CN117635856A - Mine exploitation original digital elevation model reconstruction method, system and medium - Google Patents
Mine exploitation original digital elevation model reconstruction method, system and medium Download PDFInfo
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Abstract
The invention discloses a reconstruction method, a system and a medium of an original digital elevation model for mining, wherein the method comprises the following steps: acquiring an mined mine image; preprocessing the mined mine image to obtain a mined mine digital elevation model; according to the vector surface, performing first erasure processing on a region to be rebuilt of the mine digital elevation model after exploitation to obtain the digital elevation model to be rebuilt; calculating a prediction point table of the area to be rebuilt according to the mine digital elevation model after exploitation; calculating a digital elevation model of the target reconstruction area according to the area to be reconstructed and the prediction point table; and obtaining an original digital elevation model according to the digital elevation model to be reconstructed and the digital elevation model of the target reconstruction area. The invention realizes the reconstruction of the original digital elevation model, improves the efficiency and the accuracy and reduces the cost. The invention can be widely applied to the technical field of digital elevation model reconstruction.
Description
Technical Field
The invention relates to the technical field of digital elevation model reconstruction, in particular to a method, a system and a medium for reconstructing an original digital elevation model of mining.
Background
The Digital Elevation Model (DEM) is a numerical model for generating elevation information representing a terrain surface by collecting ground elevation data and performing digital processing, and provides more abundant and detailed three-dimensional terrain structure information than a traditional terrain map. In areas lacking prior data, complex terrain or dense vegetation coverage, it is difficult to directly acquire the original DEM data, and with current DEMs it is also difficult to reflect the actual terrain information before mining or before mountain excavation. At present, the traditional digital elevation model manufacturing method is time-consuming and labor-consuming, has limited accuracy, and cannot meet the requirements of accurate monitoring and updating of the surface morphology in a dynamic environment in time. In the prior art, the digital elevation model reconstruction method has low efficiency, low accuracy and high cost.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the invention provides a method, a system and a medium for reconstructing an original digital elevation model of mining, which effectively improve efficiency and accuracy and reduce cost.
On one hand, the embodiment of the invention provides a reconstruction method of an original digital elevation model for mining, which comprises the following steps:
acquiring an mined mine image;
preprocessing the mined mine image to obtain a mined mine digital elevation model;
according to the vector surface, performing first erasure processing on a region to be rebuilt of the mine digital elevation model after exploitation to obtain the digital elevation model to be rebuilt;
calculating a prediction point table of the area to be rebuilt according to the mine digital elevation model after exploitation;
calculating a digital elevation model of the target reconstruction area according to the area to be reconstructed and the prediction point table;
and obtaining an original digital elevation model according to the digital elevation model to be reconstructed and the digital elevation model of the target reconstruction area.
In some embodiments, the calculating the prediction point table of the area to be rebuilt according to the mine digital elevation model includes:
calculating a first contour set according to the mined mine digital elevation model, wherein the first contour set comprises a preset number of first contour lines;
and calculating the prediction point table according to the first contour line set.
In some embodiments, the computing a first set of contours from the post-mining mine digital elevation model comprises:
calculating a second contour line set according to the mined mine digital elevation model, wherein the second contour line set comprises the second contour lines with the preset number;
performing second erasure processing on each second contour line in the second contour line set to obtain a corresponding first contour line;
and obtaining the first contour line set according to a plurality of the first contour lines.
In some embodiments, the computing the prediction table from the first set of contours comprises:
carrying out regression processing on each first contour line in the first contour line set to obtain a corresponding prediction point set;
and obtaining the predicted point table according to the predicted point sets.
In some embodiments, the performing step of the regression process includes:
selecting a preset test number of test values according to the first contour line to obtain a test value set;
inputting the test value set into a Gaussian process regression model for prediction to obtain a predicted value set to be selected;
selecting a target predicted value set from the predicted value set to be selected according to the noise intensity;
and combining the test value set and the target predicted value set according to a preset elevation value to obtain the predicted point set.
In some embodiments, the gaussian process regression model is obtained by:
selecting a preset training number of training points from the first contour line to obtain a training point set;
and inputting the training point set into a preset model for training according to a kernel function to obtain the Gaussian process regression model.
In some embodiments, the calculating the digital elevation model of the target reconstruction region according to the region to be reconstructed and the prediction point table includes:
performing interpolation processing on each prediction point set in the prediction point table on the to-be-reconstructed area to obtain a corresponding reconstruction contour line;
and obtaining the digital elevation model of the target reconstruction area according to a plurality of reconstruction contour lines, wherein the area range of the digital elevation model of the target reconstruction area is larger than the area range of the area to be reconstructed.
In another aspect, an embodiment of the present invention provides a system for reconstructing an original digital elevation model of mining, including:
the first module is used for acquiring an after-mining mine image;
the second module is used for preprocessing the mined mine image to obtain a mined mine digital elevation model;
the third module is used for carrying out first erasure processing on the area to be rebuilt of the mine digital elevation model after exploitation according to the vector surface to obtain the digital elevation model to be rebuilt;
the fourth module is used for calculating a prediction point table of the area to be rebuilt according to the mine digital elevation model after exploitation;
a fifth module, configured to calculate a digital elevation model of the target reconstruction area according to the to-be-reconstructed area and the prediction point table;
and a sixth module, configured to obtain an original digital elevation model according to the digital elevation model to be reconstructed and the digital elevation model of the target reconstruction area.
In another aspect, an embodiment of the present invention provides a system for reconstructing an original digital elevation model of mining, including:
at least one memory for storing a program;
at least one processor for loading the program to perform the one mine exploitation raw digital elevation model reconstruction method.
In another aspect, an embodiment of the present invention provides a storage medium having stored therein a computer executable program for implementing the method for reconstructing a mining raw digital elevation model when executed by a processor.
The invention has the following beneficial effects:
according to the method, firstly, the mine image after exploitation is obtained, the mine image after exploitation is preprocessed to obtain the mine digital elevation model after exploitation, then the to-be-rebuilt area of the mine digital elevation model after exploitation is subjected to first erasure processing to obtain the to-be-rebuilt digital elevation model, then the prediction point table of the to-be-rebuilt area is calculated, the digital elevation model of the target rebuilding area is calculated, finally the original digital elevation model is obtained according to the to-be-rebuilt digital elevation model and the digital elevation model of the target rebuilding area, rebuilding of the original digital elevation model is achieved, efficiency and accuracy are improved, and cost is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for reconstructing an original digital elevation model of mining according to an embodiment of the invention;
FIG. 2 is a schematic view of a post-mining digital elevation model obtained by a drone according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a digital elevation model to be reconstructed after erasing an area to be reconstructed according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an embodiment of the present invention for extracting a first contour and a selected test value set;
FIG. 5 is a schematic diagram of a first contour attribute according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an attribute of a test value according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of Gaussian process regression according to an embodiment of the invention;
FIG. 8 is a schematic diagram of a digital elevation model of an interpolated target reconstruction region according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a combined original digital elevation model according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
In the description of the present invention, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number is understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the embodiments of the invention is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
Before further describing embodiments of the present application in detail, the terms and terminology involved in the embodiments of the present application are described as follows:
digital Elevation Model (DEM): the method realizes the digital simulation of the ground topography (namely the digital expression of the topography surface morphology) through the limited topography elevation data, is a solid ground model which represents the ground elevation in the form of a group of ordered value arrays, is a branch of a Digital Topography Model (DTM), and can derive various topography characteristic values. DTM is generally considered to describe the spatial distribution of linear and nonlinear combinations of various topographical factors including elevation, such as slope, slope direction, rate of change of slope, etc., where DEM is a single digital topographical model of zero order, and other topographical characteristics such as slope, slope direction, and rate of change of slope, etc., may be derived based on DEM.
Embodiments of the present application are specifically explained below with reference to the accompanying drawings:
as shown in fig. 1, the embodiment of the invention provides a method for reconstructing an original digital elevation model of mining, which can be applied to a background processor, a server or cloud equipment corresponding to original digital elevation model reconstruction software. During application, the method of the present embodiment includes, but is not limited to, the following steps:
and S11, acquiring an after-mining mine image.
In this embodiment, the unmanned aerial vehicle can be used to carry a high-resolution camera for daytime flight, and a high-resolution post-mining mine image can be acquired by oblique photography. In oblique photography, the image quality and data accuracy of the acquired mine image after mining can be improved by configuring flight parameters according to the topographic features such as the slope height and gradient of the mine. Illustratively, a matrix 600Pro unmanned aerial vehicle can be utilized to carry a Pentmax 645D camera for oblique photographing to acquire high resolution post-mining mine images.
And step S12, preprocessing the mine image after exploitation to obtain a mine digital elevation model after exploitation.
In this embodiment, the post-mining mine image may be imported into the intelligent map software of Xinjiang or other digital image-based photogrammetry software, and the post-mining mine digital elevation model is obtained through image processing, and the post-mining mine digital elevation model results are shown in fig. 2. The post-mining mine digital elevation model can provide the topographic elevation information of the post-mining mine.
And S13, performing first erasure processing on the area to be rebuilt of the mine digital elevation model after exploitation according to the vector surface to obtain the digital elevation model to be rebuilt.
In this embodiment, a vector surface of a to-be-rebuilt area may be firstly drawn by using Geographic Information System (GIS) software or a related algorithm according to boundary information and topographic feature information of a mine area after mining, then the vector surface is overlapped with digital elevation data in a mine digital elevation model after mining, the digital elevation data of the to-be-rebuilt area is erased according to attribute information of the vector surface, and a to-be-rebuilt digital elevation model is obtained, where the to-be-rebuilt area is a high-precision digital elevation Cheng Ouyu before mining, and the obtained to-be-rebuilt digital elevation model is shown in fig. 3. The geographic information system software may include ArcGIS or Global Mapper.
And S14, calculating a prediction point table of the area to be rebuilt according to the mine digital elevation model after exploitation.
In this embodiment, the prediction point table of the area to be rebuilt is calculated according to the post-mining mine digital elevation model, which may be that a first contour set is calculated according to the post-mining mine digital elevation model, the first contour set includes a preset number of first contour lines, and then the prediction point table is calculated according to the first contour set.
In this embodiment, calculating a first contour set from the post-mining digital elevation model includes:
calculating a second contour line set according to the mined mine digital elevation model, wherein the second contour line set comprises a preset number of second contour lines;
performing second erasing treatment on each second contour line in the second contour line set to obtain a corresponding first contour line;
a first set of contours is obtained from the plurality of first contours.
In this embodiment, the first contour set is calculated according to the post-mining mine digital elevation model, and the second contour set may be obtained by first extracting a preset number of second contours from the post-mining mine digital elevation model through GIS software. The extracted second contour lines can reflect the topographical features, the interval density of the second contour lines determines the DEM precision of the area to be reconstructed, and the interval value of the second contour lines can be selected according to actual requirements, so that the preset number is set. And then, on each second contour line in the second contour line set, erasing the contour lines in the range of the area to be reconstructed to obtain corresponding first contour lines, and combining the obtained first contour lines to obtain a first contour line set. The first contour line after erasing is divided into two sections, which are separated by the area to be rebuilt.
In this embodiment, calculating the prediction table according to the first contour set includes:
carrying out regression processing on each first contour line in the first contour line set to obtain a corresponding prediction point set;
and obtaining a predicted point table according to the multiple predicted point sets.
In this embodiment, the prediction point table may be calculated according to the first contour set, by performing regression processing on each first contour in the first contour set to obtain a corresponding prediction point set, and then combining the obtained multiple prediction point sets to obtain the prediction point table.
In the present embodiment, the executing step of the regression process includes:
selecting a preset test number of test values according to the first contour line to obtain a test value set;
inputting the test value set into a Gaussian process regression model for prediction to obtain a predicted value set to be selected;
selecting a target predicted value set from the predicted value set to be selected according to the noise intensity;
and combining the test value set and the target predicted value set according to the preset elevation value to obtain a predicted point set.
In this embodiment, the regression processing may be performed by selecting a predetermined number of test values on the to-be-reconstructed region in the first contour line to obtain a test value set. For example, the preset test number may be set to 50, and 50 test values are selected on the area to be reconstructed in the first Contour line, so as to obtain a test value set t= { T1, T2, T3, …, T50}, the first Contour line and the selected test value set are shown in fig. 4, a part of the attributes of the first Contour line are shown in fig. 5, the attributes include an elevation value (content), a longitude value (x) and a latitude value (y), a part of the attributes of the test values are shown in fig. 6, and the attributes include a longitude value (cx). And then inputting the test value set into a Gaussian process regression model for prediction according to a prediction formula to obtain a predicted value set to be selected, wherein the predicted value set comprises a plurality of predicted values. For example, when the length of the test value set is 50, the test value set is input into a gaussian process regression model to predict according to a prediction formula, so as to obtain a predicted value set p= { P1, P2, P3, …, P50} to be selected, and when the elevation value is 268, the gaussian process regression result is shown in fig. 7. The length of the predicted value set to be selected is equal to the length of the test value set, and the prediction formula is as follows: f (x) to N (μ, σ) 2), μ refers to the expectation of the predictive function value at the test point x position, σ 2 refers to the variance of the predictive function value at the test point x position, and f (x) refers to the conditional distribution of the random variable function f given the test point x. That is, there are different prediction results, such as μ1, σ 1*2, f (x 1) distribution or f (x 2) distribution, for different test points x1, x2, etc. And then comparing the noise intensities sigma n of the multiple predicted values in the predicted value set to be selected through cross verification, and selecting the multiple predicted values with relatively better noise intensities to obtain a target predicted value set. The length of the target predicted value set is smaller than that of the predicted value set to be selected. And finally, according to the preset elevation value, combining the test value set and the target predicted value set to obtain a predicted point set. For example, the preset elevation value z may be set to 268, and when the preset test number is 50, the test value set t= { T1, T2, T3, …, T50} and the target predicted value set p= { P1, P2, P3, …, P50} are combined to obtain the predicted point set { (T1, P1,268), (T2, P2, 268), (T3, P3,268), …, (T50, P50,268) }.
In this embodiment, the gaussian process regression model is obtained by:
selecting a preset training number of training points from the first contour line to obtain a training point set;
and inputting the training point set into a preset model for training according to the kernel function to obtain a Gaussian process regression model.
In this embodiment, the gaussian process regression model may be obtained by first selecting a preset training number of training points from the first contour line to obtain a training point set. For example, the preset training number may be set to 60, and 60 training points are selected from the first contour to obtain training point sets { (x 1, y 1), (x 2, y 2), (x 3, y 3), …, (x 60, y 60) }. And then inputting the training point set into a preset model for training according to the kernel function to obtain a Gaussian process regression model. The preset model may include a gaussian process regression model in Scikit-Learn before training, and the model parameters may include a length scaling parameter l and a kernel variance parameter σf in a kernel function form. In this embodiment, a gaussian process regression model before training is established, and a radial basis function may be used to describe the correlation of the positions of the training points. Assuming that the coordinate value of the training point contains a noise term epsilon, the actual observed value y can be regarded as the actual function value f (x) plus random noise epsilon, and the calculation formula of the actual observed value is as follows: y=f (x) +epsilon. That is, some of the actual observed values y are not determined by the true function value f (x), but are also affected by random noise epsilon. Wherein ε -N (0, σn2), the noise ε follows the normal distribution N (0, σn2), and σn2 represents the variance of the noise. When the value of σn2 is small, it indicates that the noise is not significant, and the actual observed value y is mainly affected by the actual function value f (x). When the sigma n2 value is larger, the actual observed value y is greatly influenced by noise epsilon, and the true value of the function is not clearly resolved. After the noise term is introduced into the observation model, the original Gaussian process model only considers the uncertainty of the function itself. Meanwhile, a Bayesian method is used for updating the Gaussian process model, so that the model simultaneously considers the uncertainty influence of the function value and the noise. The updated Bayesian Gaussian process model can better fit the observation value with noise to enable the contour line to be more in line with actual linear distribution, and the model is used as a Gaussian process regression model before training.
And S15, calculating a digital elevation model of the target reconstruction area according to the area to be reconstructed and the prediction point table.
In this embodiment, the target reconstruction region digital elevation model is calculated according to the region to be reconstructed and the prediction point table, which may be that each prediction point set in the prediction point table is first subjected to interpolation processing on the region to be reconstructed to obtain a corresponding reconstruction contour line, and then the target reconstruction region digital elevation model is obtained according to a plurality of reconstruction contour lines, where the region range of the target reconstruction region digital elevation model is greater than the region range of the region to be reconstructed.
In this embodiment, the predicted points in the predicted point table may be converted into the elevation points of the GIS, each set of predicted points in the predicted point table is subjected to interpolation processing on the to-be-reconstructed area to obtain a corresponding reconstructed contour line, and then multiple reconstructed contour lines are combined into a single dot pattern layer to obtain the digital elevation model of the target reconstruction area, where the result of the digital elevation model of the target reconstruction area is shown in fig. 8. The interpolation processing may include a Kriging algorithm, where the range of the target reconstruction region digital elevation model is greater than the range of the region to be reconstructed.
And S16, obtaining an original digital elevation model according to the digital elevation model to be reconstructed and the digital elevation model of the target reconstruction area.
In this embodiment, the digital elevation model to be rebuilt and the digital elevation model of the target rebuilding area may be overlapped by using GIS software, so as to obtain a high-precision original digital elevation model before mining, where the result of the original digital elevation model is shown in fig. 9. In this embodiment, the region range of the digital elevation model of the target reconstruction area is greater than the region range of the region to be reconstructed, so that smooth transition between the two digital elevation models can be realized in the process of overlapping the digital elevation model to be reconstructed and the digital elevation model of the target reconstruction area.
The embodiment of the invention has the beneficial effects that: according to the embodiment of the invention, the mine image after exploitation is firstly obtained, the mine image after exploitation is preprocessed to obtain the mine digital elevation model after exploitation, then the area to be rebuilt of the mine digital elevation model after exploitation is subjected to first erasure processing to obtain the digital elevation model to be rebuilt, the prediction point table of the area to be rebuilt is calculated, the digital elevation model of the target rebuilding area is calculated, and finally the original digital elevation model is obtained according to the digital elevation model to be rebuilt and the digital elevation model of the target rebuilding area, so that rebuilding of the original digital elevation model is realized, efficiency and accuracy are improved, and cost is reduced.
The embodiment of the invention also provides a system for reconstructing the original digital elevation model of the mining, which comprises the following steps:
the first module is used for acquiring an after-mining mine image;
the second module is used for preprocessing the mine image after exploitation to obtain a mine digital elevation model after exploitation;
the third module is used for carrying out first erasure processing on the area to be rebuilt of the mine digital elevation model after exploitation according to the vector surface to obtain the digital elevation model to be rebuilt;
the fourth module is used for calculating a prediction point table of the area to be rebuilt according to the mine digital elevation model after exploitation;
the fifth module is used for calculating a digital elevation model of the target reconstruction area according to the area to be reconstructed and the prediction point table;
and a sixth module, configured to obtain an original digital elevation model according to the digital elevation model to be reconstructed and the digital elevation model of the target reconstruction area.
The content in the method embodiment is applicable to the system embodiment, the functions specifically realized by the system embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
The embodiment of the invention also provides a system for reconstructing the original digital elevation model of the mining, which comprises the following steps:
at least one memory for storing a program;
at least one processor for loading a program to perform a mine exploitation raw digital elevation model reconstruction method as shown in fig. 1.
The content in the method embodiment is applicable to the system embodiment, the functions specifically realized by the system embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
The embodiment of the invention also provides a storage medium, wherein a computer executable program is stored, and the computer executable program is used for realizing the reconstruction method of the mining original digital elevation model shown in fig. 1 when being executed by a processor.
The content in the method embodiment is applicable to the storage medium embodiment, and functions specifically implemented by the storage medium embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the above embodiment, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and these equivalent modifications and substitutions are intended to be included in the scope of the present invention as defined in the appended claims.
Claims (10)
1. The method for reconstructing the original digital elevation model of the mining is characterized by comprising the following steps of:
acquiring an mined mine image;
preprocessing the mined mine image to obtain a mined mine digital elevation model;
according to the vector surface, performing first erasure processing on a region to be rebuilt of the mine digital elevation model after exploitation to obtain the digital elevation model to be rebuilt;
calculating a prediction point table of the area to be rebuilt according to the mine digital elevation model after exploitation;
calculating a digital elevation model of the target reconstruction area according to the area to be reconstructed and the prediction point table;
and obtaining an original digital elevation model according to the digital elevation model to be reconstructed and the digital elevation model of the target reconstruction area.
2. The method for reconstructing a mine mining original digital elevation model according to claim 1, wherein the calculating a prediction point table of a region to be reconstructed according to the mine mining original digital elevation model comprises:
calculating a first contour set according to the mined mine digital elevation model, wherein the first contour set comprises a preset number of first contour lines;
and calculating the prediction point table according to the first contour line set.
3. A method of reconstructing a mining-induced digital elevation model according to claim 2, wherein said calculating a first set of contours from said post-mining digital elevation model comprises:
calculating a second contour line set according to the mined mine digital elevation model, wherein the second contour line set comprises the second contour lines with the preset number;
performing second erasure processing on each second contour line in the second contour line set to obtain a corresponding first contour line;
and obtaining the first contour line set according to a plurality of the first contour lines.
4. The method for reconstructing a mining original digital elevation model according to claim 2, wherein said calculating said prediction table from said first contour set comprises:
carrying out regression processing on each first contour line in the first contour line set to obtain a corresponding prediction point set;
and obtaining the predicted point table according to the predicted point sets.
5. The method for reconstructing a raw digital elevation model for mining according to claim 4, wherein said regression process is performed by:
selecting a preset test number of test values according to the first contour line to obtain a test value set;
inputting the test value set into a Gaussian process regression model for prediction to obtain a predicted value set to be selected;
selecting a target predicted value set from the predicted value set to be selected according to the noise intensity;
and combining the test value set and the target predicted value set according to a preset elevation value to obtain the predicted point set.
6. The method for reconstructing a mining original digital elevation model according to claim 5, wherein the gaussian process regression model is obtained by:
selecting a preset training number of training points from the first contour line to obtain a training point set;
and inputting the training point set into a preset model for training according to a kernel function to obtain the Gaussian process regression model.
7. The method for reconstructing a mining original digital elevation model according to claim 1, wherein calculating a target reconstruction region digital elevation model according to the region to be reconstructed and the prediction point table comprises:
performing interpolation processing on each prediction point set in the prediction point table on the to-be-reconstructed area to obtain a corresponding reconstruction contour line;
and obtaining the digital elevation model of the target reconstruction area according to a plurality of reconstruction contour lines, wherein the area range of the digital elevation model of the target reconstruction area is larger than the area range of the area to be reconstructed.
8. A mining original digital elevation model reconstruction system, comprising:
the first module is used for acquiring an after-mining mine image;
the second module is used for preprocessing the mined mine image to obtain a mined mine digital elevation model;
the third module is used for carrying out first erasure processing on the area to be rebuilt of the mine digital elevation model after exploitation according to the vector surface to obtain the digital elevation model to be rebuilt;
the fourth module is used for calculating a prediction point table of the area to be rebuilt according to the mine digital elevation model after exploitation;
a fifth module, configured to calculate a digital elevation model of the target reconstruction area according to the to-be-reconstructed area and the prediction point table;
and a sixth module, configured to obtain an original digital elevation model according to the digital elevation model to be reconstructed and the digital elevation model of the target reconstruction area.
9. A mining original digital elevation model reconstruction system, comprising:
at least one memory for storing a program;
at least one processor for loading the program to perform a method of reconstruction of a mining raw digital elevation model according to any one of claims 1-7.
10. A storage medium having stored therein a computer executable program for implementing a method of reconstructing a mine exploitation raw digital elevation model according to any one of claims 1 to 7 when executed by a processor.
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