Laser array type soil surface displacement field identification method and system

Abstract

The invention provides a laser array type soil surface displacement field identification method, which comprises the following steps of: determining a plurality of observation points according to a laser array and a target on the soil surface, establishing a coordinate system by taking a soil body mark point without load as an original point, and recording the original coordinates of each observation point; after the soil body displaces, the laser array records coordinates of the observation points after displacement according to a scanning period; extracting coordinates of a plurality of observation points after soil displacement according to the target displacement field of the soil surface and the spatial change surface data; and interpolating and outputting the plurality of coordinates into a surface by using an interpolation method. According to the invention, high-precision and non-contact scanning is carried out on the land surface by utilizing laser displacement sensor equipment through a laser array type soil surface displacement field identification technology, and the displacement condition of the land surface can be monitored in real time through processing and analyzing scanned data, so that potential geological disaster risk points can be identified. Therefore, the purpose of continuously monitoring the soil surface displacement in real time is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of engineering measurement, and in particular relates to a laser array type soil surface displacement field recognition method. Background Art

[0002] With the acceleration of urbanization and the frequent occurrence of geological disasters, the displacement of the land surface has become an important research topic. Traditional land displacement monitoring methods often require a lot of manpower and material resources for field investigations, which is costly and inefficient. Using remote sensing technology for non-contact detection and regular collection of surface area image data to observe the deformation of the surface area has the disadvantage that it can only be observed periodically and cannot be monitored continuously and in real time, so the judgment results may be inaccurate or missed. Summary of the invention

[0003] In view of the above technical problems, the present invention uses laser array type soil surface displacement field recognition technology to perform high-precision, non-contact scanning of the land surface using laser displacement sensor equipment. By processing and analyzing the scanned data, the displacement of the land surface can be monitored in real time, and potential geological disaster risk points can be identified, thereby achieving the purpose of real-time and continuous monitoring of soil surface displacement.

[0004] The first aspect of the present invention provides:

[0005] Determine a number of observation points based on the laser array and the target on the soil surface, establish a coordinate system with the soil body mark point when there is no load as the origin, and record the original coordinates of each of the observation points;

[0006] After the soil body is displaced, the laser array records the coordinates of each of the observation points after displacement according to a scanning cycle;

[0007] Extracting the coordinates of the plurality of observation points after the soil body is displaced according to the target displacement field and the spatial variation surface data of the soil surface;

[0008] The plurality of coordinates are interpolated and output as a surface using an interpolation method.

[0009] The second aspect of the present invention provides: a laser array soil surface displacement field identification system, a laser array installation device, a plurality of laser sensors, at least one processor, and at least one memory, wherein the plurality of laser sensors are electrically connected to the processor; the installation device comprises: a plurality of laser sensor installation holes, a carrier, the carrier having a plurality of laser sensor installation holes evenly distributed thereon, and laser sensors are installed in the laser sensor installation holes; the memory stores instructions, which are executed by at least one processor in accordance with the steps of the above-mentioned method.

[0010] The third aspect of the present invention provides: a computer-readable storage medium having a computer program / instruction stored thereon, characterized in that the computer program / instruction implements the steps of the above method when executed by a processor.

[0011] The fourth aspect of the present invention provides: a computer program product, comprising a computer program / instruction, characterized in that the computer program / instruction implements the steps of the above method when executed by a processor.

[0012] A fifth aspect of the present invention provides: a network system, comprising: a first server and a second server, wherein the first server or the second server is capable of executing the steps of the above method.

[0013] The beneficial effects of the present invention are: a method for using laser displacement sensor technology to realize real-time monitoring and identification of the displacement field of the land surface is proposed, and at the same time, it can meet the requirements of high-precision, non-contact, and real-time scanning in a small plane, and interpolate the points of data collected by each laser displacement sensor to form a surface, and integrate the surface measured in each scanning cycle into a deformation animation; the target displacement field and spatial variation surface data interpolation and visualization software are used for the soil surface deformation animation to output the maximum and minimum values ​​of the deformation in each scanning cycle, and obtain the deformation result of the soil. In addition, the system has a simple structure, strong reliability, and low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Flow chart of an embodiment of the present invention;

[0015] Figure 2 Interpolation flow chart of the interpolation method according to an embodiment of the present invention;

[0016] Figure 3 Semivariogram analysis flow chart of an embodiment of the present invention;

[0017] Figure 4 A flow chart of generating the number of two-dimensional coordinate groups used by the interpolation method in an embodiment of the present invention;

[0018] Figure 5 Interpolation process of the interpolation method according to the embodiment of the present invention Figure 2 ;

[0019] Figure 6 Flowchart of determining observation points according to an embodiment of the present invention;

[0020] Figure 7 A schematic diagram of the structure of an identification system according to an embodiment of the present invention;

[0021] Figure 8 A schematic diagram of the structure of a laser array installation device according to an embodiment of the present invention;

[0022] Fig. 9Monitoring of soil surface displacement field in embodiments of the present invention Figure 1 ;

[0023] Fig.10 Monitoring of soil surface displacement field in embodiments of the present invention Figure 2 ;

[0024] Fig.11 Monitoring of soil surface displacement field in embodiments of the present invention Figure 3 ;

[0025] Fig.12 A diagram of soil surface displacement field sensor monitoring data according to an embodiment of the present invention;

[0026] Fig.13 Kriging method monitoring diagram of soil surface displacement field according to an embodiment of the present invention;

[0027] Fig.14 The three-spline method monitoring diagram of the soil surface displacement field according to the embodiment of the present invention;

[0028] The symbols in the figure indicate the following meanings: laser array mounting device 1, laser sensor 2, processor 3, memory 4, several laser sensor mounting holes 11, carrier 12. DETAILED DESCRIPTION

[0029] The following examples further illustrate the content of the present invention, but should not be construed as limiting the present invention. Without departing from the spirit and substance of the present invention, modifications or substitutions made to the methods, steps or conditions of the present invention all fall within the scope of the present invention.

[0030] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0031] In some embodiments of the present invention, Figure 1 As shown:

[0032] A laser array soil surface displacement field identification method, comprising:

[0033] Determine a number of observation points based on the laser array and the target on the soil surface, establish a coordinate system with the soil body mark point when there is no load as the origin, and record the original coordinates of each of the observation points;

[0034] After the soil body is displaced, the laser array records the coordinates of each of the observation points after displacement according to a scanning cycle;

[0035] Extracting the coordinates of the plurality of observation points after the soil body is displaced according to the target displacement field and the spatial variation surface data of the soil surface;

[0036] The plurality of coordinates are interpolated and output as a surface using an interpolation method.

[0037] In these embodiments:

[0038] Laser array soil surface displacement field recognition is a technology that uses laser displacement sensor technology to achieve real-time monitoring and recognition of land surface displacement field. This technology has the advantages of strong real-time performance, high precision, and high degree of automation. It can greatly improve the efficiency and accuracy of land displacement monitoring and provide important technical support for the prevention and response of geological disasters. Therefore, laser array soil surface displacement field recognition technology has broad application prospects in the fields of geological disaster monitoring, urban planning, and land resource management.

[0039] Soil surface displacement field: specifically refers to the displacement field of the soil surface, that is, the displacement size and direction of each point on the soil surface at different times. It reflects the deformation of the soil surface under external loads, deadweight or geological effects.

[0040] Target displacement field: describes the change of displacement size and direction of an object at a certain point in space over time. It is an important representation of the object's motion state and can reflect the object's deformation, motion trajectory, and force conditions.

[0041] Spatially changing surface data: refers to data that describes the changes in the location, shape, size and attribute characteristics of a region (surface) in geographic space at different time points. These data are usually expressed in two-dimensional or three-dimensional form to reflect the distribution of geographic entities or phenomena in geographic space and their evolution over time.

[0042] In some embodiments of the present invention, Figure 2 As shown, the difference from the above embodiment is that:

[0043] The step of interpolating the plurality of coordinates into a surface by using an interpolation method comprises:

[0044] Performing semivariogram analysis on the two-dimensional groups of horizontal coordinates and the two-dimensional arrays of vertical coordinates generated by the plurality of said coordinates;

[0045] Select the Kriging interpolation method and set the interpolation parameters, including data source, data field, output surface type, transformation type, trend removal order, and search neighborhood;

[0046] Perform interpolation operations, calculate the attribute value of each prediction point according to the set parameters and theoretical variation function model, output it as a surface and generate a prediction surface map.

[0047] In some embodiments of the present invention, Figure 3 As shown, the difference from the above embodiment is that:

[0048] The step of performing semivariogram analysis on the plurality of coordinates comprises:

[0049] Calculate the distances between the coordinates of all the observation points after displacement, group the distances, and then calculate the average semi-variance value of each group of distances;

[0050] According to the average semivariance value of each group of distances, a distance semivariance scatter plot is drawn, and a theoretical variogram model is obtained by fitting;

[0051] According to the distance semivariance scatter plot, the nugget value, the sill value and the range are determined.

[0052] In some embodiments of the present invention, Figure 4 As shown, the difference from the above embodiment is that:

[0053] The step of interpolating the plurality of coordinates into a surface by an interpolation method also includes:

[0054] According to the coordinates of the observation points in the same scanning cycle, comparing and obtaining the minimum horizontal coordinate and the maximum horizontal coordinate, the minimum vertical coordinate and the maximum vertical coordinate of all the observation points;

[0055] According to the minimum horizontal coordinate and the maximum horizontal coordinate, the minimum vertical coordinate and the maximum vertical coordinate as the starting point and the end point, a plurality of equally spaced horizontal coordinates and vertical coordinates are generated, and the number of horizontal coordinate groups and the vertical coordinate array are generated at the same time;

[0056] The number of groups of horizontal coordinates and the array of vertical coordinates are converted into a coordinate matrix of a grid to obtain the number of two-dimensional groups of horizontal coordinates and the two-dimensional array of vertical coordinates.

[0057] In some embodiments of the present invention, Figure 5 As shown, the difference from the above embodiment is that:

[0058] The step of interpolating the plurality of coordinates by an interpolation method and outputting them as a surface comprises:

[0059] N+1 data points are determined, N intervals are obtained, and a curve including N line segments is obtained, wherein the curve of each segment of the N line segments can be expressed as a cubic polynomial.

[0060] In some embodiments of the present invention, Figure 6 As shown, the difference from the above embodiment is that:

[0061] The steps of determining several observation points based on the laser array and the targets on the soil surface include:

[0062] Placing a plurality of laser displacement sensors in evenly arranged mounting holes to form the laser array;

[0063] The target is placed on the observation point on the soil surface, and the emission direction of the laser displacement sensor is perpendicular to the reflection surface of the target.

[0064] In some embodiments of the present invention, Figure 1 As shown, the difference from the above embodiment is that:

[0065] The soil displacement step includes applying load or vibration to the soil.

[0066] In some embodiments of the present invention, Figure 1 As shown, the difference from the above embodiment is that:

[0067] The step of extracting the coordinates of the observation points under load conditions comprises:

[0068] The interpolation point extraction method (self.Z=grid_values.data) is used to extract the interpolation calculation result and the interpolated point.

[0069] In some embodiments of the present invention, Figure 1 As shown, the difference from the above embodiment is that:

[0070] The plurality of planes are outputted sequentially in the scanning period to form a deformation animation.

[0071] In some embodiments of the present invention, Figure 1 As shown, the difference from the above embodiment is that:

[0072] A laser array soil surface displacement field recognition system, the system comprising: a laser array installation device, a plurality of laser sensors, at least one processor, and at least one memory, wherein the memory stores instructions, and when the at least one processor is used to execute the steps of the method according to any one of the above embodiments; the plurality of laser sensors are electrically connected to the processor; the installation device comprises: a plurality of laser sensor installation holes, and a carrier, wherein the plurality of laser sensor installation holes are evenly distributed on the carrier, and laser sensors are installed in the laser sensor installation holes.

[0073] In these embodiments:

[0074] S1: 9, 12 or more laser displacement sensors are arranged into a laser array as follows: Figure 1 The laser displacement sensors are placed in the holes to form a laser array.

[0075] S2: Determine an observation point on the soil surface and set a target at the observation point, and the reflective surface of the target is perpendicular to the emission direction of the laser displacement sensor.

[0076] S3: Establish a coordinate system with the lower left corner of the soil body when there is no load as the origin, and record the coordinates of each observation point, such as the first point.

[0077] S4: Apply load or vibration to the soil. The laser displacement sensor monitors the displacement of each observation point every 0.01s and records the coordinates of each observation point after displacement. For example, the point recorded in the first 0.01s after displacement is

[0078] Where: superscript n is the initial state or the nth measuring point after deformation, subscript i is the measuring point number, i = 1, 2, 3, 4, 5, 6, 7, 8, 9;

[0079] S5: The target displacement field and spatially varying surface data interpolation and visualization software written in Python is used to extract the coordinates of the observation points under load conditions, and these points are output as a surface using Kriging or tri-spline interpolation methods.

[0080] S6: Visualization: Use matplotlib.pyplot to plot the interpolated points. Play these planes at a speed of one per 0.01s to make a deformation animation.

[0081] Among them, S5 specifically includes the following steps:

[0082] The target displacement field and spatial variation surface data interpolation and visualization software written in Python is used to interpolate and visualize the 9 or 12 points or more points collected by the laser displacement device into a deformed surface video:

[0083] S51: Generate the linear space of x and y. First, use the linspace function in Python's NumPy library to calculate the minimum and maximum horizontal coordinates, minimum and maximum vertical coordinates of all points under the same time load conditions. Then, the np.linspace function uses these minimum and maximum coordinate values ​​as the starting and end points to generate 100 equally spaced horizontal and vertical coordinates. Finally, the x and y arrays contain these generated coordinate values, respectively.

[0084] In S51: linspace function: The linspace function in the NumPy library is used to generate an arithmetic progression, which is suitable for generating uniformly distributed values ​​in a specified interval. The basic usage of the linspace function is to specify the starting value, the ending value, and the number of samples to be generated. The basic syntax of the function is as follows: numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)

[0085] S52: Generate grid coordinates. np.meshgrid converts x and y into a grid coordinate matrix. self.X and self.Y are now two-dimensional arrays containing the x and y coordinates of all points on the grid.

[0086] 52: np.meshgrid function: It is a function in the NumPy library that is mainly used to generate a grid point coordinate matrix from a coordinate vector. The np.meshgrid function accepts two or more one-dimensional arrays as input and returns multiple two-dimensional arrays (for two input arrays) or higher-dimensional arrays (for three or more input arrays). These returned arrays represent the coordinates of the grid points formed by all possible combinations of the input arrays.

[0087] S53: Use Kriging interpolation method or tri-spline interpolation method.

[0088] 53: Kriging interpolation method: Ordinary Kriging is a statistical interpolation method that assumes that the data has some spatial autocorrelation (described by variogram model (variogram model is a mathematical model used to describe the degree of variation between random variables in geographic space)), and the Linear model assumes that spatial variability increases linearly with distance and there is no platform, that is, there is no fixed level of spatial variability.

[0089] Three-spline interpolation method: A smooth continuous function curve can be fitted through a given set of scattered data. The basic idea of ​​cubic spline interpolation is to approximate each small interval formed by two adjacent nodes with a low-order polynomial (no more than three times), and to ensure that the function is smooth at the connection of each node, that is, the derivative is continuous. Suppose a set of nodes X:x0,x1,...,xn and a set of corresponding function values ​​Y:y0,y1,...,yn are given on the interval [a,b]. If the function S(x) satisfies the following conditions, then S(x) is called the cubic spline interpolation of the interpolation on the node X and Y:

[0090] In each subinterval (xi,xi+1) (i=0,1,...,n-1), S(x) is a cubic polynomial.

[0091] At each node, S(xi)=yi, i=0,1,...,n.

[0092] S(x) is a quadratic continuously differentiable function on the interval [a,b].

[0093] The mathematical expression is usually:

[0094] [\gamma(h)=C_0+A\cdoth]

[0095] Among them, (\gamma(h)) is the semivariogram, (h) is the spatial distance, (C_0) is the nugget effect, (A) is the linear slope, (h\leqa) is valid, (\gamma(h)) remains unchanged when (h>a). (NuggetEffect: Indicates the variability between data points within a very small distance. It is the variance value when the distance is zero, usually used to indicate the variability of data at the micro level. Same below)

[0096] In the Kriging interpolation method:

[0097] Generally includes the following steps:

[0098] Data preparation and input; semivariogram analysis; determine key parameters; use Kriging interpolation method; set Kriging interpolation method parameters; perform Kriging interpolation operations.

[0099] Collect data: Collect the geospatial data that needs to be interpolated, which usually includes the coordinates of the sampling points.

[0100] Data verification and analysis: Check the completeness and accuracy of the data, delete sampling data points that are obviously deviated from the actual situation, and ensure the reliability of the data.

[0101] Calculate semivariance: The measure of the degree of spatial correlation between points is semivariance. Calculate the distances between all sample points, group these distances, and then calculate the average semivariance value of each group of distances.

[0102] Draw a semivariogram: Based on the calculated semivariance value, draw a distance-semivariance scatter plot, and obtain the theoretical variogram model by fitting.

[0103] Determine model parameters: In the distance-semivariogram, several key parameters need to be determined, such as the nugget, sill, and range.

[0104] According to the characteristics of soil surface data and the analysis purpose of soil surface analysis and monitoring, select the appropriate Kriging interpolation method, such as ordinary Kriging, simple Kriging, and universal Kriging.

[0105] In geographic information system software (such as ArcGIS), set the parameters of Kriging interpolation, including data source, data field, output surface type, transformation type, trend removal order, search neighborhood, etc.

[0106] Perform Kriging interpolation operations, calculate the attribute value of each prediction point according to the set parameters and theoretical variation function model, and generate a prediction surface map.

[0107] Verify the interpolation results to see if they match the actual situation and the expected goal. If the results are not satisfactory, you can adjust the interpolation parameters or reselect the interpolation method and perform the interpolation operation again.

[0108] The interpolation results can be exported into the required format (such as graphs, tables, etc.) and applied to research and analysis in related fields.

[0109] Through the above steps, the Kriging interpolation method can make full use of the autocorrelation of spatial data to generate more accurate and reliable prediction surfaces, providing strong support for the analysis and application of geospatial data.

[0110] The embodiment of the present invention lists the variogram model including Gaussian model, linear model, spherical model, exponential model, etc.:

[0111] Linear model: Assume that spatial variability increases linearly with distance, there is no platform, that is, there is no fixed level of spatial variability. The mathematical expression is usually:

[0112] [\gamma(h)=C_0+A\cdoth]

[0113] Among them, (\gamma(h)) is the semivariogram, (h) is the spatial distance, (C_0) is the nugget effect, (A) is the linear slope, (h\leqa) is valid, (\gamma(h)) remains unchanged when (h>a). (NuggetEffect: Indicates the variability between data points within a very small distance. It is the variance value when the distance is zero, usually used to indicate the variability of data at the micro level. Same below)

[0114] Exponential model: It is assumed that spatial variability gradually decreases with increasing distance and eventually tends to a constant level. The mathematical expression is usually:

[0115] [\gamma(h)=C_0+C\cdot(1-e^{-\frac{h}{a}})]

[0116] Among them, (\gamma(h)) is the semivariogram, (h) is the spatial distance, (C_0) is the nugget effect, (C) is the sill value, and (a) is the range, that is, the distance to reach the sill value.

[0117] (Range: refers to the distance beyond which there is no obvious correlation between data points, also known as influence distance. Same below)

[0118] Gaussian model: Assume that spatial variability increases first and then decreases with distance, forming a bell-shaped curve. The mathematical expression is usually:

[0119] [\gamma(h)=C_0+C\cdot(1-e^{-\frac{h^2}{a^2}})]

[0120] Among them, (\gamma(h)) is the semivariogram, (h) is the spatial distance, (C_0) is the nugget effect, (C) is the sill value (still), and (a) is the range, that is, the distance to reach the sill value. (Sill (sill value): When the distance is greater than the range, the variance function tends to be stable. It reflects the difference between two unrelated samples. When the sill value of the data is 1 or deviates from 1 by 0.3, it indicates that there is a spatial trend between the data. The same below)

[0121] Gaussian Process (GP): Gaussian process is a non-parametric model for regression and classification, which assumes that any finite number of observations obey a multivariate Gaussian distribution. In the Gaussian process, the kernel function (also called the covariance function) is used to describe the correlation between different observations, which usually decreases as the distance between the observations increases.

[0122] In some geospatial analyses, spatial variability may be affected by a variety of factors, including topography, soil type, climate, etc., which may cause the variability to change with distance rather than simply decreasing monotonically.

[0123] Spherical model: Assume that spatial variability increases linearly with distance until it reaches a maximum value and then suddenly drops to zero. The mathematical expression is usually:

[0124] [\gamma(h)=\begin{cases}C_0&\text{if}h=0\C_0+C\left(\fr ac{3h}{2a-\frac{1}{2}\left(\frac{h}{a}\right)^3\right)&\tex t{if}0<h\leqa\C_0+C&\text{if}h> a\end{cases}]

[0125] Among them, (\gamma(h)) is the semivariogram function, (h) is the distance between point pairs, (C_0) is the nugget effect, (C) is the sill value, and (a) is the range, that is, the distance threshold at which the correlation begins to weaken.

[0126] The spherical model, in geostatistics and spatial statistics, is a theoretical model used to describe spatial variability. It assumes that spatial variability increases linearly with distance until it reaches a maximum value (i.e., the radius of the spherical model), and then suddenly drops to zero. This model is often used to describe the structure of spatial autocorrelation, especially in spatial interpolation and geostatistical analysis.

[0127] Nugget effect: reflects the variation characteristics of regionalized variables at a very small scale, that is, the spatial variation of variables at micro distances or zero distances. The existence of nugget effect indicates that there is variability in the variables at the observation scale that cannot be explained by the spatial structure. This variability may come from measurement errors, short-distance variation, random factors or other unknown local processes.

[0128] Sill: It is the value when the variogram or variance function reaches a relatively stable constant. It represents the maximum variation in the system or system attributes.

[0129] Range, also known as sample range or range, is the distance between sampling points when the variogram or semivariogram reaches the base value. It represents the scope of spatial correlation at a certain observation scale.

[0130] Three-spline interpolation method: In mathematics, the cubic spline interpolation function is defined on each small interval. If there are N segments, there are N functions. If there are N+1 data points, there will be a total of N intervals, and there will be a curve divided into N segments. The curve of each segment can be expressed as a cubic polynomial, that is

[0131] S(x)=a_i+b_i(x-x_i)+c_i(x-x_i)^2+d_i(x-x_i)^3

[0132] Where a_i, b_i, c_i, d_i are coefficients.

[0133] S54 outputs the interpolation result. At the end of the interpolation method code, self.Z=grid_values.data is used to extract the interpolation calculation result.

[0134] self.Z=grid_values.data:

[0135] grid_values.data: Indicates access to the data attribute or method of the grid_values ​​object. data contains several or a certain type of data, such as values, arrays, lists, or other data structures.

[0136] self.Z: The self keyword here is used in Python to refer to the instance of the class itself. self.Z = grid_values.data: This line of code assigns the value of grid_values.data to the Z attribute of the instance.

[0137] Example:

[0138] Read the table file (such as Excel) shown in Table 1 and extract the data.

[0139] Group the data into groups of three points.

[0140] Perform three-dimensional interpolation on each set of data.

[0141] Plot the original data points and the interpolated surface.

[0142] Reading Excel Files

[0143] Extract data and convert Pandas DataFrame to Numpy array

[0144] Group the data into a three-dimensional array and get the number of rows of the data;

[0145] Assuming that every three data points form a group, calculate the number of groups;

[0146] Group the data into three rows;

[0147] Initialize an empty Numpy array to store point data;

[0148] Drawing steps:

[0149] Go through each data group and transpose the group data so that each column represents the x, y, and z coordinates;

[0150] Extract the z coordinate value for interpolation;

[0151] Generate 100 points within the x-coordinate range;

[0152] Generate 100 points within the y coordinate range;

[0153] Generate x and y grids;

[0154] Initialize an array Z with the same shape as the X grid to store the interpolation results (fill it with zeros here first);

[0155] Calculate the z-value of each point on the grid using cubic interpolation.

[0156] Then create a new graph and set the following content as needed:

[0157] Add a 3D subplot, draw the original data points, red dots, draw the interpolated surface, use the magma color map, transparency is 0.9, add text labels to each data point, display coordinate values, font size, font color, horizontal alignment, vertical alignment, set the graphic title, display the legend, and display the graphic.

[0158] In the above process, the main steps include:

[0159] Read the Excel file and extract the data. Group the data into groups of three points. Perform 3D interpolation on each group of data. Plot the original data points and the interpolated surface.

[0160] In this example, it is assumed that every three rows of data in the Excel file represent a set of three-dimensional points. The griddata function is used for cubic interpolation. Scatter plots and interpolated surface plots with text labels are drawn.

[0161] like Fig.12 As shown, the data is measured by the optoNCDT1420 laser displacement sensor produced by MICRO-EPSILON and input into the Z data in Table 1.

[0162] Z data refers to lines 3, 6, and 9. Each three lines of data represent the three-axis coordinates of 4*4 measurement points measured in 0.01s.

[0163] Then, the program written is used to execute the method used in the above embodiment to output three images such as Figure 9-11 The picture shown, where rows 1-3 correspond to Fig. 9 , lines 4-6 correspond to Fig.10 , lines 7-9 correspond to Fig.11 .

[0164]

[0165]

[0166] Table 1

[0167] In a target displacement field and spatially varying surface data interpolation and visualization program / software / computer product written in Python or other programming languages, it is possible to choose whether to use the Kriging interpolation method or the tri-spline interpolation method.

[0168] The Kriging interpolation method generates the following results: Fig.13 The result of the three-spline interpolation method is as follows Fig.14 Among them, Kriging is a commonly used interpolation method in geographic surveying, and tri-spline is more commonly used in cardiac angiography.

[0169] It can be seen from the figure above that the maximum point in the Kriging interpolation image is too far away from the interpolation plane, and there is a certain degree of distortion. The result of the three-spline interpolation makes the points strictly fit the plane. Kriging interpolation has many calculation models to choose from, while three-spline only has one calculation model, and the calculation process is simpler than Kriging interpolation.

[0170] The beneficial effects of the patent of the present invention are: a laser array type soil surface displacement field identification method with higher accuracy: the laser array type soil surface displacement field identification method utilizes multiple laser displacement sensors to form a laser array, which can realize high-precision scanning and displacement monitoring of the land surface, which is more accurate than traditional methods.

[0171] Higher speed: The laser array soil surface displacement field identification method can monitor the displacement of the land surface in real time and update the deformation animation every 0.01 seconds, realizing high-frequency displacement monitoring, which is faster than traditional methods.

[0172] Non-contact: The laser array soil surface displacement field identification method uses laser radar equipment for scanning without contacting the land surface, reducing interference with the land and improving safety.

[0173] Wider measurable range: The laser array soil surface displacement field identification method can measure a wider range of curved surfaces and irregularly shaped land deformations, which is more flexible and applicable than traditional methods.

[0174] In addition, this measurement method has good stability, high precision and good application prospects.

[0175] The embodiments and functional operations of the subject matter described in this specification may be implemented in: digital electronic circuitry, tangibly implemented computer software or firmware, computer hardware, including the structures disclosed in this specification and their structural equivalents, or a combination of more than one of the above. The embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more tangible non-transitory program carriers, for execution by a data processing device or to control the operation of a data processing device.

[0176] Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information for transmission to an appropriate receiver device for execution by a data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of the above devices.

[0177] A computer program (which may also be referred to or described as a program, software, software application, module, software module, script or code) may be written in any form of programming language, including compiled or interpreted languages ​​or declarative or procedural languages, and the computer program may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file that stores other programs or data, for example, one or more scripts stored in the following: in a markup language document; in a single file dedicated to a related program; or in multiple collaborative files, for example, files storing one or more modules, subroutines or code portions. A computer program may be deployed to execute on one or more computers, the computers being located in one place or distributed to multiple locations and interconnected by a communication network.

[0178] Although the present invention has been described in detail above by general description, specific implementation methods and experiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A laser array soil surface displacement field identification method, characterized in that: include: Determine a number of observation points based on the laser array and the target on the soil surface, establish a coordinate system with the soil body mark point when there is no load as the origin, and record the original coordinates of each of the observation points; After the soil body is displaced, the laser array records the coordinates of each of the observation points after displacement according to a scanning cycle; Extracting the coordinates of the plurality of observation points after the soil body is displaced according to the target displacement field and the spatial variation surface data of the soil surface; The plurality of coordinates are interpolated and output as a surface using an interpolation method.

2. The laser array soil surface displacement field identification method according to claim 1 is characterized in that: The step of interpolating the plurality of coordinates into a surface by using an interpolation method comprises: Performing semivariogram analysis on the two-dimensional groups of horizontal coordinates and the two-dimensional array of vertical coordinates generated according to the coordinates; Select the Kriging interpolation method and set the interpolation parameters, including data source, data field, output surface type, transformation type, trend removal order, and search neighborhood; Perform interpolation operations, calculate the attribute value of each prediction point according to the set parameters and theoretical variation function model, output it as a surface and generate a prediction surface map.

3. The laser array soil surface displacement field identification method according to claim 2 is characterized in that: The step of performing semivariogram analysis on the plurality of coordinates comprises: Calculate the distances between the coordinates of all the observation points after displacement, group the distances, and then calculate the average semi-variance value of each group of distances; According to the average semivariance value of each group of distances, a distance semivariance scatter plot is drawn, and a theoretical variogram model is obtained by fitting; According to the distance semivariance scatter plot, the nugget value, the sill value and the range are determined.

4. The laser array soil surface displacement field identification method according to claim 2 is characterized in that: The step of interpolating the plurality of coordinates into a surface by an interpolation method also includes: According to the coordinates of the observation points in the same scanning cycle, comparing and obtaining the minimum horizontal coordinate and the maximum horizontal coordinate, the minimum vertical coordinate and the maximum vertical coordinate of all the observation points; According to the minimum horizontal coordinate and the maximum horizontal coordinate, the minimum vertical coordinate and the maximum vertical coordinate as the starting point and the end point, a plurality of equally spaced horizontal coordinates and vertical coordinates are generated, and a horizontal coordinate group number and a vertical coordinate array are generated; The number of groups of horizontal coordinates and the array of vertical coordinates are converted into a coordinate matrix of a grid to obtain the number of two-dimensional groups of horizontal coordinates and the two-dimensional array of vertical coordinates.

5. The laser array soil surface displacement field identification method according to claim 1, characterized in that: The step of interpolating the plurality of coordinates by an interpolation method and outputting them as a surface comprises: Determine N+1 data points based on the two-dimensional array of horizontal coordinates and the two-dimensional array of vertical coordinates generated according to the coordinates; Get N intervals; Obtaining a curve including N line segments according to the N+1 data points and the N intervals, wherein the curve of each segment of the N line segments can be expressed as a cubic polynomial; A surface is generated according to the curve including the N line segments.

6. The laser array soil surface displacement field identification method according to claim 1, characterized in that: The steps of determining several observation points based on the laser array and the targets on the soil surface include: Placing a plurality of laser displacement sensors in evenly arranged mounting holes to form the laser array; The target is placed on the observation point on the soil surface, and the emission direction of the laser displacement sensor is perpendicular to the reflection surface of the target.

7. The laser array soil surface displacement field identification method according to claim 1 is characterized in that: The soil displacement step includes applying load or vibration to the soil.

8. The laser array soil surface displacement field identification method according to claim 1 is characterized in that: The step of interpolating the plurality of coordinates into a surface by using an interpolation method also includes: An interpolation point extraction method is used to extract the interpolation calculation result, and the interpolated points are extracted.

9. The laser array soil surface displacement field identification method according to claim 1, characterized in that: The method further comprises: The plurality of planes are outputted sequentially in the scanning period to form a deformation animation.

10. A laser array soil surface displacement field identification system, characterized in that: The system comprises: a laser array installation device (1), a plurality of laser sensors (2), at least one processor (3), and at least one memory (4), wherein the plurality of laser sensors (2) are electrically connected to the processor (3); the installation device (1) comprises: a plurality of laser sensor installation holes (11), and a carrier (12), wherein the plurality of laser sensor installation holes (11) are evenly distributed on the carrier, and laser sensors (2) are installed in the laser sensor installation holes (11); the memory (4) stores instructions, and when at least one of the processors (3) is used to execute the steps of the method according to any one of claims 1 to 9.

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