A smart sampling method and system for waste soil in mining areas

Abstract

This invention relates to the field of data processing technology, specifically to an intelligent sampling method and system for abandoned soil in mining areas. The method includes: determining the multidimensional geological characteristics and geological fault depths of each initial location point in the abandoned soil area; determining the degree of fault influence on each initial location point based on the relationship between the geological fault depth and the target sampling depth; determining the probability of each initial location point being a representative location point based on the overall level of influence within the neighborhood of each initial location point, as well as the differences in influence and multidimensional geological characteristics between each initial location point and other initial location points within its preset neighborhood; and selecting representative location points from the initial location points based on the probability, significantly improving the objectivity and accuracy of representative location point selection, thereby improving the accuracy of grid side lengths and ultimately enhancing the sampling reliability of abandoned soil in mining areas.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an intelligent sampling method and system for abandoned soil in mining areas. Background Technology

[0002] In the sampling process of abandoned soil in mining areas, the grid method is commonly used for geological sampling. This involves deploying a grid across the exploration area and performing geological sampling and analysis at the grid nodes. However, the grid method has several drawbacks. Due to the relatively rich and diverse geological structures of abandoned soil in mining areas, the grid side lengths often need to be manually set. If the side lengths are too large, some geological features within the mining area may be lost, resulting in low representativeness of the sampling results. Conversely, if the side lengths are too small, there will be too many duplicate features, reducing sampling efficiency. To address the inherent subjectivity of manually setting grid side lengths, the current common approach is to establish multiple initial location points within the abandoned soil area and then select representative location points from these initial points. The grid side length is then determined based on these representative location points. However, the current method for selecting representative location points typically involves determining the difference between the geological information of each initial location point and the preset standard geological information, and then determining the representative location point based on the magnitude of this difference. Firstly, in this method, it is difficult to determine the preset standard geological information, which is prone to deviation. Moreover, it is impossible to make lateral comparisons between initial location points, which affects the accuracy of obtaining representative location points, thereby affecting the accuracy of grid side lengths and ultimately affecting the reliability of sampling waste soil in mining areas. Summary of the Invention

[0003] To address the technical problem of poor accuracy in acquiring representative location points, which affects the accuracy of grid side lengths and ultimately the reliability of sampling waste soil in mining areas, this invention aims to provide an intelligent sampling method and system for waste soil in mining areas. The specific technical solution adopted is as follows: In a first aspect of the present invention, a smart sampling method for waste soil in mining areas is provided, comprising: Determine the multidimensional geological features and geological fault depths at each initial location point in the abandoned soil area of ​​the mining area; Based on the relationship between the geological fault depth and the target sampling depth at each initial location point, the degree of influence of the fault at each initial location point is determined. Based on the overall level of influence within the neighborhood of each initial location point, as well as the differences in influence between each initial location point and other initial location points within its preset neighborhood, and the differences in multidimensional geological characteristics, the probability of each initial location point serving as a representative location point is determined; the probability is inversely correlated with the overall level of influence, the differences in influence, and the differences in multidimensional geological characteristics. Based on the stated possibilities, representative position points are selected from each initial position point.

[0004] In one exemplary embodiment, the process of obtaining the degree of influence includes: If the geological fault depth at the initial location point is greater than or equal to the target sampling depth, the influence of the initial location point is set to the first preset constant. If the geological fault depth at the initial location point is less than the target sampling depth, the difference between the target sampling depth and the geological fault depth at the initial location point is determined, and the degree of influence of the initial location point is obtained based on the difference. The degree of influence is positively correlated with the difference, and the degree of influence is greater than the first preset constant when the geological fault depth at the initial location point is less than the target sampling depth.

[0005] In an exemplary embodiment, the step of determining the degree of influence of the initial position point based on the difference includes: Determine the width of the fault fracture in the geological fault at the initial location point; By combining the fault fracture width and the difference, the degree of influence of the initial location point is obtained; the degree of influence is positively correlated with the fault fracture width.

[0006] In one exemplary embodiment, the process of obtaining the possibility includes: The correlation degree between the candidate initial position point and each of its adjacent initial position points is obtained based on the average influence degree between the candidate initial position point and each of its adjacent initial position points, the difference in influence degree between the candidate initial position point and each of its adjacent initial position points, and the difference in multidimensional geological characteristics between the candidate initial position point and each of its adjacent initial position points; the candidate initial position point is any initial position point, and the adjacent initial position points are any initial position points within a preset neighborhood of the candidate initial position point. By integrating the correlation between the candidate initial position point and all its adjacent initial position points, the probability of the candidate initial position point being used as the representative position point is obtained.

[0007] In an exemplary embodiment, the step of selecting representative location points from each initial location point based on the probability includes: Sort the initial position points from highest to lowest probability, and select the first preset percentage of initial position points as representative position points.

[0008] In an exemplary embodiment, the intelligent sampling method for abandoned soil in the mining area further includes: The rationality of each candidate edge length is obtained by iterating through each candidate edge length within the preset grid edge length data range and based on the correlation between the candidate sampling point and the representative point corresponding to each candidate edge length. Determine the maximum reasonable side length, and use the candidate side length corresponding to the maximum reasonable side length as the final grid side length; The sampling location points corresponding to the final grid side length are used as the sampling location points of the abandoned soil area in the mining area to sample the abandoned soil in the mining area.

[0009] In an exemplary embodiment, the process of obtaining the reasonableness of the side length includes: The sampling rationality of the candidate side length is determined based on the positional relationship between each candidate sampling point and the representative point corresponding to the candidate side length. Based on the multidimensional geological feature differences between each candidate sampling location point and the representative location point corresponding to the candidate side length, the multidimensional feature difference parameters of the candidate side length are determined. By integrating the sampling rationality of the candidate side length and the multidimensional feature difference parameters, the rationality of the candidate side length is obtained.

[0010] In one exemplary embodiment, the process of obtaining the sampling rationality includes: Determine the nearest representative location point of the sample location point to be selected, wherein the nearest representative location point represents the representative location point of the sample location point to be selected as the nearest sampling location point; Determine the probability of the selected sample location point, and the distance between the selected sample location point and each neighboring representative location point; Based on the probability of the proposed sampling location and the distance between the proposed sampling location and each of the neighboring representative locations, a sampling rationality impact index is obtained; the sampling rationality impact index is positively correlated with the probability of the proposed sampling location and negatively correlated with the distance between the proposed sampling location and each of the neighboring representative locations. The sampling rationality of the candidate side length is obtained by integrating the sampling rationality impact index of all candidate sampling location points corresponding to the candidate side length.

[0011] In an exemplary embodiment, the process of obtaining the multidimensional feature difference parameters includes: Determine the multidimensional geological feature differences between the selected sampling location and each neighboring representative location; By integrating the multidimensional geological feature differences between the selected sampling location and each neighboring representative location, a multidimensional feature difference influence index is obtained for the selected sampling location; the multidimensional feature difference influence index is positively correlated with the multidimensional geological feature differences between the selected sampling location and each neighboring representative location. By integrating the multidimensional feature difference influence index of all sampled locations corresponding to the candidate side length, the multidimensional feature difference parameter of the candidate side length is obtained.

[0012] In an exemplary embodiment, the process of obtaining the probability of the sample location point to be selected includes: The probability of the representative location point closest to the selected sampling location point is taken as the probability of the selected sampling location point.

[0013] In a second aspect of the present invention, an intelligent sampling system for abandoned soil in mining areas is provided, comprising: a memory and a processor; the memory is connected to the processor; the memory is used to store program instructions; the processor is used to implement the above-described intelligent sampling method for abandoned soil in mining areas when the program instructions are executed.

[0014] This invention offers the following advantages: Multidimensional geological features comprehensively reflect the spatial geological diversity of abandoned soil in mining areas, avoiding information gaps caused by single indicators; the introduction of geological fault depth identifies initial location points significantly affected by tectonic activity, providing crucial evidence for subsequent assessments of the stability and representativeness of these points; by comparing geological fault depth with the target sampling depth, it can be determined whether the fault extends to the sampling depth, thus obtaining the degree of fault influence on the initial location point, providing effective data support for determining the likelihood of the initial location point serving as a representative location point; and through lateral analysis of the initial location point and its adjacent... By incorporating spatial neighborhood analysis into the data of other initial location points within the domain, considering factors such as the overall level of influence, differences in influence levels, and differences in multidimensional geological features, a horizontal comparison between the initial location points and the region is achieved. This comprehensive consideration of the influence of geological faults, spatial neighborhood consistency, and multidimensional geological features ensures the objectivity and accuracy of the selected representative location points. This overcomes the subjectivity and one-sidedness of existing technologies that only compare with preset standards, avoids subjective biases caused by human experience judgment, and significantly improves the objectivity and accuracy of representative location point selection. This, in turn, improves the accuracy of grid side lengths and ultimately enhances the sampling reliability of abandoned soil in mining areas. Attached Figure Description

[0015] Figure 1 This is a flowchart of an intelligent sampling method for abandoned soil in mining areas provided by the present invention; Figure 2 The present invention provides a method for intelligent sampling of waste soil in mining areas, which also includes the following steps (flowchart). Figure 3 This is a flowchart illustrating the process of obtaining the rationality of the side length according to an embodiment of the present invention. Detailed Implementation

[0016] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, 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 pertains. All data and information collected in this application have been obtained with full consent.

[0017] This embodiment provides an intelligent sampling method for waste soil in mining areas, which is suitable for sampling waste soil in mining areas.

[0018] First, multiple initial location points are established in the area to be explored: the abandoned soil area of ​​the mining area. The purpose of establishing these initial location points is to select representative locations from among them based on the relevant geological information of each initial location point. These representative locations will then be used to determine the sampling locations in the abandoned soil area for subsequent sampling. The reason for using the method provided in this embodiment to determine the sampling locations in the abandoned soil area is that the geological structure within the mining area is relatively rich and diverse, exhibiting different intensities and continuity in different regions, thus better capturing mineralization trends.

[0019] It should be understood that this embodiment is not limited to the rules for setting initial position points. In one exemplary embodiment, the initial position points are distributed in the abandoned soil area of ​​the mining area. In this embodiment, the distance between adjacent initial position points is known, and the distance range is generally 5 meters to 30 meters. This embodiment takes 10 meters as an example. Then, based on the distance between adjacent initial position points, each initial position point is set in the abandoned soil area of ​​the mining area. Alternatively, the total number of initial position points is known, and the distance between adjacent initial position points is determined by combining the area of ​​the abandoned soil area of ​​the mining area and the requirement for uniform distribution of initial position points, thereby completing the setting of initial position points. It should be understood that the method for obtaining the distance between two position points involved in this embodiment is as follows: a two-dimensional coordinate system is constructed on the plane of the abandoned soil area of ​​the mining area. The two-dimensional coordinate points of the position points can be determined in the two-dimensional coordinate system, and the distance between two position points is the distance between the two-dimensional coordinate points.

[0020] like Figure 1 As shown in the figure, the intelligent sampling method for abandoned soil in mining areas provided in this embodiment includes the following steps: Step S1: Determine the multidimensional geological features and geological fault depths at each initial location point in the abandoned soil area of ​​the mining area; Step S2: Based on the relationship between the geological fault depth and the target sampling depth at each initial location point, determine the degree of influence of the fault on each initial location point; Step S3: Based on the overall level of influence within the neighborhood of each initial location point, as well as the differences in influence between each initial location point and other initial location points within its preset neighborhood and the differences in multidimensional geological characteristics, determine the likelihood of each initial location point serving as a representative location point. Step S4: Based on the probability, select representative position points from each initial position point.

[0021] The following is a detailed explanation of each step.

[0022] Step S1: Determine the multidimensional geological features and geological fault depths of each initial location point in the abandoned soil area of ​​the mining area.

[0023] In many cases, compressional-torsional fault structures are well-developed within mining areas, varying significantly in scale, and constitute the main structural planes within the area. Therefore, this embodiment employs high-frequency electromagnetic sounding to explore each initial location point in the abandoned soil area of ​​the mining area, thereby obtaining the multidimensional geological characteristics and geological fault depths of each initial location point.

[0024] Multidimensional geological features include geological parameters in at least two dimensions. The specific number of dimensions and the specific geological parameters for each dimension are set according to actual exploration needs, and this embodiment does not impose specific limitations. This embodiment uses ground-penetrating radar or a high-density electrical resistivity tomography (EDT) instrument to detect multiple geological parameters at the initial location point. This device emits high-frequency electromagnetic waves and receives the induced electromagnetic field response of the subsurface medium to invert geological parameters such as resistivity and polarizability at different depths. As an example, multiple geological parameters include, but are not limited to: resistivity, polarizability, estimated thickness of the target stratum, and fault development index. Among them, resistivity is measured in ohmmeters and characterizes the density or water content of the strata; polarizability is measured as a percentage and characterizes the mineralization enrichment degree of metallic minerals; the estimated thickness of the target stratum is measured in meters and can be obtained through electromagnetic frequency-depth conversion; the fault development index is dimensionless, derived from the electrical level gradient, and quantified as a value between 0 and 1, characterizing the complexity of the geological structure.

[0025] To facilitate data processing, geological parameters in each dimension can be normalized to eliminate the influence of dimensions and scale them to the same order of magnitude. For any single dimension of geological parameter, a normalization method is given below: obtain the maximum and minimum values ​​of the geological parameter in that dimension for all initial location points, and then use the maximum / minimum value normalization method to normalize the geological parameter in that dimension for each initial location point. This method is used to normalize geological parameters in each dimension. All geological parameters mentioned below are the normalized results.

[0026] It should be understood that, under normal circumstances, the geological parameters of different dimensions at different initial locations cannot be exactly the same due to geological differences. Therefore, if the geological parameters of any dimension are exactly the same at all initial locations, it is determined that there is a problem with the data acquisition, which may be due to a malfunction of the exploration instrument. In this case, an abnormal data alarm signal will be output and no further data processing will be performed.

[0027] For any initial location point, the geological parameters of each dimension are sorted, and the resulting sequence serves as the multidimensional geological feature of that initial location point. Similarly, the multidimensional geological features of all other initial location points are obtained. It should be understood that the geological parameters of each dimension for all initial location points are sorted using the same sorting method, for example, in the following order: resistivity, polarizability, estimated thickness of the target stratum, and fault development index.

[0028] Due to the influence of crustal activity and other factors during the historical formation of the mining area, faults and fissures may exist in some areas, which could lead to displacement or fracturing of the ore body and affect the representativeness of each initial location point. Therefore, it is first necessary to obtain the geological fault depth at each initial location point using exploration instruments. The geological fault depth represents the depth of the underground geological fault at the corresponding initial location point, that is, the vertical distance between the exposed soil surface at the corresponding initial location point and the position of the fault closest to the exposed surface.

[0029] Step S2: Based on the relationship between the geological fault depth and the target sampling depth at each initial location point, determine the degree of influence of the fault on each initial location point.

[0030] Since the sampling depth at various sampling points in the abandoned soil area of ​​the mining area is usually the same during soil sampling, this embodiment sets a target sampling depth according to actual sampling needs. It should be understood that the target sampling depth is a preset value greater than 0, and the specific value is set according to sampling needs; for example, 2 meters. Based on the relationship between the geological fault depth at each initial location point and the target sampling depth, the degree of influence of the fault on each initial location point is determined.

[0031] In an exemplary embodiment, for any initial location point, defined as a candidate initial location point, taking the i-th initial location point as an example, the relationship between the geological fault depth and the target sampling depth at the i-th initial location point is determined: If the geological fault depth at the i-th initial location point is greater than or equal to the target sampling depth, it means that the geological fault at the i-th initial location point is outside the target sampling depth, does not affect soil sampling, and will not affect the final sampling results. Therefore, the degree of influence of the fault on the i-th initial location point is set as the first preset constant. The first preset constant is a non-negative number and a small value. As an example, the first preset constant is 0, which means that the influence of the fault on the i-th initial location point is 0, that is, it is not affected by the fault.

[0032] If the geological fault depth at the i-th initial location point is less than the target sampling depth, it means that the geological fault at the i-th initial location point exists within the target sampling depth, affecting soil sampling and thus impacting the final sampling results. Therefore, subtracting the geological fault depth at the i-th initial location point from the target sampling depth yields a difference. The larger this difference, the smaller the geological fault depth at the i-th initial location point, the closer the geological fault is to the exposed surface, and the more susceptible the soil sampling is to the fault's influence. The degree of influence of the i-th initial location point on the fault is higher, and thus the degree of influence is positively correlated with this difference.

[0033] It should be understood that the impact of the geological fault depth at the i-th initial location point being less than the target sampling depth is greater than the first preset constant. Since the first preset constant is 0, the impact of the geological fault depth at the i-th initial location point being less than the target sampling depth is a positive number.

[0034] In an exemplary embodiment, this embodiment also obtains the fault fracture width of the geological fault at each initial location point using exploration equipment. Specifically, the fault fracture width can be the fracture width at the location closest to the exposed surface of the geological fault at the corresponding initial location point. A larger fault fracture width indicates a greater degree of segmentation of the geological data at the initial location point by the fault fracture, resulting in a greater loss of geological information during sampling. This means the initial location point is more significantly affected by the fault, and therefore, the degree of influence is positively correlated with the fault fracture width. To facilitate data processing, this embodiment requires normalization of the fault fracture width to eliminate the influence of dimensions. One normalization method is as follows: obtain the maximum and minimum values ​​of the fault fracture widths of the geological faults at all initial location points, and then use the maximum and minimum value normalization method to normalize the fault fracture widths of the geological faults at each initial location point. The fault fracture widths of the geological faults at each initial location point mentioned below are all normalized results.

[0035] By combining the fault fracture width at the i-th initial location point with the difference between the target sampling depth and the geological fault depth at the i-th initial location point, the degree of fault influence at the i-th initial location point is obtained. Based on the above logical analysis, the following is a specific calculation method for the degree of fault influence at the i-th initial location point: ; in, This indicates the degree to which the i-th initial location is affected by the fault. Indicates the target sampling depth. This represents the depth of the geological fault at the i-th initial location point. This represents the width of the fault fracture at the i-th initial location point. This represents the rate of change of the difference between the target sampling depth and the geological fault depth at the i-th initial location point relative to the target sampling depth, used for... Normalization processing is performed. Incorporating the fault fracture width into the calculation of the degree of influence of the initial location point on the fault improves the reliability of this assessment. Using this method, the degree of influence of the fault on each initial location point is obtained.

[0036] In other embodiments, the fault fracture width can be disregarded when calculating the degree of influence of the fault on the initial location point. Instead, the degree of influence of the fault on each initial location point can be obtained solely based on the difference between the target sampling depth and the geological fault depth at each initial location point, as detailed below: .

[0037] Step S3: Based on the overall level of influence within the neighborhood of each initial location point, as well as the differences in influence between each initial location point and other initial location points within its preset neighborhood, and the differences in multidimensional geological characteristics, determine the likelihood of each initial location point serving as a representative location point.

[0038] Analysis shows that the representativeness of any initial location point is reflected in the local consistency of its differences with other initial location points. That is, within a certain range, the differences in multidimensional geological features between the initial location point and other surrounding initial location points are usually small. However, since different initial location points have different inherent properties such as depth, strike, and dip angle of the geological fault they are located on, when a geological fault exists, theoretically, the geological data collected from a certain initial location point will be displaced along a certain direction. That is, at another initial location point, its influence on the geological data of different initial location points is different.

[0039] Therefore, for the i-th initial position point, a preset neighborhood range of the i-th initial position point within the abandoned soil area of ​​the mining area is determined. In an exemplary embodiment, a circular area with the i-th initial position point as the center and a preset radius is used as the preset neighborhood range of the i-th initial position point. The size of the preset radius is determined by the distance between adjacent initial position points; for example, if the distance between adjacent initial position points is 10 meters, the preset radius can be set to 20 meters. All other initial position points within the preset neighborhood range of the i-th initial position point, excluding the i-th initial position point, are defined as adjacent initial position points of the i-th initial position point. Any adjacent initial position point of the i-th initial position point is set as the r-th adjacent initial position point, and the number of adjacent initial position points of the i-th initial position point is set to R.

[0040] Based on the overall level of influence within the neighborhood of the i-th initial location point, as well as the differences in influence between the i-th initial location point and other initial location points within its preset neighborhood, and the differences in multidimensional geological characteristics, the possibility of the i-th initial location point being used as a representative location point is determined.

[0041] In an exemplary embodiment, for the r-th adjacent initial location point, the average influence degree of the i-th initial location point and the influence degree of the r-th adjacent initial location point are calculated. The average influence degree of the i-th initial location point and the r-th adjacent initial location point characterizes the overall level of influence between the two location points. The higher the overall level of influence, the greater the influence of the fault on the two location points. The lower the correlation between the i-th initial location point and the r-th adjacent initial location point, the lower the representativeness of the i-th initial location point, that is, the lower the probability that the i-th initial location point is as a representative location point. The correlation between the i-th initial location point and the r-th adjacent initial location point is inversely correlated with the average influence degree, that is, the probability that the i-th initial location point is as a representative location point is inversely correlated with the average influence degree.

[0042] Calculate the absolute value of the difference between the influence degree of the i-th initial location point and the influence degree of the r-th adjacent initial location point. The absolute value of the difference in influence degree between the i-th initial location point and the r-th adjacent initial location point represents the difference in influence degree between the two locations. The lower the difference in influence degree, the more similar the two locations are to the fault's influence, the more correlated the i-th initial location point and the r-th adjacent initial location point are, i.e., the higher their correlation. The more representative the i-th initial location point is, i.e., the higher the probability that the i-th initial location point is a representative location point, the higher the probability that the i-th initial location point is a representative location point. The correlation degree between the i-th initial location point and the r-th adjacent initial location point is inversely correlated with the absolute value of the difference in influence degree; that is, the probability that the i-th initial location point is a representative location point is inversely correlated with the absolute value of the difference in influence degree.

[0043] The multidimensional geological feature differences between the i-th initial location point and the r-th adjacent initial location point are determined. In an exemplary embodiment, the mean absolute error between the multidimensional geological features of the i-th initial location point and the r-th adjacent initial location point is calculated, and this mean absolute error is taken as the multidimensional geological feature difference. The mean absolute error is calculated as follows: the absolute value of the difference between the geological parameters of the same dimension in the multidimensional geological features of the i-th initial location point and the r-th adjacent initial location point is calculated, and then the average of the absolute values ​​of the differences of the geological parameters in all dimensions is calculated as the mean absolute error between the multidimensional geological features of the i-th initial location point and the r-th adjacent initial location point. The lower the difference in multidimensional geological features, the more similar the multidimensional geological features of the two locations are. The more correlated the i-th initial location point is with the r-th adjacent initial location point, the higher the degree of correlation between them. The more representative the i-th initial location point is, the higher the probability that the i-th initial location point is as a representative location point. The degree of correlation between the i-th initial location point and the r-th adjacent initial location point is inversely correlated with the difference in multidimensional geological features, that is, the probability that the i-th initial location point is as a representative location point is inversely correlated with the difference in multidimensional geological features.

[0044] Based on the above logical analysis, the following is a specific method for calculating the correlation between the i-th initial position point and its r-th adjacent initial position point: ; in, This represents the degree of association between the i-th initial position and its r-th neighboring initial position. This represents the absolute value of the difference in influence between the i-th initial position point and its r-th adjacent initial position point. This represents the average influence of the i-th initial position point on its r-th adjacent initial position point. This represents the average absolute error of the multidimensional geological features between the i-th initial location point and the r-th adjacent initial location point. The above method calculates the difference between the numerical value 1 and the corresponding parameter to achieve inverse correlation calculation of the parameters, and then integrates the three aspects of data by calculating the average value.

[0045] Through the above process, the correlation degree between the i-th initial position point and each of its adjacent initial position points is obtained. Then, the correlation degrees between the i-th initial position point and all its adjacent initial position points are fused to obtain the probability that the i-th initial position point is a representative position point. In an exemplary embodiment, the average correlation degree between the i-th initial position point and each of its adjacent initial position points is calculated to achieve data fusion, and this average value is used as the probability that the i-th initial position point is a representative position point. Thus, the probability of each initial position point being a representative position point is obtained. It should be understood that the probability mentioned in this embodiment refers to the probability obtained in this step.

[0046] Step S4: Based on the probability, select representative position points from each initial position point.

[0047] Step S3 obtains the probability that each initial position point is a representative position point. The higher the probability, the more likely the initial position point is to be used as a representative position point. In an exemplary embodiment, the probabilities of each initial position point are sorted from high to low, and the first preset percentage of initial position points are selected as representative position points. The value of the first preset percentage is set according to actual needs, such as the required number of representative position points and the total number of initial position points. In an exemplary embodiment, the first preset percentage is taken as the first 20%, meaning the first 20% of initial position points are selected as representative position points. It should be understood that if the value obtained by selecting the first 20% is not an integer, it is rounded up to an integer, thus selecting an integer number of initial position points as representative position points.

[0048] After obtaining representative location points, these points can be used to determine accurate grid edge lengths, ultimately achieving reliable sampling of waste soil from the mining area. In an exemplary embodiment, such as... Figure 2 As shown, the intelligent sampling method for abandoned soil in mining areas provided in this embodiment also includes the following grid side length and soil sampling steps: Step S5: Traverse each candidate edge length within the preset grid edge length data range, and obtain the rationality of each candidate edge length based on the correlation between the candidate sampling point and the representative point.

[0049] In this embodiment, the grid shape is taken as a square. This embodiment predetermines a grid side length data range. Considering the actual operational scale of macroscopic geological exploration in the mining area, the grid side length is typically controlled between 5 meters and 30 meters. Using 1 meter as the selection step size, multiple grid side lengths are sequentially selected from the grid side length data range and defined as candidate side lengths. Therefore, the candidate side lengths are integers within the range [5, 30], starting from 5 meters and increasing by 1 meter sequentially until reaching 30 meters.

[0050] The process iterates through each candidate side length within the preset grid side length data range. Once a candidate side length is determined, the abandoned soil area of ​​the mining area can be divided into multiple grids. At the same time, the center point of each grid is obtained. The center point of each grid is used as the candidate sampling location point, thereby obtaining the candidate sampling location point corresponding to each candidate side length.

[0051] During each iteration of the candidate side lengths, the rationality of the candidate side length is determined based on the correlation between the candidate side length's corresponding sampling position and the representative position obtained in step S4. This yields the rationality of the side length for each candidate side length. In an exemplary embodiment, such as... Figure 3 As shown, the process of obtaining the reasonableness of the side length includes: Step S51: Determine the rationality of sampling the candidate side length based on the positional relationship between each candidate sampling point and the representative point.

[0052] It should be understood that for any candidate side length, it is defined as the first... There are several candidate side lengths. The distances between the sample locations corresponding to the candidate side lengths are the same. Therefore, the closer the distance between the candidate sample location and the representative location, the better, so that the final sampling effect in the final sampling process is better.

[0053] For the Let any one of the candidate sampling locations corresponding to the candidate side lengths be defined as the y-th candidate sampling location. Determine the neighboring representative locations of the y-th candidate sampling location. The neighboring representative locations of the y-th candidate sampling location represent the representative locations of the nearest sampling location. The process is as follows: For the y-th candidate sampling location, calculate its spatial distance to all representative locations, and select the K nearest representative locations as the neighboring representative locations of that candidate sampling location. Here, K can be 3.

[0054] The probability of determining the y-th candidate sampling location as a sampling location is equal to the probability of the nearest representative location to the y-th candidate sampling location. Specifically: determine the distances between each representative location and the y-th candidate sampling location, identify the minimum distance, determine the probability of the representative location corresponding to the minimum distance, and use this probability as the probability of the y-th candidate sampling location as a sampling location. The higher the probability of the y-th candidate sampling location as a sampling location, the higher the sampling rationality impact index of the y-th candidate sampling location. That is, the higher the sampling rationality, the higher the probability of it being the final sampling location. Therefore, the sampling rationality impact index of the y-th candidate sampling location is positively correlated with the probability of the y-th candidate sampling location as a sampling location.

[0055] Determine the distances between the y-th candidate sample location and each of its neighboring representative locations. To facilitate subsequent data processing, these distances need to be normalized. One normalization method is given below: Obtain the distances of the y-th candidate sample location... The maximum and minimum values ​​of the distances between each candidate sampling location and its corresponding neighboring representative locations are calculated for each candidate side length. Then, a maximum / minimum value normalization method is used to normalize the distance between the y-th candidate sampling location and its neighboring representative locations. The distances between candidate sampling locations and their corresponding neighboring representative locations mentioned below are all normalized results. The average value of the distance between the y-th candidate sampling location and its neighboring representative locations is calculated and defined as the distance characteristic between the y-th candidate sampling location and its neighboring representative locations. The smaller the value of the distance characteristic, the closer the y-th candidate sampling location is to its neighboring representative locations, and the higher the sampling rationality influence index of the y-th candidate sampling location. In other words, the higher the sampling rationality, the higher the probability that it will be the final sampling location. Therefore, the sampling rationality influence index of the y-th candidate sampling location is inversely correlated with the distance characteristic between the y-th candidate sampling location and its neighboring representative locations.

[0056] Based on the above logical analysis, the following is a specific calculation method for the sampling rationality impact index of the y-th candidate sampling location point: ; in, This represents the index affecting the rationality of sampling at the y-th candidate sampling location. This represents the probability that the y-th candidate sampling location point will be selected as the sampling location point. This represents the distance feature between the y-th candidate sampling location and its neighboring representative locations. Thus, the y-th... The sampling rationality impact index of each sampling location point corresponding to each candidate side length.

[0057] Then, the fusion of the first The sampling rationality impact index of all sampling locations corresponding to the candidate side length is calculated as follows: Specifically, the sampling rationality impact index of the candidate side length is calculated as follows: The average value of the sampling rationality impact index of all sampling locations corresponding to the nth candidate side length is used as the nth average value. The rationality of sampling the candidate side lengths. The higher the sampling rationality value of the first candidate side length, the better the sample in this respect. The higher the rationality of each sampling point under each candidate side length, the better. The more reasonable the side length of each candidate side is, the better.

[0058] Step S52: Determine the multidimensional feature difference parameters of the candidate side length based on the multidimensional geological feature differences between each candidate sampling location point and the representative location point corresponding to the candidate side length.

[0059] It should be understood that when determining sampling locations, the multidimensional geological features of any sampling location should be significantly different from those of other adjacent locations. The greater the difference in multidimensional geological features between the sampling location and its surrounding locations, the higher the sampling value of the sampling location.

[0060] In one exemplary embodiment, still using the first Taking the y-th candidate sampling location point corresponding to a candidate side length as an example, the multidimensional geological features of the y-th candidate sampling location point and the multidimensional geological features of each neighboring representative location point of the y-th candidate sampling location point are determined. For obtaining the multidimensional geological features of the y-th candidate sampling location point, a spatial interpolation algorithm, such as inverse distance weighted interpolation, is used. Based on the multidimensional geological features of each neighboring representative location point of the y-th candidate sampling location point, the multidimensional geological features of the y-th candidate sampling location point are calculated. The inverse distance weighted interpolation algorithm is existing technology and will not be elaborated further here.

[0061] The multidimensional geological feature differences between the y-th candidate sampling location and its neighboring representative locations are determined. These multidimensional geological feature differences are calculated using the same method described above, namely, the mean absolute error of the multidimensional geological features. The greater the multidimensional geological feature differences between the y-th candidate sampling location and its neighboring representative locations, the greater the influence of the multidimensional feature difference on the y-th candidate sampling location. The larger the multidimensional feature difference parameter of each candidate side length, the more positively correlated the multidimensional feature difference influence index is with the multidimensional geological feature difference. In an exemplary embodiment, the average value of the multidimensional geological feature differences between the y-th candidate sampling location and each of its neighboring representative locations is calculated, and this average value is used as the multidimensional feature difference influence index of the y-th candidate sampling location. Thus, the y-th... The multidimensional feature differences of each candidate sampling location point corresponding to each candidate side length affect the index.

[0062] Then, the fusion of the first The multidimensional feature difference influence index of all candidate sampling locations corresponding to the candidate side length is specifically calculated as follows: The average value of the multidimensional feature difference influence index of all sampling locations corresponding to the nth candidate side length is used as the nth candidate side length. The multidimensional feature difference parameters of the candidate side lengths. The larger the value of the multidimensional feature difference parameter of the candidate side length, the greater the value of the multidimensional feature difference parameter of the candidate side length. The stronger the overall sampling specificity of each candidate side length, the better the overall sampling specificity of the first... The more reasonable the side length of each candidate side is, the better.

[0063] Step S53: Integrate the sampling rationality of the candidate side length and the multidimensional feature difference parameters to obtain the rationality of the candidate side length.

[0064] No. The rationality of sampling the length of each candidate side and the first The multidimensional feature difference parameter of the candidate side lengths characterizes the first... The rationality of the length of the candidate side is then determined according to the length of the first side. The rationality of sampling the length of each candidate side and the first The multidimensional feature difference parameters of the candidate side lengths are obtained to obtain the first... The rationality of the length of the candidate side. In an exemplary embodiment, the rationality of the length of the candidate side is calculated. The rationality of sampling the length of the candidate side and the first The average value of the multidimensional feature difference parameters of the candidate side lengths is used as the average value of the first side length. The rationality of the selected side lengths is determined by calculating the average value and fusing data from both aspects to obtain an accurate and reliable assessment of the side length rationality.

[0065] Iterate through each candidate edge length. Each time a candidate edge length is traversed, the rationality of the edge length is obtained.

[0066] Step S6: Determine the maximum reasonable side length and use the candidate side length corresponding to the maximum reasonable side length as the final grid side length.

[0067] For each candidate edge length within the preset grid edge length data range, the higher the edge length rationality value, the higher the rationality of the candidate edge length as the final grid edge length. Therefore, the edge length rationality value with the largest value is determined from the edge length rationality values ​​of each candidate edge length, and the candidate edge length corresponding to the edge length rationality value with the largest value is taken as the final grid edge length, which is defined as the final grid edge length.

[0068] Step S7: Select the sampling location points corresponding to the final grid side length as the sampling location points of the abandoned soil area in the mining area, so as to sample the abandoned soil in the mining area.

[0069] Upon obtaining the final grid side length, the corresponding grid division method is also determined. The abandoned soil area in the mining area is then divided into grids according to this method, resulting in individual grids. The center point of each grid is used as the sampling location point for the abandoned soil area. Soil sampling equipment is then used to collect samples of the abandoned soil from each sampling location point. In an exemplary embodiment, abandoned soil sampling may include soil sampling, rock sampling, and water sampling. Each sampling location point is numbered, and information such as its geographical coordinates, sampling depth, and sampling time are recorded and associated with the sampling. The sampling results from each location point are then sent to a laboratory. The laboratory analysis results are combined with the geographical information of the sampling locations to establish a database.

[0070] This embodiment also provides an intelligent sampling system for abandoned soil in mining areas, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-described intelligent sampling method embodiment for abandoned soil in mining areas when the program instructions are executed.

[0071] In one exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described embodiment of the intelligent sampling method for abandoned soil in mining areas.

[0072] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A smart sampling method for waste soil in mining areas, characterized by comprising: Determine the multidimensional geological features and geological fault depths at each initial location point in the abandoned soil area of ​​the mining area; Based on the relationship between the geological fault depth and the target sampling depth at each initial location point, the degree of influence of the fault at each initial location point is determined. Based on the overall level of influence within the neighborhood of each initial location point, as well as the differences in influence between each initial location point and other initial location points within its preset neighborhood, and the differences in multidimensional geological characteristics, the probability of each initial location point serving as a representative location point is determined; the probability is inversely correlated with the overall level of influence, the differences in influence, and the differences in multidimensional geological characteristics. Based on the stated possibilities, representative position points are selected from each initial position point.

2. The intelligent sampling method for abandoned soil in mining areas as described in claim 1, characterized in that, The process of obtaining the degree of influence includes: If the geological fault depth at the initial location point is greater than or equal to the target sampling depth, the influence of the initial location point is set to the first preset constant. If the geological fault depth at the initial location point is less than the target sampling depth, the difference between the target sampling depth and the geological fault depth at the initial location point is determined, and the degree of influence of the initial location point is obtained based on the difference. The degree of influence is positively correlated with the difference, and the degree of influence is greater than the first preset constant when the geological fault depth at the initial location point is less than the target sampling depth.

3. The intelligent sampling method for abandoned soil in mining areas as described in claim 1, characterized in that, The process of obtaining the possibility includes: The correlation degree between the candidate initial position point and each of its adjacent initial position points is obtained based on the average influence degree between the candidate initial position point and each of its adjacent initial position points, the difference in influence degree between the candidate initial position point and each of its adjacent initial position points, and the difference in multidimensional geological characteristics between the candidate initial position point and each of its adjacent initial position points; the candidate initial position point is any initial position point, and the adjacent initial position points are any initial position points within a preset neighborhood of the candidate initial position point. By integrating the correlation between the candidate initial position point and all its adjacent initial position points, the probability of the candidate initial position point being used as the representative position point is obtained.

4. The intelligent sampling method for abandoned soil in mining areas as described in claim 1, characterized in that, The step of selecting representative position points from each initial position point based on the probability includes: Sort the initial position points from highest to lowest probability, and select the first preset percentage of initial position points as representative position points.

5. The intelligent sampling method for abandoned soil in mining areas as described in claim 1, characterized in that, The intelligent sampling method for abandoned soil in the mining area also includes: The rationality of each candidate edge length is obtained by iterating through each candidate edge length within the preset grid edge length data range and based on the correlation between the candidate sampling point and the representative point corresponding to each candidate edge length. Determine the maximum reasonable side length, and use the candidate side length corresponding to the maximum reasonable side length as the final grid side length; The sampling location points corresponding to the final grid side length are used as the sampling location points of the abandoned soil area in the mining area to sample the abandoned soil in the mining area.

6. The intelligent sampling method for abandoned soil in mining areas as described in claim 5, characterized in that, The process of obtaining the rationality of the side length includes: The sampling rationality of the candidate side length is determined based on the positional relationship between each candidate sampling point and the representative point corresponding to the candidate side length. Based on the multidimensional geological feature differences between each candidate sampling location point and the representative location point corresponding to the candidate side length, the multidimensional feature difference parameters of the candidate side length are determined. By integrating the sampling rationality of the candidate side length and the multidimensional feature difference parameters, the rationality of the candidate side length is obtained.

7. The intelligent sampling method for abandoned soil in mining areas as described in claim 6, characterized in that, The process of obtaining the rationality of the sampling includes: Determine the nearest representative location point of the sample location point to be selected, wherein the nearest representative location point represents the representative location point of the sample location point to be selected as the nearest sampling location point; Determine the probability of the selected sample location point, and the distance between the selected sample location point and each neighboring representative location point; Based on the probability of the proposed sampling location and the distance between the proposed sampling location and each of the neighboring representative locations, a sampling rationality impact index is obtained; the sampling rationality impact index is positively correlated with the probability of the proposed sampling location and negatively correlated with the distance between the proposed sampling location and each of the neighboring representative locations. The sampling rationality of the candidate side length is obtained by integrating the sampling rationality impact index of all candidate sampling location points corresponding to the candidate side length.

8. The intelligent sampling method for abandoned soil in mining areas as described in claim 7, characterized in that, The process of obtaining the multidimensional feature difference parameters includes: Determine the multidimensional geological feature differences between the selected sampling location and each neighboring representative location; By integrating the multidimensional geological feature differences between the selected sampling location and each neighboring representative location, a multidimensional feature difference influence index is obtained for the selected sampling location; the multidimensional feature difference influence index is positively correlated with the multidimensional geological feature differences between the selected sampling location and each neighboring representative location. By integrating the multidimensional feature difference influence index of all sampled locations corresponding to the candidate side length, the multidimensional feature difference parameter of the candidate side length is obtained.

9. The intelligent sampling method for abandoned soil in mining areas as described in claim 8, characterized in that, The process of obtaining the probability of the sample location points to be selected includes: The probability of the representative location point closest to the selected sampling location point is taken as the probability of the selected sampling location point.

10. An intelligent sampling system for waste soil in mining areas, characterized in that it comprises: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is configured to implement the intelligent sampling method for abandoned soil in mining areas as described in any one of claims 1-9 when program instructions are executed.

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