the function is called to output the cohesion and internal friction angle corresponding to all depths.

[0142] By reading the Python pseudocode, you can more deeply understand the significance of writing functions in Python to calculate the cohesion and internal friction angle at different depths.

[0143] like Figure 6 As shown in step 4, create two new interpolation functions in the COMSOL global definition tree, corresponding to the interpolation functions of cohesion and internal friction angle respectively, and then import the data output by Python. Taking the slope case as an example, the import results are as follows: Figure 6 shown.

[0144] Before importing the interpolation, create two variables in the model tree component bar, corresponding to cohesion and internal friction angle. The condition of the internal friction angle is set as follows:

[0145] if((35-y)<15,int 3(35-y),65e3)

[0146] like Figure 7 As shown in the figure, the global material distribution of the slope is set using COMSOL simulation software, and variable conditions are set. In this example, int3 is the established interpolation function, and the conditional settings for the cohesion probability distribution include: if(x<20,int3(27-y))...

[0147] like Figure 8a 、 8b and Figure 9a 、 9b As shown, the operation is performed in the COMSOL simulation software, the geometry function is located in the model power component bar, and the parametric curve method is found. For 3D slopes, it is necessary to first establish a working surface, find the parametric curve method on the working surface, establish a slope model, and then specify the stretching length. Finally, the effect diagram of the spatial variability distribution of 2D slope materials used to represent the spatial distribution of cohesion and the spatial distribution of internal friction angle, as well as the effect diagram of the spatial variability distribution of 3D slope materials used to represent the spatial distribution of cohesion and the spatial distribution of internal friction angle can be obtained.

[0148] like Figure 10 As shown, the present invention not only provides a slope stability data analysis method and analysis system, but also provides corresponding electronic equipment and storage media:

[0149] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of a slope stability data analysis method.

[0150] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of a slope stability data analysis method.

[0151] Figure 10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is provided. Figure 10 A block diagram is shown of an exemplary electronic device suitable for implementing exemplary embodiments of the present invention. Figure 10The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. The electronic device can typically be a device in an electronic product based on the slope stability data analysis method in the above embodiment.

[0152] like Figure 10As shown, electronic device 500 is implemented as a general-purpose computing device. Components of electronic device 500 may include, but are not limited to, one or more processing units or processors 516, memory 528, and a bus 518 connecting various system components (including memory 528 and processor 516). Bus 518 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus. Electronic device 500 typically includes a variety of computer-readable media. These media can be any available media accessible by electronic device 500, including volatile and non-volatile media, removable and non-removable media. Memory 528 may include computer-readable media in the form of volatile memory, such as random access memory (RAM) 530 and / or cache memory 532. The electronic device 500 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 534 may be used to read and write non-removable, non-volatile magnetic media (not shown in the figure, commonly referred to as a "hard drive"). Although not shown in the figure, the storage system 534 may provide a disk drive for reading and writing removable non-volatile disks (e.g., floppy disks, removable hard disks, hot-swappable storage media), as well as an optical drive for reading and writing removable non-volatile optical disks (e.g., CD-ROMs, DVD-ROMs, or other optical media). In these cases, each drive may be connected to the bus 518 via one or more data medium interfaces. The memory 528 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention. A program / utility 540 having a set (at least one) of program modules 542 may be stored in, for example, memory 528, such program modules 542 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. The program modules 542 typically perform the functions and / or methods described in the embodiments of the present invention. The electronic device 500 may also communicate with one or more external devices 514 (e.g., a keyboard, a pointing device, a display 524, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.).Such communication can be performed through input / output (I / O) interface 522. In addition, electronic device 500 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through network adapter 520. Network adapter 520 communicates with other modules of electronic device 500 through bus 518.

[0153] It should be understood that, although not shown in the figures, those skilled in the art may use other hardware and / or software modules in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems. The processor 516 executes various functional applications and data processing by running programs stored in the memory 528, such as implementing the methods provided by any one or more embodiments of the present invention.

[0154] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0155] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features that are included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, any one of the embodiments claimed in the claims may be used in any combination in the embodiments of the present invention.

[0156] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0157] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0158] All features disclosed in this specification, or steps in all methods or processes disclosed, except for mutually exclusive features and / or steps, may be combined in any manner. Any feature disclosed in this specification, unless otherwise stated, may be replaced by an alternative feature that is equivalent or serves a similar purpose. That is, unless otherwise stated, each feature is merely an example of a set of equivalent or similar features. Throughout this specification, like reference numerals indicate like elements.

[0159] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including corresponding claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including corresponding claims, abstracts and drawings) can be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A slope stability data analysis method based on Bayesian probability distribution, applied to the occurrence analysis of slope strength and spatial variability of material parameters, characterized by: include: Obtain the geometric model of the slope, assign slope material parameters, and calculate the corresponding rock mass mechanical parameters based on the acoustic wave information at the slope site; Performing data processing on the rock mass mechanics parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, and calculating corresponding rock mass mechanics parameters at different depths based on the functions to form rock mass mechanics parameter distribution results; Using multi-physics field simulation software to process the rock mass mechanical parameter distribution results, obtain the spatial variability distribution of the slope material, and obtain the slope stability data analysis results based on the spatial variability distribution of the slope material; Data processing is performed on the rock mass mechanical parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, and the corresponding rock mass mechanical parameters at different depths are calculated according to the functions to form rock mass mechanical parameter distribution results, further comprising: The compressive strength and tensile strength of the rock mass are determined by drilling and extracting cores, and the cohesion and internal friction angle are determined by Mohr's circle curve; Through acoustic wave testing, the acoustic wave velocity at different depths is extracted, and the changes of cohesion and internal friction angle with depth are mapped; According to the normal distribution probability density function and the sinusoidal increasing function, the corresponding values ​​of cohesion and internal friction angle at different depths are statistically analyzed and the corresponding data are derived. The acoustic wave test was conducted to extract the acoustic wave velocity at different depths and map the changes in cohesion and internal friction angle with depth, specifically: Based on the Hoek-Brown criterion, the changes in cohesion and internal friction angle with depth are mapped to obtain the material parameters and acoustic wave data of the rock mass in the Hoek-Brown criterion. The acoustic wave data includes: the acoustic wave velocity of the rock mass without being affected by the disturbance, and the acoustic wave velocity of the rock mass after being affected by the disturbance; Mapping the distribution of cohesion and internal friction angle according to the acoustic wave data to obtain Bayesian probability-sine increasing functions of cohesion and internal friction angle corresponding to different depths; Based on the Bayesian probability-sine increasing function, a function is written to calculate the cohesion and internal friction angle at different depths and export the data, where: Bayesian probability of cohesion - sinusoidally increasing function for: ； In the formula, A, k, b, c is the fitting coefficient of the single-interval sinusoidal increasing function, x is a variable, σ is the variance, and e is the base of the natural logarithm.

2. The slope stability data analysis method based on Bayesian probability distribution according to claim 1 is characterized in that: The rock mass mechanical parameters are specifically rock mass cohesion and internal friction angle; The obtaining of the geometric model of the slope further comprises: obtaining the geometric model of the slope according to a GIS remote sensing method, and / or a geological survey method, and / or a simplified model method; The calculation is performed in combination with the acoustic wave information at the slope site, specifically: the acoustic wave information is the acoustic wave information extracted from the acoustic wave test at the slope site; The rock mass mechanics parameter distribution results specifically include: numerical values ​​of cohesion and internal friction angle at different depths calculated by programming.

3. The slope stability data analysis method based on Bayesian probability distribution according to claim 1 is characterized in that: The method further comprises: performing data processing on the rock mass mechanical parameter distribution results using multi-physics field simulation software to obtain the spatial variability distribution of slope materials, and obtaining slope stability data analysis results based on the spatial variability distribution of slope materials. The multi-physics simulation software is COMSOL, and interpolation functions corresponding to cohesion and internal friction angle are newly created in the global definition tree in the CMOSOL hierarchy, and data obtained by programming based on the Bayesian probability-sine increasing function is input; Create a variable in the model tree component bar. The variable serves as a conditional expression of an if statement, and is used to execute or not execute the corresponding if statement execution body according to the current actual value of the variable.

4. The slope stability data analysis method based on Bayesian probability distribution according to claim 3 is characterized in that: Create a variable in the model tree component bar. The variable is used as the conditional expression of the if statement to execute or not execute the corresponding if statement execution body according to the current actual value of the variable. Specifically: The variables created in the model tree component column include a first variable and a second variable. The first variable corresponds to cohesion, and the second variable corresponds to the internal friction angle. The first and second variables are placed in the conditional expressions of different if statements respectively. The if statement execution body part includes the calculation of at least one of the interpolation functions, and different if statement execution body parts are executed according to different judgment results of the if statement conditional expression.

5. The slope stability data analysis method based on Bayesian probability distribution according to claim 1 is characterized in that: The method further comprises: performing data processing on the rock mass mechanical parameter distribution results using multi-physics field simulation software to obtain the spatial variability distribution of slope materials, and obtaining slope stability data analysis results based on the spatial variability distribution of slope materials. Locate the geometry function in the model component bar, obtain the parameterized curve method, establish the slope model based on the parameterized curve method, and obtain a graphical spatial variability distribution coordinate diagram by specifying the stretching length.

6. A slope stability data analysis system based on Bayesian probability distribution, used to implement a slope stability data analysis method based on Bayesian probability distribution, characterized in that: include: The rock mass mechanics parameter generation and acquisition module obtains the geometric model of the slope, assigns the slope material parameters, and calculates the corresponding rock mass mechanics parameters based on the acoustic wave information at the slope site; A rock mass mechanics parameter data processing module processes the rock mass mechanics parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, calculates the corresponding rock mass mechanics parameters at different depths based on the functions, and forms rock mass mechanics parameter distribution results; A spatial variability distribution simulation processing module uses multi-physics field simulation software to process the rock mass mechanical parameter distribution results to obtain the spatial variability distribution of the slope material, and obtains the slope stability data analysis results based on the spatial variability distribution of the slope material; Data processing is performed on the rock mass mechanical parameters to obtain corresponding normal distribution probability density functions and sinusoidal increasing functions, and the corresponding rock mass mechanical parameters at different depths are calculated according to the functions to form rock mass mechanical parameter distribution results, further comprising: The compressive strength and tensile strength of the rock mass are determined by drilling and extracting cores, and the cohesion and internal friction angle are determined by Mohr's circle curve; Through acoustic wave testing, the acoustic wave velocity at different depths is extracted, and the changes of cohesion and internal friction angle with depth are mapped; According to the normal distribution probability density function and the sinusoidal increasing function, the corresponding values ​​of cohesion and internal friction angle at different depths are statistically analyzed and the corresponding data are derived. The acoustic wave test was conducted to extract the acoustic wave velocity at different depths and map the changes in cohesion and internal friction angle with depth, specifically: Based on the Hoek-Brown criterion, the changes in cohesion and internal friction angle with depth are mapped to obtain the material parameters and acoustic wave data of the rock mass in the Hoek-Brown criterion. The acoustic wave data includes: the acoustic wave velocity of the rock mass without being affected by the disturbance, and the acoustic wave velocity of the rock mass after being affected by the disturbance; Mapping the distribution of cohesion and internal friction angle according to the acoustic wave data to obtain Bayesian probability-sine increasing functions of cohesion and internal friction angle corresponding to different depths; Based on the Bayesian probability-sine increasing function, a function is written to calculate the cohesion and internal friction angle at different depths and export the data, where: Bayesian probability of cohesion - sinusoidally increasing function for: ； In the formula, A, k, b, c is the fitting coefficient of the single-interval sinusoidal increasing function, x is a variable, σ is the variance, and e is the base of the natural logarithm.

7. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that It stores a computer program that can be executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method according to any one of claims 1 to 5.

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