CN110737652B - Data cleaning method and system for three-dimensional digital model of surface mine and storage medium - Google Patents
Data cleaning method and system for three-dimensional digital model of surface mine and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a system and a storage medium for cleaning data of a three-dimensional digital model of a surface mine, wherein the method comprises the following steps: acquiring a surface elevation value of a surface mine area, and then establishing an elevation matrix of the surface mine area; the surface mine area comprises equipment and a pit area where the equipment is located, and each matrix element in the elevation matrix corresponds to a position coordinate and an elevation value of each position in the surface mine mountain area; then, standard deviation sampling is carried out on the elevation matrix to obtain a standard deviation sampling matrix; converting the standard deviation sampling matrix by adopting a top hat algorithm to obtain a foreground and background separation matrix, wherein the equipment is regarded as a foreground, and a pit area where the equipment is located is regarded as a background; then, acquiring matrix elements corresponding to the equipment by using the foreground and background separation matrix, and acquiring position coordinates and elevation values of the equipment according to the matrix elements; and finally, carrying out interpolation replacement on the elevation value of the equipment by adopting an interpolation method according to the position of the equipment. The invention can identify the equipment and wash the data thereof by the method.
Description
Technical Field
The invention belongs to the field of open-pit mining, and particularly relates to a method and a system for cleaning data of a three-dimensional digital model of an open-pit mine and a storage medium.
Background
In the field of open-pit mining, the digitization, automation and informatization of mines are realized by utilizing a computer technology, and the digitization technology is applied to the production process of open-pit mining, so that the production efficiency and the safety level are improved. In the digital construction process of the surface mine, the three-dimensional modeling of the surface mine can effectively guide the formulation of a production plan and the production management work, and is the basis for realizing informatization and intellectualization of the surface mine. Therefore, achieving rapid and accurate three-dimensional modeling of surface mines is a crucial research topic in mining.
However, although the three-dimensional digital model obtained by the digital surface model obtaining method (global positioning system real-time dynamic measurement, unmanned airborne laser radar measurement, unmanned aerial vehicle oblique photogrammetry, etc.) commonly used today can highly restore the original appearance of the mine, the data volume of the model is large and contains a large amount of interference information, such as spatial information of mining equipment, spatial information of mine structures, etc. Due to these interferences, when the surface pit three-dimensional digital model is used for subsequent planning and mining design, the surface pit terrain control line is often manually depicted and extracted to optimize the model. The labor intensity of the work is high, the work period is long, and therefore an automatic method is needed for realizing the identification, the positioning and the filtering of the mining equipment in the three-dimensional digital model of the surface mine.
Disclosure of Invention
The invention aims to provide a data cleaning method, a system and a storage medium for a three-dimensional digital model of a surface mine, which can automatically identify the position of mining equipment in the three-dimensional digital model of the surface mine and filter the mining equipment.
The invention provides a data cleaning method of a three-dimensional digital model of a surface mine, which comprises the following steps:
step S1: acquiring a surface elevation value of a surface mine area based on a three-dimensional digital model of the surface mine, and then establishing an elevation matrix of the surface mine area;
the surface mine area comprises equipment and a pit area where the equipment is located, and each matrix element in the elevation matrix corresponds to a position coordinate and an elevation value of each position in the surface mine mountain area;
step S2: performing standard deviation sampling on the elevation matrix to obtain a standard deviation sampling matrix;
step S3: converting the standard deviation sampling matrix by adopting a top hat algorithm to obtain a foreground and background separation matrix, wherein the equipment is regarded as a foreground, and a pit area where the equipment is located is regarded as a background;
step S4: acquiring matrix elements corresponding to the equipment by using the foreground and background separation matrix, and acquiring position coordinates and elevation values of the equipment according to the matrix elements;
step S5: and carrying out interpolation replacement on the elevation value of the equipment by adopting an interpolation method according to the position of the equipment.
The method provided by the invention realizes the cleaning of the equipment data in the three-dimensional digital model based on the condition that the equipment data is interfered in the mine field, so that the three-dimensional digital model can more accurately restore the original appearance of the mine. On one hand, the data cleaning is realized based on the three-dimensional model of the elevation matrix, compared with the conventional TIN model (triangulation network model), the data cleaning method is stronger in data edibility and easier to realize automation of data cleaning, and the triangulation network model is higher in data storage efficiency, but is difficult to edit and low in automation degree; on the other hand, the invention discovers that the terrain change of the pit area of the surface mine is smooth and regular based on research, the dispersion of elevation data is small, the edge elevation of the equipment area is suddenly changed, and the dispersion of the elevation data is large, therefore, the invention skillfully utilizes a standard deviation sampling matrix to carry out preliminary separation on equipment and the pit area based on the special terrain of the surface mine, and the standard deviation data obtained from the equipment area is generally much larger than the standard deviation value of the pit area, thereby realizing the preliminary separation; meanwhile, the difference between the slope terrain in the mine area and the equipment cannot be accurately distinguished only by using the standard deviation, so that the equipment is further separated from the mine area by using the top cap algorithm, and the reliability of the finally identified equipment position is improved.
In summary, the values of the device and the pit area in the standard deviation sampling matrix and the foreground-background separation matrix are greatly different, so that the device and the pit area can be effectively distinguished, and the matrix elements corresponding to the device are extracted.
Further preferably, the step S2 of performing standard deviation sampling on the elevation matrix to obtain a standard deviation sampling matrix includes: sliding a sampling core C in the elevation matrix and acquiring element values in a standard deviation sampling matrix;
wherein, one element value in the standard deviation sampling matrix is calculated according to a standard deviation formula every time sliding is performed, and the standard deviation formula is as follows:
in the formula, SNRepresenting one element value, C, in the sample matrix of standard deviation corresponding to the current slidingijThe element values of the ith row and the jth column in the sampling core C in the current sliding are represented,
and representing the average value of elements in the sampling core C in the current sliding, wherein the size of the sampling core C is mxn, and the element value in the sampling core C in the sliding process is the element value of the sampling core C in the area of the elevation matrix.
The sliding step of the sampling core C is, for example, m × n, but the value is not specifically limited in the present invention.
Further preferably, in step S3, the standard deviation sampling matrix is converted to obtain a foreground-background separation matrix according to the following formula:
wherein, the structural elements of B (i, j) are as follows:
wherein T (X, Y) represents a foreground-background separation matrix, S (X, Y) represents a standard deviation sampling matrix, theta represents corrosion operation,
indicating the number of expansion operations, X,y represents a position coordinate, X is a horizontal axis, and Y is a vertical axis.
The top cap algorithm can increase the contrast of the equipment and the pit area, so that the discrete degree is further enlarged, and the equipment and the slope can be effectively distinguished.
Further preferably, the process of identifying the matrix element corresponding to the device by using the foreground-background separation matrix in step S4 is as follows: carrying out binarization processing based on the foreground and background separation matrix to obtain a binarization matrix, and then identifying matrix elements corresponding to equipment;
wherein, a threshold value of binarization processing is determined by adopting a maximum inter-class variance method;
and the elements larger than the threshold are matrix elements corresponding to the equipment, and the elements smaller than the threshold are matrix elements corresponding to the pit area where the equipment is located.
Further preferably, before the binarizing by using the foreground and background separation matrix to obtain the binarized matrix, the method further includes: regularizing the foreground and background separation matrix to obtain a regularized matrix;
wherein, the regularization treatment is carried out according to the following formula:
in the formula (I), the compound is shown in the specification,
representing a regularization matrix TNThe value of the element in the ith row and jth column in (X, Y), TijThe element values T of the ith row and the jth column in the foreground and background separation matrixmin、TmaxRespectively the minimum value and the maximum value in the foreground-background separation matrix.
Further preferably, the process of determining the threshold value of the binarization processing by using the maximum inter-class variance method is as follows:
firstly, sequentially taking values in the element value range of a foreground and background separation matrix or a regularization matrix as a threshold value t, dividing the matrix into a foreground class interval and a background class interval according to the element value, and calculating each intervalVariance σ corresponding to threshold t2(t), wherein the element value range of the foreground class interval is [0, t ]]The element value range of the background class interval is [ t +1, L-1 ]]L-1 is the maximum value of the element values of the foreground and background separation matrix or the regularization matrix;
then, the variance σ is selected2(t) the maximum threshold value t is used as the threshold value of the binarization processing;
the variance σ2The calculation formula of (t) is as follows:
σ2(t)＝ω0(μ0-μ)2+ω1(μ1-μ)2
in the formula, ω0、ω1The occurrence probabilities of the foreground class and the background class are respectively equal to the ratio of the number of elements in the foreground class interval, the number of elements in the background class interval and the total number of elements in the foreground and background separation matrix or the regularization matrix;
μ0、μ1and mu is the mean value of the element values in the foreground class interval, the mean value of the element values in the background class interval, and the mean value of the element values in the foreground-background separation matrix or the regularization matrix respectively.
It should be understood that the larger the corresponding variance, the better the separation.
Further preferably, after the binarization processing is performed based on the foreground-background separation matrix to obtain a binarization matrix, the method further includes: and (4) up-sampling the binary matrix to the size of the elevation matrix, and then identifying matrix elements corresponding to the equipment.
Preferably, in step S5, performing interpolation replacement on the elevation value of the device by using an inverse distance weighted interpolation method, where a sampling interpolation window is set at the location of the device, a central point of the sampling interpolation window corresponds to the location of the device, and a sampling point other than the central point is a pit area point adjacent to the device;
the calculation formula of the inverse distance weighted interpolation is as follows:
wherein Z is an elevation value after interpolation replacement when the equipment is taken as a point to be interpolated, and Z isiIs the elevation of the sampling point, diThe distance between the point to be interpolated and the sampling point,
and n is the number of sampling points in the sampling interpolation window.
In another aspect, the present invention provides a system based on the above method, including:
an elevation matrix construction module: the method comprises the steps of obtaining a surface elevation value of a surface mine area based on a surface mine three-dimensional digital model, and then establishing an elevation matrix of the surface mine area;
a standard deviation sampling matrix construction module: the standard deviation sampling device is used for sampling the standard deviation of the elevation matrix to obtain a standard deviation sampling matrix;
the foreground and background separation matrix construction module: the foreground and background separation matrix is obtained by converting the standard deviation sampling matrix by adopting a top hat algorithm;
the equipment extraction module is used for acquiring matrix elements corresponding to the equipment by using the foreground and background separation matrix and acquiring the position coordinates and the elevation values of the equipment according to the matrix elements;
and the interpolation module is used for carrying out interpolation replacement on the elevation value of the equipment by adopting an interpolation method according to the position of the equipment.
Furthermore, the present invention also provides a storage medium storing computer program instructions which, when executed by a terminal device, cause the terminal device to perform the method of the claims above.
Advantageous effects
The method provided by the invention realizes automatic cleaning of the equipment data in the three-dimensional digital model based on the condition that the equipment data is interfered in the mine field, so that the three-dimensional digital model can more accurately restore the original appearance of the mine.
According to the method, the local elevation change of the mine pit is inspected firstly, the position of the equipment is preliminarily determined through local standard deviation sampling, the terrain change of the mine pit area of the open-pit mine is found to be smooth and regular based on research, the dispersion degree of elevation data is small, the edge elevation of the equipment area is suddenly changed, the dispersion degree of the elevation data is large, and further the preliminary separation of the equipment and the mine pit area can be realized by utilizing the standard deviation; and then, equipment and the pit are further separated by using a top cap algorithm in mathematical morphology, and particularly, slope terrain and equipment in a mine area can be effectively distinguished, so that the reliability of final equipment position identification is improved, and the reliability of data cleaning is improved.
Drawings
Fig. 1 is a schematic flow chart of a data cleaning method for a three-dimensional digital model of a surface mine according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a three-dimensional digital model and a corresponding ortho-image of a surface mine according to an embodiment of the present invention, wherein (a) is an ortho-image of the surface mine, and (b) is a three-dimensional digital surface model of the surface mine, and a white frame is selected where mining equipment is located;
FIG. 3 is a schematic diagram of a local standard deviation sample S (X, Y) according to an embodiment of the present invention;
fig. 4 is a schematic diagram of the foreground and background separation matrix T (X, Y) provided in the embodiment of the present invention;
fig. 5 is a schematic diagram of a binarization matrix B (X, Y) according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an upsampling method provided by an embodiment of the present invention;
fig. 7 is a schematic diagram illustrating an effect of performing interpolation replacement on the elevation of the device based on an inverse distance weighted interpolation method according to the embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples.
The invention provides a data cleaning method of a three-dimensional digital model of an open-pit mine, which aims to extract and filter main interference information in the three-dimensional digital model, namely spatial information of mining equipment, to obtain the three-dimensional digital model only containing ground elevation information of a pit area, and is particularly used for identifying, positioning and filtering the mining equipment in the three-dimensional digital model of the open-pit mine, namely effectively filtering elevation value data of the equipment, eliminating the interference of the equipment and reducing the original appearance of the mine to a higher degree.
As shown in fig. 1, the method for cleaning data of a three-dimensional digital model of a surface mine according to an embodiment of the present invention includes the following steps:
step 101: the surface elevation value of the surface mine area is obtained based on a surface mine three-dimensional digital model (DSM). Wherein the surface mine area includes equipment and a pit area in which the equipment is located. The specific process is as follows: the method comprises the steps of establishing a surface mine DSM (digital surface model) based on measurement technologies such as oblique photogrammetry technology or unmanned aerial vehicle-mounted LiDAR measurement technology; then acquiring three-dimensional point cloud information of the equipment and a pit area where the equipment is located based on a three-dimensional digital model DSM of the surface mine; and determining the surface elevation value of the surface mine area based on the equipment and the three-dimensional point cloud information of the pit area where the equipment is located. As shown in fig. 2, (a) is an orthographic view of a surface mine, and (b) is a surface mine DSM in which the white boxes are the locations of mining equipment.
Step 102: and constructing equipment and an elevation matrix Z (X, Y) of the pit area where the equipment is located based on the surface elevation values of the surface mine area. And each matrix element in the elevation matrix corresponds to the position coordinate and the elevation value of the equipment and each position in the pit area where the equipment is located. Z (X, Y) represents an elevation Z with position coordinates (X, Y), and XY is a Cartesian coordinate system, i.e., X is the horizontal axis and Y is the vertical axis.
Step 103: the elevation matrix Z (X, Y) is subjected to standard deviation sampling by using a local standard deviation sampling algorithm to obtain a standard deviation sampling matrix S (X, Y), as shown in fig. 3.
Sliding a sampling core C in an elevation matrix and acquiring element values in a standard deviation sampling matrix; calculating one element value in the standard deviation sampling matrix according to a standard deviation formula once per sliding, wherein the standard deviation formula is as follows:
in the formula，SNRepresenting one element value, C, in the sample matrix of standard deviation corresponding to the current slidingijThe element values of the ith row and the jth column in the sampling core C in the current sliding are represented,
and representing the average value of elements in the sampling core C in the current sliding, wherein the size of the sampling core C is mxn, and the element value in the sampling core C in the sliding process is the element value of the sampling core C in the area of the elevation matrix. In this embodiment, the sliding sequence is from left to right, from top to bottom, and the sliding step length is mxn, so as to construct the standard deviation sampling matrix S (X, Y).
From the above, the size of the standard deviation matrix S (X, Y) is smaller than the size of the elevation matrix Z (X, Y) of the equipment and the pit area in which the equipment is located.
Step 104: and converting the standard deviation sampling matrix by adopting a top hat algorithm to obtain a foreground and background separation matrix T (X, Y), wherein the equipment is taken as a foreground, and a pit area where the equipment is located is taken as a background. Fig. 4 shows the foreground-background separation matrix T (X, Y).
The top cap algorithm corresponds to the following formula:
wherein, the structural elements of B (i, j) are as follows:
wherein T (X, Y) represents a foreground-background separation matrix, S (X, Y) represents a standard deviation sampling matrix, theta represents corrosion operation,
indicating the dilation operation.
Step 105: regularizing the foreground-background separation matrix T (X, Y) to obtain a regularization matrix TN(X, Y) and applying the maximum between-class varianceNormal regularization matrix TNAnd (X, Y) performing binarization conversion to obtain a binarization matrix B (X, Y) of the foreground and background separation matrix. As shown in fig. 5.
Wherein, the regularization treatment is carried out according to the following formula:
in the formula (I), the compound is shown in the specification,
representing a regularization matrix TNThe value of the element in the ith row and jth column in (X, Y), TijThe element values T of the ith row and the jth column in the foreground and background separation matrixmin、TmaxRespectively the minimum value and the maximum value in the foreground-background separation matrix.
As can be seen from the above regularization formula, the values of the elements in the regularization matrix are all within the interval of 0 to 255.
And determining a threshold value of binarization processing by adopting a maximum inter-class variance method, wherein the implementation process comprises the following steps:
first, regularization matrix TNSequentially taking values in the element value range of (X, Y) as threshold values t, dividing the matrix into foreground intervals and background intervals according to the element values, and calculating the variance sigma corresponding to each threshold value t2(t), wherein the element value range of the foreground class interval is [0, t ]]The element value range of the background class interval is [ t +1, L-1 ]]L-1 is the maximum value of the element values of the foreground and background separation matrix or the regularization matrix;
then, the variance σ is selected2(t) the maximum threshold value t is used as the threshold value for the binarization process.
The formula is as follows:
Th＝argmax{σ2(t)}；σ2(t)＝ω0(μ0-μ)2+ω1(μ1-μ)2
where Th is the threshold value of the binarization process, ω0、ω1Are respectively foreground class and background classThe occurrence probability of (2) is respectively equal to the ratio of the number of elements positioned in the foreground class interval, the number of elements positioned in the background class interval and the total number of elements in the regularization matrix; mu.s0、μ1And mu are the mean value of the element values in the foreground class interval, the mean value of the element values in the background class interval and the mean value of the element values in the regularization matrix respectively.
The binarization process in this embodiment is as follows: regularizing matrix TNIn (X, Y), the label whose element value is greater than the threshold Th is 1, and the label whose element value is less than the threshold Th is 0, to obtain the binary matrix B (X, Y). In this embodiment, the binarization processing is performed based on the regularization matrix, and in other feasible embodiments, the binarization processing may be performed based on the foreground-background separation matrix.
Step 106: and (3) upsampling the binarization matrix B (X, Y) to the size of the elevation matrix Z (X, Y), and extracting matrix elements with elements of 1 in the matrix B (X, Y), thereby acquiring the coordinates (X, Y) of the equipment and the elevation value Z.
As shown in fig. 6, in this embodiment, the upsampling method is as follows: taking an example of upsampling a matrix with the size of 2 × 2 to a matrix with the size of 6 × 6, taking each element in the original 2 × 2 matrix as a center, constructing a 6 × 6 sparse matrix, filling other element values in the sparse matrix with each center point value, and obtaining the matrix, namely the 6 × 6 upsampling matrix.
Step 107: and acquiring elevation values of the equipment and the pit areas adjacent to the equipment according to the equipment coordinates, and performing interpolation replacement on the elevation values of the equipment by taking the elevation values of the pit areas adjacent to the equipment as a reference based on an inverse distance weighting interpolation method.
The method comprises the steps that a sampling interpolation window is arranged at the position of equipment, the central point of the sampling interpolation window corresponds to the position of the equipment, and the sampling point of a non-central point is a pit area point adjacent to the equipment. The method specifically comprises the following steps: acquiring an edge elevation value of the equipment, and taking the edge elevation value as a point to be interpolated; constructing a sampling interpolation window by taking the elevation value of the edge of the equipment as a center, and searching the neighborhood of the sampling interpolation window; and performing inverse distance weighted interpolation calculation on the obtained sampling points to replace the elevation value of the equipment. In this embodiment, the sampling interpolation window size is 3 × 3.
The calculation formula of the inverse distance weighted interpolation is as follows:
wherein Z is an elevation value after interpolation replacement when the equipment is taken as a point to be interpolated, and Z isiIs the elevation of the sampling point, diThe distance between the point to be interpolated and the sampling point,
and n is the number of sampling points in the sampling interpolation window. In this embodiment, the distance d can be simplified to be the distance from each point in the 3 x 3 square window to the center point, i.e. 1 or
The weight u is typically taken to be 2.
It should be understood that the invention identifies the position of the equipment and replaces the elevation value of the equipment, so that the data in the three-dimensional digital model of the surface mine is the data of the pit area, and the pit can be restored more truly.
The mining equipment can be accurately and effectively detected for different mines and different scenes in different mines; the algorithm is stable, and the system is convenient to overhaul and maintain. According to the embodiment of the invention, the local elevation change of a mine pit is considered firstly, and the position of equipment is preliminarily determined through sampling of local standard deviation; then separating the equipment from the pit by using a top cap algorithm in mathematical morphology; and further separating the equipment by a maximum inter-class variance method, filtering noise points and finally extracting the coordinates of the equipment. According to the invention, the mining equipment is extracted for multiple times, so that the accuracy of extraction in the equipment can be ensured. And then, carrying out interpolation replacement on the elevation of the equipment by an inverse distance weighting interpolation method so as to enable the equipment to interfere with data.
Based on the method, the embodiment of the invention also provides a data cleaning system of the three-dimensional digital model of the surface mine, which comprises the following steps:
an elevation matrix construction module: the method comprises the steps of obtaining a surface elevation value of a surface mine area based on a surface mine three-dimensional digital model, and then establishing an elevation matrix of the surface mine area;
a standard deviation sampling matrix construction module: the standard deviation sampling device is used for sampling the standard deviation of the elevation matrix to obtain a standard deviation sampling matrix;
the foreground and background separation matrix construction module: the foreground and background separation matrix is obtained by converting the standard deviation sampling matrix by adopting a top hat algorithm;
a regularization module: and the regularization matrix is obtained by regularizing the foreground and background separation matrix.
And the equipment extraction module is used for acquiring matrix elements corresponding to the equipment by using the foreground and background separation matrix and then acquiring the position coordinates and the elevation values of the equipment according to the matrix elements. In this embodiment, a maximum inter-class variance method is specifically adopted to determine a threshold value of binarization processing, construct a binarization matrix, and then obtain a device position coordinate and an elevation value.
And the interpolation module is used for carrying out interpolation replacement on the elevation value of the equipment by adopting an interpolation method according to the position of the equipment.
It should be understood that: the functional unit modules in the embodiments of the present invention may be integrated into one processing unit, or each unit module may exist alone physically, or two or more unit modules are integrated into one unit module, and may be implemented in a form of hardware or software. For the specific implementation process of each model, reference is made to the method content, which is not described in detail in the present invention.
The present invention also provides a storage medium storing computer program instructions that, when executed by a terminal device, cause the terminal device to execute the method for cleaning data of a three-dimensional digital model of a surface mine.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the invention is not to be limited to the examples described herein, but rather to other embodiments that may be devised by those skilled in the art based on the teachings herein, and that various modifications, alterations, and substitutions are possible without departing from the spirit and scope of the present invention.
Claims (10)
1. A data cleaning method of a three-dimensional digital model of a surface mine is characterized by comprising the following steps: the method comprises the following steps:
step S1: acquiring a surface elevation value of a surface mine area based on a three-dimensional digital model of the surface mine, and then establishing an elevation matrix of the surface mine area;
the surface mine area comprises equipment and a pit area where the equipment is located, and each matrix element in the elevation matrix corresponds to a position coordinate and an elevation value of each position in the surface mine mountain area;
step S2: performing standard deviation sampling on the elevation matrix to obtain a standard deviation sampling matrix, wherein a sampling core C slides in the elevation matrix and obtains element values in the standard deviation sampling matrix, one element value in the standard deviation sampling matrix is calculated according to a standard deviation formula every time the sampling core C slides once, and the size of the sampling core C is mxn;
step S3: converting the standard deviation sampling matrix by adopting a top hat algorithm to obtain a foreground and background separation matrix, wherein the equipment is regarded as a foreground, and a pit area where the equipment is located is regarded as a background;
step S4: acquiring matrix elements corresponding to the equipment by using the foreground and background separation matrix, and acquiring position coordinates and elevation values of the equipment according to the matrix elements;
step S5: and carrying out interpolation replacement on the elevation value of the equipment by adopting an interpolation method according to the position of the equipment.
2. The method of claim 1, wherein: for the standard deviation sampling matrix of step S2, the standard deviation formula is as follows:
in the formula, SNRepresenting one element value, C, in the sample matrix of standard deviation corresponding to the current slidingijThe element values of the ith row and the jth column in the sampling core C in the current sliding are represented,
and representing the average value of the elements in the sampling kernel C in the current sliding process, wherein the element value in the sampling kernel C in the sliding process is the element value of the area of the sampling kernel C in the elevation matrix.
3. The method of claim 1, wherein: in step S3, the standard deviation sampling matrix is converted to obtain a foreground-background separation matrix according to the following formula:
wherein, the structural elements of B (i, j) are as follows:
wherein T (X, Y) represents a foreground-background separation matrix, S (X, Y) represents a standard deviation sampling matrix, theta represents corrosion operation,
indicating the expansion operation, X, Y indicating the position coordinates, X the horizontal axis, Y the vertical axis.
4. The method of claim 1, wherein: the process of identifying the matrix element corresponding to the device using the foreground-background separation matrix in step S4 is as follows: carrying out binarization processing based on the foreground and background separation matrix to obtain a binarization matrix, and then identifying matrix elements corresponding to equipment;
wherein, a threshold value of binarization processing is determined by adopting a maximum inter-class variance method;
the elements larger than or equal to the threshold are matrix elements corresponding to the equipment, and the elements smaller than the threshold are matrix elements corresponding to the pit area where the equipment is located.
5. The method of claim 4, wherein: before the binarization processing is carried out by using the foreground and background separation matrix to obtain the binarization matrix, the method also comprises the following steps: regularizing the foreground and background separation matrix to obtain a regularized matrix;
wherein, the regularization treatment is carried out according to the following formula:
in the formula (I), the compound is shown in the specification,
representing a regularization matrix TNThe value of the element in the ith row and jth column in (X, Y), TijThe element values T of the ith row and the jth column in the foreground and background separation matrixmin、TmaxRespectively the minimum value and the maximum value in the foreground-background separation matrix.
6. The method according to claim 4 or 5, characterized in that: the process of determining the threshold value of the binarization processing by adopting the maximum inter-class variance method is as follows:
firstly, values are sequentially taken as threshold values t in the element value range of a foreground and background separation matrix or a regularization matrix, the matrix is divided into a foreground class interval and a background class interval according to the element values, and then the variance sigma corresponding to each threshold value t is calculated2(t), wherein the element value range of the foreground class interval is [0, t ]]The element value range of the background class interval is [ t +1, L-1 ]]L-1 is the maximum value of the element values of the foreground and background separation matrix or the regularization matrix;
then, the variance σ is selected2(t) the maximum threshold value t is used as the threshold value of the binarization processing;
the variance σ2The calculation formula of (t) is as follows:
σ2(t)＝ω0(μ0-μ)2+ω1(μ1-μ)2
in the formula, ω0、ω1The occurrence probabilities of the foreground class and the background class are respectively equal to the ratio of the number of elements in the foreground class interval, the number of elements in the background class interval and the total number of elements in the foreground and background separation matrix or the regularization matrix;
μ0、μ1and mu is the mean value of the element values in the foreground class interval, the mean value of the element values in the background class interval, and the mean value of the element values in the foreground-background separation matrix or the regularization matrix respectively.
7. The method of claim 4, wherein: after carrying out binarization processing on the basis of the foreground and background separation matrix to obtain a binarization matrix, the method further comprises the following steps: and (4) up-sampling the binary matrix to the size of the elevation matrix, and then identifying matrix elements corresponding to the equipment.
8. The method of claim 1, wherein: in the step S5, performing interpolation replacement on the elevation value of the device by using an inverse distance weighted interpolation method, wherein a sampling interpolation window is set at the position of the device, the central point of the sampling interpolation window corresponds to the device position, and the sampling point of the non-central point is a pit area point adjacent to the device;
the calculation formula of the inverse distance weighted interpolation is as follows:
wherein Z is an elevation value after interpolation replacement when the equipment is taken as a point to be interpolated, and Z isiIs the elevation of the sampling point, diThe distance between the point to be interpolated and the sampling point,
and n is the number of sampling points in the sampling interpolation window.
9. A system based on the method of any one of claims 1-8, characterized by: the method comprises the following steps:
an elevation matrix construction module: the method comprises the steps of obtaining a surface elevation value of a surface mine area based on a surface mine three-dimensional digital model, and then establishing an elevation matrix of the surface mine area;
a standard deviation sampling matrix construction module: the standard deviation sampling device is used for sampling the standard deviation of the elevation matrix to obtain a standard deviation sampling matrix;
the foreground and background separation matrix construction module: the foreground and background separation matrix is obtained by converting the standard deviation sampling matrix by adopting a top hat algorithm;
the equipment extraction module is used for acquiring matrix elements corresponding to the equipment by using the foreground and background separation matrix and acquiring the position coordinates and the elevation values of the equipment according to the matrix elements;
and the interpolation module is used for carrying out interpolation replacement on the elevation value of the equipment by adopting an interpolation method according to the position of the equipment.
10. A storage medium, characterized by: storing computer program instructions which, when executed by a terminal device, cause the terminal device to perform the method of any one of claims 1 to 8.
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