A method for inversion of slope rock and soil parameters based on time series correlation analysis

Abstract

The present invention discloses a slope rock and soil parameter inversion method based on time series correlation analysis, which belongs to the field of civil engineering operation and maintenance technology. The present invention fully considers the time series changes and spatial correlation of slope rock and soil material parameters, and improves the accuracy of slope rock and soil parameter inversion. By combining field monitoring data with geological exploration information, a forward algorithm is used to construct the solution space of rock and soil parameters, and a multi-objective optimization algorithm (SDMOM) is used to efficiently optimize the solution space to ensure the temporal and spatial consistency and rationality of material parameters. A meta-heuristic algorithm is used to quickly solve the parameter inversion problem, which significantly improves the calculation efficiency, reduces the calculation time, and further reduces the impact of the perception system error on the displacement inversion.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering operation and maintenance, and in particular to a slope rock and soil parameter inversion method based on time series correlation analysis. Background Art

[0002] The physical and mechanical parameters of slope rock and soil are fundamental to slope stability and structural analysis. Currently, these parameters are often obtained through geological surveys. However, in practical engineering projects, geological surveys are typically conducted during the design phase, and the geological parameters obtained span the design, construction, and operation phases. However, as structures directly exposed to the natural environment, slopes undergo various physical and chemical reactions during their service life, and the physical and mechanical parameters of the rock and soil vary dynamically. In particular, atmospheric precipitation and groundwater have been shown to significantly alter the physical and mechanical parameters of the soil. Therefore, to accurately analyze the stability and service performance of slopes during operation, it is necessary to determine how these internal slope parameters change over time. Traditional geological surveys can only obtain rock and soil parameters from a few sparse measurement points within the slope area. Furthermore, due to high labor and economic costs and the destructive nature of the slope, long-term monitoring is difficult to implement in practical engineering projects.

[0003] Geotechnical parameter inversion has important theoretical and practical significance in geotechnical engineering. Its core lies in estimating the physical and mechanical parameters of the slope rock and soil, such as internal friction angle and cohesion, based on on-site slope monitoring data or test data through specific inversion methods. Inversion technology updates the parameters by combining observation data with numerical simulation, which can effectively narrow the gap between the original geological survey parameters and the actual engineering conditions, thereby providing a reliable basis for slope safety operation and risk assessment. Although geotechnical parameter inversion has broad application prospects in slope stability analysis, the inversion problem is essentially an ill-posed problem, that is, there is a problem of non-uniqueness of the solution and sensitivity to input data. Different observation data may lead to different inversion results, increasing the uncertainty of the model parameters.

[0004] To address these challenges, researchers have recently begun combining advanced optimization algorithms with machine learning methods to improve the accuracy and efficiency of the inversion process. For example, intelligent algorithms such as genetic algorithms and particle swarm optimization have been initially applied to geotechnical parameter inversion. These methods optimize inversion results through global search, reducing the influence of local extreme values. While machine learning-based inversion methods have improved inversion efficiency to a certain extent, determining the solution space for ill-posed inversion problems remains a key issue hindering high-precision inversion of soil parameters. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a slope rock and soil parameter inversion method based on time series correlation analysis.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0007] A slope rock and soil parameter inversion method based on time series correlation analysis includes the following steps:

[0008] S1. Obtain monitoring / detection data of slope rock and soil parameters at different times, and perform data cleaning on the acquired monitoring / detection data;

[0009] S2. Using the positive algorithm to obtain the slope rock and soil parameter solution space that meets the requirements for the monitoring / detection data after data cleaning;

[0010] S3. Based on the slope rock and soil parameter solution space obtained in S2, a multi-objective optimization algorithm model considering the spatial correlation of structured data is established;

[0011] S4. Use multi-objective optimization algorithm to solve the spatial time series and obtain the inversion parameters of the slope rock and soil at different times.

[0012] Further, the following steps are included:

[0013] S11, sorting the monitoring / detection data of the slope rock and soil parameters obtained at different times by time series, deleting the null values ​​in the series and normalizing the data;

[0014] S12, establishing an additive model to decompose the ordered time series in S11 and extracting the residuals of the monitoring / detection data of the slope rock and soil parameters at different times;

[0015] S13, the residuals at different times are combined into a residual sequence and the size of each data in the residual sequence is compared with the average of the sequence. If the data meets the preset conditions, it is considered as abnormal data;

[0016] S14. Eliminate abnormal data to obtain a cleaned time series.

[0017] Furthermore, the additive model decomposition in S12 is expressed as:

[0018]

[0019] Where Y(t) represents the actual observation value at time t, represents the trend item, represents the periodic term, R(t) represents the residual term at time t, and a i 、b i 、c i d i and e iare the i-th learnable parameters, i is an integer greater than or equal to 0, n is the number of fitting items and satisfies:

[0020]

[0021] Where R(t) n Represents the residual at time t when set to n.

[0022] Furthermore, the S3 specifically includes the following steps:

[0023] S31. Construct inversion parameters according to the spatial position of the slope, with rows representing horizontal directions in geological surveys and columns representing vertical directions in geological surveys, to form a two-dimensional matrix g, where positions not belonging to the slope body are replaced by the number 0;

[0024] S32, taking the slope rock and soil parameter solution space obtained in S2 as the optimization domain, uniformly randomly sampling sequence numbers in each subset of the optimization domain to obtain a set of solutions, and repeating multiple times to obtain the initial solution set;

[0025] S33, calculating the correlation between the soil material parameter matrix at time t and time t+1 in each set of solutions in the initial solution set based on the correlation algorithm;

[0026] S34, using the L1 norm to represent the inverted monitoring error, and calculating the fitness function in combination with the correlation obtained in S33;

[0027] S35. Using the meta-heuristic algorithm, taking the initial solution set as the initial population, and combining it with the fitness function, a multi-objective optimization algorithm that considers the spatial correlation of structured data is established.

[0028] Furthermore, the specific calculation method of the correlation in S33 is:

[0029]

[0030] Where C(t) is the correlation between geotechnical parameters at time t and t+1. The smaller the value, the better the correlation. t,i,j At time t, the matrix index is the value at position i and j, im is the maximum index value in the i direction, jm is the maximum index value in the j direction, and smooth is the minimum value set to ensure that the formula makes sense.

[0031] Furthermore, the specific calculation method of the fitness function in S34 is:

[0032]

[0033] Where, is the value of the monitoring point obtained by inversion parameter calculation, S tThe value obtained after cleaning the monitoring point, smooth is the minimum value set to ensure the formula is meaningful, C(t) is the material correlation, P(G i ) is G i The fitness of the group solution.

[0034] Furthermore, the meta-heuristic algorithm in S35 is specifically expressed as:

[0035]

[0036] Where, represents the flying speed of particle i in the dth dimension in the kth iteration, represents the position of particle i in the dth dimension in the kth iteration, c1 and c2 are acceleration constants, r1 and r2 are random numbers, and w is the inertia weight.

[0037] The present invention has the following beneficial effects:

[0038] The beneficial effects of the present invention are as follows: Different from the traditional slope rock and soil parameter inversion method, the present invention proposes a sequential inversion method based on meta-inspiration analysis, which fully considers the time series changes and spatial correlation of slope rock and soil material parameters, and improves the accuracy of slope rock and soil parameter inversion. By combining field monitoring data with geological exploration information, a forward algorithm is used to construct the solution space of rock and soil parameters, and a multi-objective optimization algorithm (SDMOM) is used to efficiently optimize the solution space to ensure the temporal and spatial consistency and rationality of material parameters. The meta-inspiration algorithm is used to quickly solve the parameter inversion problem, which significantly improves the calculation efficiency and reduces the calculation time. In addition, the present invention carries out inversion by monitoring displacement and designs a monitoring data cleaning algorithm to further reduce the impact of the perception system error on the displacement inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Flow chart of the slope rock and soil parameter inversion method for time series correlation analysis of the present invention;

[0040] Figure 2 This is a comparison chart of time series data cleaning according to an embodiment of the present invention;

[0041] Figure 3 This is a diagram of the timing inversion result of an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0043] like Figure 1 As shown in Figure 1, a sequential inversion method for slope rock and soil parameters based on meta-inspiration analysis includes the following steps:

[0044] S1. Analyze and organize monitoring / detection data at different times and perform data cleaning on the monitoring / detection data;

[0045] S2. Based on the slope monitoring / detection information and geological survey information, a forward algorithm is used to obtain the slope rock and soil parameter solution space that meets the analysis requirements, and the time series of the slope rock and soil solution space is obtained;

[0046] S3. Establish a multi-objective optimization algorithm SDMOM (Structured Data Multi-objective Optimization Method) that considers the spatial correlation of structured data;

[0047] S4. Use SDMOM to analyze the time series of the solution space and obtain the inversion parameters of the slope rock and soil at different times;

[0048] Furthermore, in S1, it is required that the monitoring / detection information can include the displacement data of the slope, and data cleaning includes the following sub-steps:

[0049] S11, the time series is divided into S 0 ={s 0 1,s 0 2, …,s 0 t} to sort the data, delete the data with null values ​​in the original sequence, ensure that the data at each moment is a real number, and normalize it. The normalization calculation formula used is:

[0050]

[0051] Where, represents the normalized sedimentation value, X i Indicates the i-th sedimentation value to be normalized, i = 1, 2, 3, ...; max_x is the maximum sedimentation value in the sample, and min_x is the minimum sedimentation value in the sample.

[0052] S12. Establish an additive model to decompose the original sequence, and its expression is:

[0053]

[0054] Where Y(t) represents the actual observation value at time t, represents the trend item, represents the periodic term, R(t) represents the residual term at time t, and ai 、b i 、c i d i and e i are the i-th learnable parameters, i is an integer greater than or equal to 0, and n is the number of fitting items, which is used to control the accuracy of fitting. If the following formula is satisfied, it means that the setting of n is reasonable:

[0055]

[0056] Where R(t) n Represents the residual at time t when set to n.

[0057] Common solutions for time series decomposition include the quasi-Newton method, the conjugate gradient method, the L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, and the trust region method. The L-BFGS algorithm is a variant of the BFGS algorithm that overcomes the memory limitations of the BFGS algorithm by using limited memory to store and compute the approximate inverse of the Hessian matrix. The L-BFGS algorithm is very popular in machine learning and is particularly well-suited for large-scale optimization problems.

[0058] S13, the residuals at different times are combined into a residual sequence R = {r1, r2, ..., r t}, compare the size of each data in the sequence with the average of the sequence, if it satisfies The data at time i is considered to be an abnormal value. is the average value of the R series.

[0059] S14, extract the data corresponding to the abnormal value, and obtain the cleaned time series S = {s1,s 2, …,s t}.

[0060] Furthermore, in S2, the geological survey information is required to include the physical and mechanical parameter information to be inverted, and the forward algorithm adopted uses the finite element method as an analysis tool, where the judgment criterion for the analysis requirement is: the average error of the calculated displacement data is less than the monitoring error of the monitoring sensor.

[0061] In order to ensure consistency with the site, the variability and correlation of the soil are usually considered during finite element analysis, and stochastic finite element analysis is carried out. When implementing stochastic finite element analysis, the correlation and variability parameters of the parameters to be inverted in the geological survey information are first analyzed, and the random field is obtained according to the Cholesky decomposition. The standard normal distribution is combined to obtain the relevant random sample matrix. Finally, the standard normal distribution is transformed with equal probability to obtain the non-standard normal distribution random field samples. The calculation process of the distribution conversion is as follows:

[0062]

[0063] Where ξ(x) is a random number sample under normal distribution, is the upper triangular matrix of the Cholesky decomposition of the cross-correlation coefficient, L2 is the upper triangular matrix of the Cholesky decomposition of the autocorrelation coefficient, Φ(·) is the cumulative distribution function of the standard normal distribution, is the inverse function of the target marginal cumulative distribution; H i (x) is a discrete random field with correlation.

[0064] The random parameters are input into the finite element to carry out calculations, and the displacement data obtained by the finite element calculation of the monitoring points are extracted.

[0065] Furthermore, in S3, the calculation steps of the multi-objective optimization algorithm SDMOM that considers the spatial correlation of structured data are as follows:

[0066] For the problem to be optimized, make P(G)→max(P(G)), where G is a set of parameter spaces input to the optimization model, including a set of solutions in the solution space obtained by inverting various physical and mechanical parameters at different times, P(G) is the adaptability function of the parameter group G, and the larger the P(G) function value, the closer the required parameters are to the optimization goal, and max(P(G)) is the maximum function value obtained under various different parameter G states.

[0067] S31. Organize the inversion parameters according to the spatial position of the slope, with rows representing the horizontal direction in geological survey and columns representing the vertical direction in geological survey, to form a two-dimensional matrix g. Parts of the matrix that do not belong to the slope body are replaced by the number 0;

[0068] S32, select the solution space obtained in S2 as the optimization domain H = {ζ1, ζ2, ..., ζ t}, where ζ t It represents the solution space obtained by geotechnical inversion parameters at time t, in which each parameter combination is sorted according to accuracy and assigned a serial number. In each subset of the optimization domain H, the serial number is uniformly randomly sampled to obtain a set of solutions G1 = {g1, g2, ..., g t}, repeat this process j times to obtain the initial solution set G={G1,G2,...,G j};

[0069] S33. Calculate the correlation between the soil material parameter matrix at time t and time t+1 in each solution in the solution set G according to the material correlation algorithm. The calculation formula is as follows:

[0070]

[0071] Where C(t) is the correlation between geotechnical parameters at time t and t+1. The smaller the value, the better the correlation. t,i，j At time t, the matrix index is the value at position i and j, im is the maximum index value in the i direction, jm is the maximum index value in the j direction, and smooth is the minimum value set to ensure that the formula makes sense.

[0072] S34. Use L1 norm to represent the inverted monitoring error, use C(t) to represent the similarity of material parameters, and combine the similarity and monitoring error to calculate the fitness function value. The calculation formula is as follows:

[0073]

[0074] Where, is the value of the monitoring point obtained by inversion parameter calculation, S t The value obtained after cleaning the monitoring point, smooth is the minimum value set to ensure the formula is meaningful, C(t) is the material correlation, P(G i ) is G i The fitness of the group solution.

[0075] S35, using the meta-heuristic algorithm, with G = {G1, G2, ..., G j} is the initial population, P(G i ) A multi-objective optimization algorithm SDMOM is established for the fitness function considering the spatial correlation of structured data.

[0076] Metaheuristic algorithms are an optimization algorithm that includes simulated annealing algorithms, genetic algorithms, particle swarm optimization algorithms, ant colony algorithms, and gray wolf optimization algorithms. The idea behind the particle swarm optimization algorithm comes from the study of bird hunting behavior. By simulating the behavior of bird flocks flying and foraging, the group can achieve the optimal goal. It does not have the crossover and mutation operations in genetic algorithms, but instead searches for the global optimal solution by following the currently known optimal value. Compared with other optimization algorithms, the particle swarm optimization algorithm requires fewer parameters to be adjusted, has a fast convergence speed, and is simple and easy to implement. We take the solution space H = {ζ1, ζ2, ..., ζ t The inversion parameter matrix number of each solution is taken as the item to be optimized, and P(G i ) is used as the fitness function, and the particle velocity and position update formulas used are as follows:

[0077]

[0078] Where, represents the flying speed of particle i in the dth dimension in the kth iteration, represents the position of particle i in the dth dimension in the kth iteration, c1 and c2 are acceleration constants used to adjust the learning step size, r1 and r2 are random numbers with values ​​between [0, 1], used to increase the randomness of the search, and w is the inertia weight, a non-negative number, used to adjust the search range of the solution space.

[0079] Furthermore, for S4, the method established in S3 is used to solve the problem, and the solution with the highest fitness function of S34 is selected as the inversion parameter of the slope rock and soil mass at different times.

[0080] The following describes the details in conjunction with specific embodiments.

[0081] In S1, displacement monitoring of the slope is carried out while geological survey is carried out to obtain the soil parameter status of the slope at the beginning of monitoring, analyze and organize the monitoring / detection data at different times, and perform data cleaning on the monitoring / detection data;

[0082] The time series obtained by monitoring is converted into S 0 ={s 0 1,s 0 2, ..., s 0 t} to sort the data, delete the data with null values ​​in the original sequence, ensure that the data at each moment is a real number, and normalize it. The normalization calculation formula used is:

[0083]

[0084] Where, represents the normalized sedimentation value, X i Indicates the i-th sedimentation value to be normalized, i = 1, 2, 3, ...; max_x is the maximum sedimentation value in the sample, and min_x is the minimum sedimentation value in the sample.

[0085] Based on the L-BFGS algorithm, an additive model is established to decompose the original sequence, and its expression is:

[0086]

[0087] Where Y(t) represents the actual observation value at time t, represents the trend item, represents the periodic term, R(t) represents the residual term at time t, which is the part that cannot be fitted by the trend term and the periodic term, where a i 、b i 、c i d i and e iare i learnable parameters, i is an integer greater than or equal to 0, and n is the number of fitting items, which is used to control the accuracy of fitting. If the following formula is satisfied, it means that the setting of n is reasonable:

[0088]

[0089] Where R(t) n Represents the residual at time t when set to n.

[0090] The residuals at different times are combined into a residual sequence R = {r1, r2, ..., r t}, compare the size of each data in the sequence with the average of the sequence, if it satisfies The data at time i is considered to be an abnormal value. is the average value of the R series.

[0091] Extract the data corresponding to the outlier time and obtain the cleaned time series S={s1,s 2, …,s t},like Figure 2 shown.

[0092] In S2, based on the slope monitoring / detection information and geological survey information, a forward algorithm is used to obtain the slope rock and soil parameter solution space that meets the analysis requirements, and the time series of the slope rock and soil solution space is obtained; in order to ensure consistency with the site, the variability and correlation of the soil are usually considered when performing finite element analysis, and random finite element analysis is carried out. When implementing random finite element analysis, first, based on the correlation and variability parameters of the parameters to be inverted in the geological survey information, a random field is obtained according to Cholesky decomposition. Combined with the standard normal distribution, a related random sample matrix is ​​obtained, and finally, the standard normal distribution is transformed through equal probability to obtain a non-standard normal distribution random field sample. The calculation process of the distribution conversion is as follows:

[0093]

[0094] Where ξ(x) is a random number sample under normal distribution, is the upper triangular matrix of the Cholesky decomposition of the cross-correlation coefficient, L2 is the upper triangular matrix of the Cholesky decomposition of the autocorrelation coefficient, Φ(·) is the cumulative distribution function of the standard normal distribution, is the inverse function of the target marginal cumulative distribution; H i (x) is a discrete random field with correlation.

[0095] The random parameters are input into the finite element to carry out the calculation, and the displacement data obtained by the finite element calculation of the monitoring point is extracted. At each monitoring moment, the parameter field where the error between the calculated displacement data of the monitoring point and the cleaned monitoring data is less than the error measured by the system is selected as the solution space at that moment.

[0096] In S3, a multi-objective optimization algorithm SDMOM (Structured Data Multi-objective Optimization Method) considering the spatial correlation of structured data is established. For the problem to be optimized, P(G)→max(P(G)), where G is a set of parameter spaces input to the optimization model, including a set of solutions in the solution space obtained by inverting various physical and mechanical parameters at different times, P(G) is the adaptability function of the parameter group G, and the larger the P(G) function value, the closer the required parameters are to the optimization goal, and max(P(G)) is the maximum function value obtained under various different parameter G states.

[0097] The inversion parameters are organized according to the spatial position of the slope, with rows representing the horizontal direction in geological exploration and columns representing the vertical direction in geological exploration, forming a two-dimensional matrix g. The positions of the matrix that do not belong to the slope body are replaced by the number 0; the solution space obtained in S2 is selected as the optimization domain H = {ζ1, ξ2, ..., ζ t}, where ζ t It represents the solution space obtained by geotechnical inversion parameters at time t, in which each parameter combination is sorted according to accuracy and assigned a serial number. In each subset of the optimization domain H, the serial number is uniformly randomly sampled to obtain a set of solutions G1 = {g1, g2, ..., g t}, repeat this process j times to obtain the initial solution set G={G1,G2,...,G j};

[0098] According to the material correlation algorithm, the correlation between the soil material parameter matrix at time t and time t+1 in each solution in the solution set G is calculated. The calculation formula is as follows:

[0099]

[0100] Where C(t) is the correlation between geotechnical parameters at time t and t+1. The smaller the value, the better the correlation. t，i，j At time t, the matrix index is the value at position i and j, im is the maximum index value in the i direction, jm is the maximum index value in the j direction, and smooth is the minimum value set to ensure that the formula makes sense.

[0101] The L1 norm is used to represent the inverted monitoring error, and C(t) is used to represent the similarity of material parameters. The fitness function value is calculated by combining the similarity and the monitoring error. The calculation formula is as follows:

[0102]

[0103] Where, is the value of the monitoring point obtained by inversion parameter calculation, St The value obtained after cleaning the monitoring point, smooth is the minimum value set to ensure the formula is meaningful, C(t) is the material correlation, P(G i ) is G i The fitness of the group solution.

[0104] Using the meta-heuristic algorithm, G={G1,G2,...,G j} is the initial population, P(G i ) A multi-objective optimization algorithm SDMOM is established for the fitness function considering the spatial correlation of structured data.

[0105] This embodiment uses the particle swarm optimization algorithm for optimization. We use the solution space H = {ζ1, ζ2, ..., ζ t The inversion parameter matrix number of each solution is taken as the item to be optimized, and P(G i ) is used as the fitness function, and the particle velocity and position update formulas used are as follows:

[0106]

[0107] Where, represents the flying speed of particle i in the dth dimension in the kth iteration, represents the position of particle i in the dth dimension in the kth iteration, c1 and c2 are acceleration constants used to adjust the learning step size, r1 and r2 are two random numbers with values ​​between [0, 1], used to increase the randomness of the search, and w is the inertia weight, a non-negative number, used to adjust the search range of the solution space.

[0108] In S4, SDMOM is used to analyze the time series of the solution space to obtain the inversion parameters of the slope rock and soil at different times; the solution with the highest fitness function is selected as the inversion parameters of the slope rock and soil at different times, such as Figure 3 shown.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0112] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0113] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A slope rock and soil parameter inversion method based on time series correlation analysis, characterized in that: The steps include: S1. Obtain monitoring or detection data of slope rock and soil parameters at different times, and perform data cleaning on the acquired monitoring or detection data; S2. Using a positive algorithm to obtain the slope rock and soil parameter solution space that meets the requirements for the monitoring or detection data after data cleaning; S3. Based on the slope rock and soil parameter solution space obtained in S2, a multi-objective optimization algorithm model considering the spatial correlation of structured data is established, which specifically includes the following steps: S31. Construct inversion parameters according to the spatial position of the slope, with rows representing the horizontal direction in geological survey and columns representing the vertical direction in geological survey, to form a two-dimensional matrix , the position part that does not belong to the slope body is replaced by the number 0; S32, taking the slope rock and soil parameter solution space obtained in S2 as the optimization domain, uniformly randomly sampling sequence numbers in each subset of the optimization domain to obtain a set of solutions, and repeating multiple times to obtain the initial solution set; S33. Based on the correlation algorithm, the correlation between the soil material parameter matrix at time t and time t+1 in each set of solutions in the initial solution set is calculated. The specific calculation method of the correlation is: ； Where, for and The correlation of geotechnical parameters at the time, the smaller the value, the better the correlation. for At this moment, the matrix index is The value at the position, for The maximum index value in the direction, for The maximum index value in the direction, The minimum value set to ensure the formula is meaningful; S34, using the L1 norm to represent the inverted monitoring error, combined with the correlation obtained in S33 to calculate the fitness function, the specific calculation method of the fitness function is: ； Where, is the value of the monitoring point obtained by inversion parameter calculation, is the value obtained after cleaning the monitoring point. To ensure that the formula has a meaningful minimum value, is the material relevance, for fitness of the group solution; S35. Using the meta-heuristic algorithm, taking the initial solution set as the initial population, and combining it with the fitness function, a multi-objective optimization algorithm that considers the spatial correlation of structured data is established; S4. Use multi-objective optimization algorithm to solve the spatial time series and obtain the inversion parameters of the slope rock and soil at different times.

2. The slope rock and soil parameter inversion method based on time series correlation analysis according to claim 1 is characterized in that: The steps include: S11, sorting the monitoring or detection data of the slope rock and soil parameters obtained at different times according to time series, deleting the null values ​​in the series and normalizing them; S12, establishing an additive model to decompose the ordered time series in S11 and extracting the residuals of the monitoring or detection data of the slope rock and soil parameters at different times; S13, the residuals at different times are combined into a residual sequence and the size of each data in the residual sequence is compared with the average of the sequence. If the data meets the preset conditions, it is considered as abnormal data; S14. Eliminate abnormal data to obtain a cleaned time series.

3. The slope rock and soil parameter inversion method based on time series correlation analysis according to claim 1 is characterized in that: The meta-heuristic algorithm in S35 is specifically expressed as follows: ； ； Where, Indicates the Particles in iterations In the The flight speed of the dimension, Indicates the Particles in iterations In the The location of the dimension, is the acceleration constant, is a random number, is the inertia weight.

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