Slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity

Abstract

The invention relates to a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity. The method comprises the following steps: firstly, carrying out geological survey on a site slope to obtain variability parameters of a slope soil body; then, a slope finite element model is constructed on the basis of slope geometric parameters and the variability parameters, and the displacement states of the slope under different parameter conditions are determined on the basis of the slope finite element model; grading the slope sensitive area based on the displacement state; obtaining displacement data based on the grade of the slope sensitive area, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data by adopting a Pearson's correlation coefficient and a digital image correlation technology; and finally, determining a slope high-correlation area based on the displacement correlation coefficient and the spatial correlation coefficient, and setting a monitoring point position. The scientificity and accuracy of slope monitoring point arrangement are improved, and the method has certain significance for predicting and preventing slope disasters.

Description

Technical Field

[0001] The present application relates to the technical field of civil engineering operation and maintenance, and in particular to a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity. Background Art

[0002] With the continuous growth of the national economy, the increase in investment in railway construction has led to a rapid expansion of the scale of the railway network, and the density of the railway network has also increased. The development of transportation infrastructure is accompanied by an increase in the number of railway engineering projects. In order to ensure the safe operation of existing lines in railway projects, existing line subgrade monitoring is particularly important. In addition, the structural status monitoring knowledge of many existing line subgrade sections of railway projects is complex, which includes various monitoring objects, monitoring indicators and monitoring technologies. It is necessary to adopt a reasonable method to summarize and manage the monitoring knowledge and apply it. Among them, the reasonable arrangement of monitoring points is a key means to improve monitoring efficiency and accuracy. At present, there are two main optimization methods for the arrangement of slope deformation monitoring points: empirical method and optimization theory analysis method.

[0003] Most of the empirical methods are based on numerical analysis to preliminarily judge the deformation of the slope and divide the deformation area. Based on engineering experience, the monitoring points are arranged at places with large deformation and high deformation sensitivity. The displacement monitoring points are arranged at locations with high displacement response sensitivity, which helps to improve the monitoring effect. However, this point selection method is too dependent on experience and has strong subjectivity. At present, the commonly used monitoring point arrangement method is highly empirical, lacks quantitative analysis theory and methods, and is very different from one project to another, so it cannot be completely copied. Therefore, it is necessary to study how to optimize the arrangement of monitoring points through theoretical analysis.

[0004] The optimization theory analysis method uses the slope deformation results obtained by numerical analysis, and uses relevant optimization methods to analyze the relationship between the proposed monitoring points and the slope stability, and optimize the layout of the monitoring points. For example, the fuzzy recognition theory uses FLAC numerical software and fuzzy recognition theory to optimize the layout of slope monitoring points by considering structures such as cracks and soft interlayers.

[0005] Based on the limitations of the two methods, a monitoring point layout theory that integrates the empirical method and optimization theory was proposed, which comprehensively considers engineering experience and theoretical analysis, and proposes a method for jointly optimizing the layout of measurement points by sensitivity and correlation using physical analysis and gray theory. However, in most studies, there are difficulties in integrating and coordinating different optimization methods, which may affect the overall performance and effect of the monitoring system.

[0006] Therefore, in the related art, there is an urgent need for a method that can improve the accuracy of optimizing the arrangement of monitoring points. Summary of the invention

[0007] Based on this, it is necessary to provide a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity, which can improve the accuracy of the optimal arrangement of monitoring points in response to the above technical problems.

[0008] In a first aspect, the present application provides a method for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity. The method comprises:

[0009] Conduct geological survey on the on-site slope to obtain the variability parameters of the slope soil;

[0010] Constructing a finite element model of the slope based on the geometric parameters of the slope and the variability parameters, and determining the displacement state of the slope under different parameter conditions based on the finite element model of the slope;

[0011] Classifying the slope sensitive area based on the displacement state;

[0012] Obtaining displacement data based on the slope sensitive area level, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data using a Pearson correlation coefficient and a digital image correlation technique respectively;

[0013] Based on the displacement correlation coefficient and the spatial correlation coefficient, the high correlation area of ​​the slope is determined and the monitoring points are set.

[0014] Optionally, in one embodiment of the present application, determining the displacement state of the slope under different parameter conditions based on the slope finite element model includes:

[0015] The exponential autocorrelation function and Latin hypercube sampling method are used to generate material random fields;

[0016] Repeated random finite element analysis is performed to balance the ground stress and obtain the slope displacement states in saturated and natural states using different parameters.

[0017] Optionally, in an embodiment of the present application, the grading of slope sensitive areas based on the displacement state includes:

[0018] The slope sensitivity level is divided based on the horizontal displacement and membership function of the slope in saturated state and natural state.

[0019] Optionally, in one embodiment of the present application, the expression of the membership function is:

[0020]

[0021]

[0022] Where x is the displacement change of a node of the slope between the saturated state and the natural state, ΔS maxIt is the maximum value of displacement change in all unit nodes. P1(x) means extremely insensitive, P2(x) means insensitive, P3(x) means sensitive, and P4(x) means extremely sensitive.

[0023] Optionally, in one embodiment of the present application, the calculation formula of the displacement correlation coefficient is:

[0024]

[0025] Among them, δ n is the displacement correlation coefficient, σ x is the standard deviation of the displacement variable, σ y is the standard deviation of another shift variable, X is the shift variable value, Y is the shift variable value, μ x is the displacement mean, μ y is another shifted mean.

[0026] Optionally, in one embodiment of the present application, the digital image correlation technology includes:

[0027] Based on the displacement points between regions, the displacement matrix is ​​generated by regional division and normalized;

[0028] A spatial correlation coefficient is determined based on the normalized displacement matrix.

[0029] Optionally, in one embodiment of the present application, the calculation formula of the spatial correlation coefficient is:

[0030]

[0031] Among them, T1′T2′ is the displacement matrix of different regions after normalization.

[0032] In a second aspect, the present application also provides a device for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity. The device comprises:

[0033] The parameter acquisition module is used to conduct geological surveys on the on-site slopes and obtain the variability parameters of the slope soil;

[0034] A finite element analysis module, used for constructing a finite element model of the slope based on the geometric parameters of the slope and the variability parameters, and determining the displacement state of the slope under different parameter conditions based on the finite element model of the slope;

[0035] A slope sensitive area classification module, used for classifying the slope sensitive area based on the displacement state;

[0036] A correlation coefficient determination module, used for obtaining displacement data based on the slope sensitive area level, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data by using a Pearson correlation coefficient and a digital image correlation technology respectively;

[0037] The monitoring point setting module is used to determine the high correlation area of ​​the slope based on the displacement correlation coefficient and the spatial correlation coefficient, and set the monitoring points.

[0038] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the methods described in the above embodiments.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in each of the above embodiments are implemented.

[0040] The above slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity first conducts geological survey on the on-site slope to obtain the variability parameters of the slope soil; then, a slope finite element model is constructed based on the slope geometric parameters and the variability parameters, and the displacement state of the slope under different parameter conditions is determined based on the slope finite element model; then, the slope sensitive area is graded based on the displacement state; then, displacement data is obtained based on the slope sensitive area grade, and the displacement correlation coefficient and spatial correlation coefficient are determined based on the displacement data using the Pearson correlation coefficient and the digital image correlation technology respectively; finally, the slope high correlation area is determined based on the displacement correlation coefficient and the spatial correlation coefficient, and the monitoring points are set. In other words, different from the previous monitoring point arrangement method, the optimization arrangement of the slope monitoring points is realized by comprehensively considering the integration of traditional engineering experience and optimization theory combined with numerical analysis. The soil variability parameters of the on-site slope are obtained through geological survey, and the slope displacement data is obtained by strength reduction in combination with the stochastic finite element analysis method. Fuzzy pattern recognition is used to calculate the slope displacement, and the finite element calculation results are used to realize the classification of the slope sensitive areas. The Pearson correlation coefficient is used to perform correlation analysis on the high-sensitivity areas, and the high-correlation areas are further screened. Combining the advantages of traditional sensitivity analysis and optimization theory, the optimization theory is used to further eliminate the inefficient layout caused by the strong correlation area on the basis of the sensitive area division, so as to determine the optimal monitoring point layout, and improve the scientificity and accuracy of the slope monitoring point layout. In addition, the integration of different optimization methods is more coordinated, providing new ideas for the layout of slope monitoring points, which is of certain significance for predicting and preventing slope disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is an application environment diagram of a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity in one embodiment;

[0042] Figure 2 It is a schematic diagram of a flow chart of a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity in one embodiment;

[0043] Figure 3 A schematic diagram of a slope sensitive area division result in one embodiment;

[0044] Figure 4 A schematic diagram of a heat map of displacement correlation in a high-sensitivity region in one embodiment;

[0045] Figure 5 A schematic diagram of slope monitoring point arrangement based on sensitivity area and correlation comparison in one embodiment;

[0046] Figure 6 It is a structural block diagram of a device for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity in one embodiment;

[0047] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or it can be placed on the cloud or other network servers. Among them, the terminal can be but not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0050] In one embodiment, Figure 2 As shown in the figure, a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity is provided. Figure 1The server in the example is used to illustrate the following steps:

[0051] S201: Conduct geological survey on the on-site slope to obtain the variability parameters of the slope soil.

[0052] In the embodiment of the present application, first, a geological survey of the on-site slope is carried out, and the variability parameters of the slope soil are obtained through geological statistical analysis. Specifically, the variability parameters include the physical and mechanical parameters of the soil, including the density, internal friction angle and cohesion of the soil.

[0053] S203: constructing a slope finite element model based on the slope geometric parameters and the variability parameters, and determining the displacement state of the slope under different parameter conditions based on the slope finite element model.

[0054] In the embodiment of the present application, a finite element model of the slope is then constructed based on the geometric parameters of the slope and the variability parameters of the soil, that is, a geometric model is established for the shape and size of the slope and the physical and mechanical parameters of the soil. The random field theory is used to simulate the spatial variability of the soil parameters as random variables, and the displacement state of the slope under different parameter conditions is analyzed.

[0055] Specifically, in one embodiment of the present application, determining the displacement state of the slope under different parameter conditions based on the slope finite element model includes:

[0056] S301: Generate material random field using exponential autocorrelation function and Latin hypercube sampling method.

[0057] S303: Perform repeated random finite element analysis to balance the ground stress and use different parameters to obtain the slope displacement state in the saturated state and the natural state.

[0058] In one embodiment of the present application, when generating a random field, the core algorithm is to use Cholesky decomposition, and based on the known soil random variable parameters, use the exponential autocorrelation function and Latin hypercube sampling to generate a material random field. Among them, for the soil random field discretization method, it is necessary to use the Latin hypercube sampling method to sample the data distribution to obtain random numbers, and the exponential autocorrelation function is used when considering the spatial autocorrelation of the soil material, and its normalized calculation formula is:

[0059]

[0060] Among them, ρ(τ x ,τ y ) is the correlation coefficient, τ x and τ y are the distances in the x and y directions, δ h and δ vThey are the fluctuation coefficients in two directions, which are used to characterize the degree of spatial autocorrelation of soil parameters. The larger the fluctuation range, the stronger the degree of spatial autocorrelation of the parameters.

[0061] After that, the self-gravity balance calculation is performed, and then the value of the field variable is gradually increased in the analysis step to simulate the strength reduction; boundary conditions and loading are applied, usually including gravity loading and possible additional loads; meshing is performed, and the appropriate unit type and size are set; the job is submitted and the analysis is run, and the model is monitored with the field variables until the model no longer converges, that is, the slope is considered to have reached a critical state, and the displacement state of the slope under different parameters is obtained to form a displacement data set.

[0062] S205: Classifying the slope sensitive areas based on the displacement state.

[0063] In the embodiment of the present application, fuzzy pattern recognition is used to analyze the slope displacement, and the finite element calculation results are used to realize the classification of the slope sensitive area. Considering the deformation sensitivity of the slope, it is divided into four levels with increasing sensitivity, namely, extremely insensitive (P1), insensitive (P2), sensitive (P3), and extremely sensitive (P4). According to the different sensitivity levels of the slope, the following is generated: Figure 3 The slope sensitive area division diagram is shown.

[0064] Specifically, in one embodiment of the present application, the grading of slope sensitive areas based on the displacement state includes:

[0065] The slope sensitivity level is divided based on the horizontal displacement and membership function of the slope in saturated state and natural state.

[0066] In one embodiment of the present application, a sensitivity fuzzy analysis method is used to analyze the sensitivity of each unit node in the finite element model. By comparing the horizontal displacement of the slope in the saturated state and the natural state, combined with the membership function, the deformation sensitivity of different positions of the slope is judged and divided into four levels with increasing sensitivity, namely extremely insensitive (P1), insensitive (P2), sensitive (P3), and extremely sensitive (P4).

[0067] Specifically, in one embodiment of the present application, the expression of the membership function is:

[0068]

[0069] Where x is the displacement change of a node of the slope between the saturated state and the natural state, ΔS max It is the maximum value of displacement change in all unit nodes. P1(x) means extremely insensitive, P2(x) means insensitive, P3(x) means sensitive, and P4(x) means extremely sensitive.

[0070] In one embodiment of the present application, the maximum membership principle is used to calculate the displacement change x and P i If there exists i0≤4, such that P i0 =max(P1(x0),P2(x0),P3(x0),P4(x0)), then x0 is said to be relatively subordinate to P i0 , thus we can judge that x0 belongs to P i0 This level.

[0071] S207: Obtaining displacement data based on the slope sensitive area level, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data using a Pearson correlation coefficient and a digital image correlation technology, respectively.

[0072] In the embodiment of the present application, the cooperative deformation relationship between different regions can be revealed by comparing multiple groups of finite element calculation results. Combined with the grade division results of the slope sensitive area, the displacement data obtained by the finite element divided grid under multiple groups of random field parameters are statistically analyzed for the units in the sensitive area (P3) and the extremely sensitive area (P4). The displacement data generated by each grid is generated into a sequence X1, and finally all random field displacement data are arranged into multiple groups of displacement sequences {X1.....X n}. The Pearson correlation coefficient is used to reflect the displacement correlation between different sequences, and the displacement correlation coefficient δ n At the same time, the displacement data generated by the finite element analysis is divided into square grids with a side length of 5m and divided into digital image matrices T n , the digital image correlation (DIC) technology is used to calculate the similarity function of the image and obtain the spatial correlation coefficient, that is, the correlation coefficient between regions γ n .

[0073] Specifically, in one embodiment of the present application, the calculation formula of the displacement correlation coefficient is:

[0074]

[0075] Among them, δ n is the displacement correlation coefficient, σ x is the standard deviation of the displacement variable, σ y is the standard deviation of another shift variable, X is the shift variable value, Y is the shift variable value, μ x is the displacement mean, μ y is another shifted mean.

[0076] In one embodiment of the present application, the digital image correlation technology includes:

[0077] S401: Generate a displacement matrix by using regional division based on the inter-region displacement points, and perform normalization processing.

[0078] S403: Determine a spatial correlation coefficient based on the normalized displacement matrix.

[0079] In one embodiment of the present application, when the DIC technique is used to analyze the correlation between regions, for the reference sub-region f, the center point is x 0 =[x0y0] T , other displacement points on the reference sub-area are x i =[x i y i ] T , with the center x 0 As a reference, the displacement point in the reference sub-area is expressed as:

[0080]

[0081] Where Δx i x 0 Represents x i to x 0 Similarly, set the displacement deviation of the comparison sub-area with the previous x i The corresponding current coordinate point is x i′ =[x i ′y i ′] T , the center point of the comparison sub-area is x d , and similarly, we get the displacement point expression of the contrast sub-area:

[0082]

[0083] Where Δx i′ x 0 Represents x i′ to x 0 displacement deviation.

[0084] The displacement matrix T is generated by using regional division to convert the displacement point expressions of different slope regions n , normalize the displacement matrix to generate a new displacement matrix T n ′, use matrix multiplication to obtain the spatial correlation coefficient γ n To reflect the correlation between regions.

[0085] Specifically, in one embodiment of the present application, the calculation formula of the spatial correlation coefficient is:

[0086]

[0087] Among them, T1′T2′ is the displacement matrix of different regions after normalization.

[0088] S209: Determine the slope high correlation area based on the displacement correlation coefficient and the spatial correlation coefficient, and set monitoring points.

[0089] In the embodiment of the present application, the displacement correlation coefficients of different grids and the spatial correlation coefficients of the same grid are statistically weighted averaged, as shown in the following formula:

[0090]

[0091] Among them, k is the total number of corresponding grids in the divided area, δ k is the displacement correlation coefficient, γ n is the spatial correlation coefficient, is the weighted correlation coefficient.

[0092] According to the correlation analysis results, the correlation threshold is set to 0.7, and high correlation areas are selected based on the threshold, such as Figure 4 As shown in the figure, a displacement correlation heat map is generated for the high correlation area in the result. For the high correlation area with a correlation coefficient greater than the threshold, the high sensitivity area is selected as the monitoring point in combination with the actual project, as shown in the figure. Figure 5 shown.

[0093] In the above-mentioned slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity, first, a geological survey is conducted on the on-site slope to obtain the variability parameters of the slope soil; then, a slope finite element model is constructed based on the slope geometric parameters and the variability parameters, and the displacement state of the slope under different parameter conditions is determined based on the slope finite element model; then, the slope sensitive area is graded based on the displacement state; then, displacement data is obtained based on the slope sensitive area grade, and the displacement correlation coefficient and spatial correlation coefficient are determined based on the displacement data using the Pearson correlation coefficient and the digital image correlation technology respectively; finally, the slope high correlation area is determined based on the displacement correlation coefficient and the spatial correlation coefficient, and the monitoring points are set. In other words, different from the previous monitoring point arrangement method, the optimization arrangement of the slope monitoring points is realized by comprehensively considering the integration of traditional engineering experience and optimization theory combined with numerical analysis. The soil variability parameters of the on-site slope are obtained through geological survey, and the slope displacement data is obtained by strength reduction in combination with the stochastic finite element analysis method. Fuzzy pattern recognition is used to calculate the slope displacement, and the finite element calculation results are used to realize the classification of the slope sensitive areas. The Pearson correlation coefficient is used to perform correlation analysis on the high-sensitivity areas, and the high-correlation areas are further screened. Combining the advantages of traditional sensitivity analysis and optimization theory, the optimization theory is used to further eliminate the inefficient layout caused by the strong correlation area on the basis of the sensitive area division, so as to determine the optimal monitoring point layout, and improve the scientificity and accuracy of the slope monitoring point layout. In addition, the integration of different optimization methods is more coordinated, providing new ideas for the layout of slope monitoring points, which is of certain significance for predicting and preventing slope disasters.

[0094] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0095] Based on the same inventive concept, the embodiment of the present application also provides a device for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity for implementing the above-mentioned method for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more devices for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity provided below can refer to the limitations of the method for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity in the above text, and will not be repeated here.

[0096] In one embodiment, Figure 6 As shown, a device 600 for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity is provided, comprising: a parameter acquisition module 601, a finite element analysis module 603, a slope sensitive area classification module 605, a correlation coefficient determination module 607 and a monitoring point setting module 609, wherein:

[0097] The parameter acquisition module 601 is used to perform geological survey on the on-site slope to obtain the variability parameters of the slope soil.

[0098] The finite element analysis module 603 is used to construct a finite element model of the slope based on the geometric parameters of the slope and the variability parameters, and determine the displacement state of the slope under different parameter conditions based on the finite element model of the slope.

[0099] The slope sensitive area classification module 605 is used to classify the slope sensitive area based on the displacement state.

[0100] The correlation coefficient determination module 607 is used to obtain displacement data based on the slope sensitive area level, and determine the displacement correlation coefficient and the spatial correlation coefficient based on the displacement data by using the Pearson correlation coefficient and the digital image correlation technology respectively.

[0101] The monitoring point setting module 609 is used to determine the slope high correlation area based on the displacement correlation coefficient and the spatial correlation coefficient, and set the monitoring points.

[0102] In one embodiment of the present application, the finite element analysis module is also used to:

[0103] The exponential autocorrelation function and Latin hypercube sampling method are used to generate material random fields;

[0104] Repeated random finite element analysis is performed to balance the ground stress and obtain the slope displacement states in saturated and natural states using different parameters.

[0105] In one embodiment of the present application, the slope sensitive area classification module is also used to:

[0106] The slope sensitivity level is divided based on the horizontal displacement and membership function of the slope in saturated state and natural state.

[0107] In one embodiment of the present application, the expression of the membership function is:

[0108]

[0109]

[0110] Where x is the displacement change of a node of the slope between the saturated state and the natural state, ΔS max It is the maximum value of displacement change in all unit nodes. P1(x) means extremely insensitive, P2(x) means insensitive, P3(x) means sensitive, and P4(x) means extremely sensitive.

[0111] In one embodiment of the present application, the calculation formula of the displacement correlation coefficient is:

[0112]

[0113] Among them, δ n is the displacement correlation coefficient, σ x is the standard deviation of the displacement variable, σ y is the standard deviation of another shift variable, X is the shift variable value, Y is the shift variable value, μ x is the displacement mean, μ y is another shifted mean.

[0114] In one embodiment of the present application, the correlation coefficient determination module is further used to:

[0115] Based on the displacement points between regions, the displacement matrix is ​​generated by regional division and normalized;

[0116] A spatial correlation coefficient is determined based on the normalized displacement matrix.

[0117] In one embodiment of the present application, the calculation formula of the spatial correlation coefficient is:

[0118]

[0119] Among them, T1′T2′ is the displacement matrix of different regions after normalization.

[0120] Each module in the above-mentioned device for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0121] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0122] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0123] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0124] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0125] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0127] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0128] The technical features of the above embodiments may be arbitrarily combined. 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.

[0129] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity, characterized in that: The method comprises: Conduct geological survey on the on-site slope to obtain the variability parameters of the slope soil; Constructing a finite element model of the slope based on the geometric parameters of the slope and the variability parameters, and determining the displacement state of the slope under different parameter conditions based on the finite element model of the slope; Classifying the slope sensitive area based on the displacement state; Obtaining displacement data based on the slope sensitive area level, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data using a Pearson correlation coefficient and a digital image correlation technique respectively; Based on the displacement correlation coefficient and the spatial correlation coefficient, the high correlation area of ​​the slope is determined and the monitoring points are set.

2. The slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity according to claim 1 is characterized in that: Determining the displacement state of the slope under different parameter conditions based on the slope finite element model includes: The exponential autocorrelation function and Latin hypercube sampling method are used to generate material random fields; Repeated random finite element analysis is performed to balance the ground stress and obtain the slope displacement states in saturated and natural states using different parameters.

3. The slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity according to claim 1 is characterized in that: The classification of slope sensitive areas based on the displacement state includes: The slope sensitivity level is divided based on the horizontal displacement and membership function of the slope in saturated state and natural state.

4. The slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity according to claim 3 is characterized in that: The expression of the membership function is: Where x is the displacement change of a node of the slope between the saturated state and the natural state, ΔS max It is the maximum value of displacement change in all unit nodes. P1(x) means extremely insensitive, P2(x) means insensitive, P3(x) means sensitive, and P4(x) means extremely sensitive.

5. The slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity according to claim 1 is characterized in that: The calculation formula of the displacement correlation coefficient is: Among them, δ n is the displacement correlation coefficient, σ x is the standard deviation of the displacement variable, σ y is the standard deviation of another shift variable, X is the shift variable value, Y is the shift variable value, μ x is the displacement mean, μ y is another shifted mean.

6. The slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity according to claim 1 is characterized in that: The digital image correlation technology includes: Based on the displacement points between regions, the displacement matrix is ​​generated by regional division and normalized; A spatial correlation coefficient is determined based on the normalized displacement matrix.

7. The slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity according to claim 6 is characterized in that: The calculation formula of the spatial correlation coefficient is: Among them, T1 ′ T2 ′ is the displacement matrix of different regions after normalization.

8. A device for arranging slope monitoring points based on sparse spatial correlation and displacement sensitivity, characterized in that: The device comprises: The parameter acquisition module is used to conduct geological surveys on the on-site slopes and obtain the variability parameters of the slope soil; A finite element analysis module, used for constructing a finite element model of the slope based on the geometric parameters of the slope and the variability parameters, and determining the displacement state of the slope under different parameter conditions based on the finite element model of the slope; A slope sensitive area classification module, used for classifying the slope sensitive area based on the displacement state; A correlation coefficient determination module, used for obtaining displacement data based on the slope sensitive area level, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data by using a Pearson correlation coefficient and a digital image correlation technology respectively; The monitoring point setting module is used to determine the high correlation area of ​​the slope based on the displacement correlation coefficient and the spatial correlation coefficient, and set the monitoring points.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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