Coal mine multi-attribute spatial feature modeling method and system

Abstract

The invention discloses a coal mine multi-attribute spatial feature modeling method and system which are used in actual exploration and production of a coal mine to reduce distribution feature errors of all attributes of a coal mine drilling sample value in a whole space due to human subjective factors. The coal mine multi-attribute spatial feature modeling method comprises the following steps: determining a multi-attribute spatial data point set, arranging the multi-attribute spatial data point set into a (x, y, z, R) spatial coordinate data format, processing the data point set, and calculating power exponent related parameters to obtain 10 groups of power exponent data groups; calculating weighting coefficient data under different power exponents according to a control variable method, and calculating to obtain equilibrium proportion evaluation factors under different data; and extracting a minimum equilibrium proportion evaluation factor, determining an optimal parameter, and generating a multi-attribute three-dimensional model of each point based on the optimal parameter.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine exploration technology, and in particular to a method and system for modeling coal mine multi-attribute spatial characteristics. Background Art

[0002] In the existing technology, during the actual exploration and production process of coal mines, the factory conducts drilling sampling and chemical analysis, and there will be multiple attribute data, such as resistivity (R), porosity (φ), analytical water (Mad), ash content (Aad), etc. How to obtain the distribution characteristics of various attributes in the whole space through drilling sampling values ​​has become one of the problems that need to be solved in coal mine production. The stage-type method uses the plane drilling position to manually define the distribution range and characteristics of various attributes according to physical conditions and experience. This method has a large number of subjective factors and cannot scientifically characterize the distribution characteristics of each attribute in three-dimensional space. It is impossible to form a scientific and objective analysis process, which affects the final three-dimensional spatial distribution characteristics of each attribute, resulting in related errors.

[0003] In view of this, the present invention is proposed. Summary of the invention

[0004] The main purpose of the present invention is to disclose a method and system for modeling the multi-attribute spatial characteristics of coal mines, which is used to solve the problem in the actual production of the prior art that the various attributes and various data generated by various types of boreholes in coal mines lead to the problem of predicting the multi-attribute characteristics of the undrilled area from the borehole data, and the empirical formulas and manual judgment methods currently used are highly subjective and cannot scientifically and effectively characterize the characteristic distribution of the multi-attribute in space. Therefore, the present invention is based on the inverse distance weighted difference, adopts the constant ratio evaluation method, and proposes a set of multi-attribute spatial characteristic modeling methods and systems for coal mines by analyzing the three parameters of weight, weighting coefficient, and power exponent.

[0005] To achieve the above object, according to one aspect of the present invention, a method for modeling a feature of a coal mine multi-attribute space is provided, and the following technical scheme is adopted:

[0006] A characteristic modeling method for coal mine multi-attribute space includes: determining a multi-attribute space data point set, organizing it into a (x, y, z, R) space coordinate data format, processing the data point set, and calculating power index related parameters to obtain 10 power index data groups; calculating weighted coefficient data under different power indexes according to a control variable method, and calculating equilibrium ratio evaluation factors under different parameters; extracting the minimum equilibrium ratio evaluation factor, determining the optimal parameters, and generating a multi-attribute three-dimensional model of each point based on the optimal parameters:

[0007]

[0008] Among them, p is the power index, m is the weighting coefficient, di is the three-dimensional space distance between the unknown point and the known point, W i is the weight of this point.

[0009] Further, the multi-attribute spatial data point set is determined, organized into a (x, y, z, R) spatial coordinate data format, the data point set is processed, and power index related parameters are calculated to obtain 10 groups of power index data groups, including: obtaining apparent resistivity R borehole attribute information, organized into a (x, y, z, R) spatial coordinate data format, x, y, z are three-dimensional spatial coordinates, a total of N known data point sets, a total of n data points, and the longest straight-line distance of data distribution in the study area is L; then, based on the known data point set N, the data points are numbered as N1, N2...N n , the apparent resistivity R of the corresponding attributes are R1, R2...R n ; Then, select the unknown point to be calculated as the center of the circle, the search radius r is half of the longest straight-line distance of the data distribution in the study area, extract the corresponding known point information for calculation, calculate the power index p, the maximum value of the power index p is 10, the minimum value is 1, the step length Sp = 1, and 10 groups of data are obtained, namely p = 1, p = 2...p = 10 data groups R p1 , R p2 ...R p10 .

[0010] Furthermore, the weighted coefficient data under different power exponents are calculated according to the control variable method to obtain the balanced ratio evaluation factor under different parameters, which includes: firstly, when the power exponent p=1, the data set R p1 Based on this, the weighted coefficient m is calculated and analyzed. The minimum value of the weighted coefficient m is 1, the maximum value is L, and the step length of the weighted coefficient is S. m =1, calculate the weighting coefficient m as an integer, and obtain the weighting coefficient data set R m , using the following formula:

[0011]

[0012] Among them, R*(u) is the unknown point information to be obtained, R(ui) is the known point data, and W i is the weight of the point; then, the apparent resistivity R1, R2...R n Normalize the data so that the apparent resistivity R of the known point data is distributed in the range of 0 to 1, and obtain the apparent resistivity NF of the known point data after normalization. R , the normalized apparent resistivity NF R Every 0.1 is divided into 10 intervals NF R1 NF R2 ...NF R10 , the first interval ratio of the known data point is PR1 , calculate the proportion P of each interval R1 , P R2 ...P R10 ;

[0013]

[0014] Formula (3) is the normalization formula for apparent resistivity, where R1 is the apparent resistivity of a known point, R min is the minimum apparent resistivity among the known points, R max is the maximum apparent resistivity at a known point, F R1 is the normalized value of R1;

[0015]

[0016] Formula (4) is the ratio of the apparent resistivity of the known point in each interval, P Ri is the ratio to be calculated, N(NF Ri ) is the number of normalized known points in different intervals (when i=1, it means the number of points with normalized apparent resistivity values ​​in the interval of 0-0.1), and N is the total number of known points; then, the calculated apparent resistivity set Rc is normalized so that the apparent resistivity Rc of the data obtained by weight calculation is distributed in the interval of 0-1, and the apparent resistivity N of the normalized calculated data is obtained. FRc , the apparent resistivity NF generated by normalization calculation Rc Every 0.1 is divided into 10 intervals N FRc1 NF Rc2 ...NF Rc10 , calculate the proportion of the first interval of data as P Rc1 , get the proportion P of each interval of the calculated data Rc1 , P Rc2 ...P Rc10 ;

[0017]

[0018] Among them, formula (5) is the normalized formula of apparent resistivity after calculation, R C1 To calculate the point apparent resistivity, R Cmin is the minimum apparent resistivity at the calculation point, R Cmax is the maximum apparent resistivity at the calculation point, FR C1 For R C1 Normalized value;

[0019]

[0020] Among them, formula (6) is the ratio of the apparent resistivity of the calculation completion point in each interval, P RCi is the ratio to be calculated, N(NFRci ) is the number of known points after normalization in different intervals (when i = 1, it means the number of points with normalized apparent resistivity values ​​in the interval of 0-0.1), N C The total number of apparent resistivities after the calculation is completed; then, the absolute value P of the difference between the first interval ratio of the known data point PR1 and the first interval ratio of the calculated data PRc1 R1 -R c1 Similarly, the absolute values ​​of the differences between the second interval, the third interval, and the tenth interval are obtained, which are P R2 -R c 2. P R3 -R c3 ...P R10 -R c10 The sum of the absolute values ​​of the differences between the two groups in 10 groups is the apparent resistivity equilibrium ratio evaluation factor P. R .

[0021] Furthermore, the step of calculating the weighted coefficient data under different power exponents according to the control variable method to obtain the equilibrium ratio evaluation factors under different parameters also includes: repeating the above step of "calculating the weighted coefficient data under different power exponents according to the control variable method" to obtain the equilibrium ratio evaluation factors of different attributes, the porosity equilibrium ratio evaluation factor Pφ, the analytical water equilibrium ratio evaluation factor P Mad , ash balance ratio evaluation factor P Aad ; Then, according to the balanced ratio evaluation method flow, based on the weighted coefficient data set Rm, the apparent resistivity balanced ratio evaluation factor data set P under p = 1 is calculated Rp 1; then, extract the apparent resistivity equilibrium ratio evaluation factor data group P under p = 1 Rp1 Minimum weighting factor m min1 , and P Rp1 The weighting coefficient m when the second minimum value min2 , compared with the weighting coefficient m when p = 1 minimum value min1 The weighting coefficient m when the sum is the second smallest value min2 , analyze whether there are other data points in the apparent resistivity equilibrium ratio evaluation factor data set PRp1 under p = 1 between the two data. If so, proceed to the final step. Otherwise, the weighting coefficient m is the minimum value. min1 That is, the optimal parameter m of the weighted coefficient m under p = 1 yp1 ; Finally, if the minimum weighting coefficient m min1 p≠1 Extract the weighting coefficient m when p=1 minimum value min1 The weighting coefficient m when the sum is the second smallest value min2 The corresponding balanced ratio evaluation factor data set P Rp1 Chinese Data P Rp1 , m = 1 and P Rp1 ,m=2, with weighting coefficient mmin1 and m min2 The weighted coefficient step length Sm2 = Sm / 10 is calculated, and the weighted coefficient data set Rm2 is calculated again, and then the apparent resistivity equilibrium ratio evaluation factor data set P calculated for the second time is obtained. Rp1 .

[0022] Furthermore, the extraction of the minimum equilibrium ratio evaluation factor, determination of the optimal parameters, and generation of the multi-attribute three-dimensional model of each point based on the optimal parameters include: repeatedly calculating the weighted coefficient data under different power exponents according to the control variable method, obtaining the optimal parameter m of the weighted coefficient m under the final power exponent p=1 yp1 ;

[0023] Repeat the above steps to extract the minimum equilibrium ratio evaluation factor to obtain the optimal parameter m of the weighted coefficient m under different power exponents. yp2 、m yp3 ,...m yp10 ; Compare the apparent resistivity equilibrium ratio evaluation factor P of 10 groups of optimal parameters of weighted coefficient m R , Balanced ratio evaluation factor P R The weighting coefficient m corresponding to the minimum value is the optimal weighting coefficient m y ; The corresponding power index p is the optimal weighting coefficient p y ;

[0024] Based on the optimal weighting coefficient m y and the optimal power index p y , the weight formula W can be determined i , the apparent resistivity value R*(u) of the unknown point is obtained, thereby obtaining the apparent resistivity value of each point in the three-dimensional space, and finally calculating the three-dimensional model of apparent resistivity.

[0025] According to another aspect of the present invention, a coal mine multi-attribute spatial feature modeling device is provided, and the following technical solution is adopted:

[0026] A processing module is used to determine a multi-attribute spatial data point set, organize it into a (x, y, z, R) spatial coordinate data format, process the data point set, and calculate power index related parameters to obtain 10 power index data groups;

[0027] A calculation module is used to calculate the weighted coefficient data under different power exponents according to the control variable method, and calculate the balanced proportion evaluation factor under different parameters;

[0028] The generation module is used to extract the minimum equilibrium ratio evaluation factor, determine the optimal parameters, and generate a multi-attribute 3D model of each point based on the optimal parameters:

[0029]

[0030] Among them, p is the power index, m is the weighting coefficient, and d i is the three-dimensional space distance between the unknown point and the known point, W i is the weight of this point.

[0031] Furthermore, the processing module includes: an acquisition module: used to obtain the apparent resistivity R borehole attribute information, and organize it into a (x, y, z, R) spatial coordinate data format, where x, y, z are three-dimensional spatial coordinates, a total of N known data point sets, a total of n data points, and a longest straight-line distance of data distribution in the study area is L; a numbering module, used to number the data points based on the known data point set N, respectively N1, N2...N n , the apparent resistivity R of the corresponding attributes are R1, R2...R n ; Search module, used to select the unknown point to be calculated as the center of the circle, the search radius r is half of the longest straight line distance of the data distribution in the study area, extract the corresponding known point information for calculation, calculate the power index p, the maximum value of the power index p is 10, the minimum value is 1, the step length Sp = 1, and 10 groups of data are obtained, namely p = 1, p = 2...p = 10 data groups R p1 , R p2 ...R p10 .

[0032] According to another aspect of the present invention, a storage medium is provided, and the following technical solution is adopted:

[0033] A storage medium includes the above-mentioned coal mine multi-attribute spatial feature modeling device. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0035] Figure 1 A flowchart of a method for modeling coal mine multi-attribute spatial characteristics according to an embodiment of the present invention;

[0036] Figure 2 A specific modeling flow chart of a coal mine multi-attribute spatial feature modeling method according to an embodiment of the present invention;

[0037] Figure 3 It is a flow chart of calculating the optimal parameters of weighted coefficients under the power index p=1 of a coal mine multi-attribute spatial feature modeling method according to an embodiment of the present invention;

[0038] Figure 4This is a power exponential system structure diagram of a coal mine multi-attribute spatial feature modeling method described in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0040] Before describing the embodiments, the abbreviations and key terms are defined as follows:

[0041] Apparent resistivity R;

[0042] The longest straight-line distance of data distribution in the study area is L;

[0043] Given a set of data points N, the data points are numbered N1, N2...Nn, and the apparent resistivities R of the corresponding attributes are R1, R2...Rn;

[0044] Apparent resistivity NF of known point data after normalization R , the apparent resistivity ratio of the known point data after normalization is P R1 , P R2 ...P R10 ;

[0045] Apparent resistivity NF of the calculated data after normalization Rc After normalization, the apparent resistivity of the calculated data in each interval is P Rc1 , P Rc2 ...P Rc10 ;

[0046] Apparent resistivity balance ratio evaluation factor P R ;

[0047] Weight Wi, weighting coefficient m, power exponent p, three-dimensional space distance di between unknown point and known point;

[0048] Apparent resistivity balance ratio evaluation factor data set P under p = 1 Rp1 ; Weighting coefficient m when PRp1 is minimum under p=1 min1 , weighting coefficient m when the second minimum value min2 ;

[0049] Search radius r;

[0050] Optimal weighting coefficient m y ; Optimal weighting coefficient p y .

[0051] The calculation formula of inverse distance weighted interpolation is:

[0052]

[0053] Among them, p is the power index, m is the weighting coefficient, di is the three-dimensional space distance between the unknown point and the known point, R*(u) is the unknown point information to be obtained, R(ui) is the known point data, and Wi is the weight of the point. In the three-dimensional geological modeling analysis of coal mines, the core point is to determine the weight of each known calculation point.

[0054] The calculation formula of the equilibrium ratio evaluation factor is:

[0055]

[0056] Among them, P R is the apparent resistivity equilibrium ratio evaluation factor, P R1 is the first interval ratio of apparent resistivity of known point data after normalization, P Rc1 It is the proportion of the first interval of apparent resistivity of the calculated data after normalization.

[0057] Figure 1 The present invention is a flowchart of a method for modeling coal mine multi-attribute spatial characteristics according to an embodiment of the present invention.

[0058] See also Figure 1 As shown, a coal mine multi-attribute spatial feature modeling method includes:

[0059] S101: determining a multi-attribute spatial data point set, organizing it into a (x, y, z, R) spatial coordinate data format, processing the data point set, and calculating power index related parameters to obtain 10 power index data groups;

[0060] S102: Calculate weighted coefficient data under different power exponents according to the control variable method to obtain balanced ratio evaluation factors under different parameters;

[0061] S103: extracting the minimum equilibrium ratio evaluation factor, determining the optimal parameters, and generating a multi-attribute three-dimensional model of each point based on the optimal parameters.

[0062] Specifically, step S101 organizes the attribute information of known boreholes in the coal mine. Here, the apparent resistivity R is taken as an example and organized into a spatial coordinate data format of (x, y, z, R). x, y, z are three-dimensional spatial coordinates. The total known data point set is N, with a total of n data points. The longest straight-line distance of the data distribution in the study area is L. Then, based on the known data point set N, the data points are numbered as N1, N2...N. n , the apparent resistivity R of the corresponding attributes are R1, R2...R n, recorded as array R{n} and normalized, the normalized apparent resistivity is divided into 10 intervals, and the ratio of 10 power intervals is calculated. In step S102, by calculating the absolute value of the difference between the known ratio of each interval and the calculated data ratio of each interval, the apparent resistivity equilibrium ratio evaluation factor of 10 groups of absolute values ​​of the difference between the two groups can be obtained. The equilibrium ratio evaluation factor of different attributes is obtained by repeatedly obtaining the sum of the absolute values ​​of the difference between the two groups. Finally, step S103 is passed, the power index of the resistivity R is calculated for example, and the weighted coefficient array is obtained, and the evaluation factor array is calculated according to the balanced ratio evaluation factor when the power index is 1. And through the evaluation factor array and the weighted coefficient, it is analyzed whether there are other data points between the two when the power index is 1, and the optimal weighted coefficient is obtained. And the weight formula is determined based on the optimal weighted coefficient to obtain the final three-dimensional model.

[0063] Figure 2 The present invention is a specific flow chart of a method for modeling coal mine multi-attribute spatial characteristics according to an embodiment of the present invention.

[0064] join Figure 2 As shown in FIG. 1 , a coal mine multi-attribute spatial feature modeling method includes the following steps:

[0065] Step 201: determine a multi-attribute spatial data point set and organize it into a (x, y, z, R) data format;

[0066] Step 202: Calculate power index related parameters to obtain 10 power index data sets;

[0067] Step 203: Calculate weighted coefficient data under different power exponents using the control variable method;

[0068] Step 204: Calculate and obtain the equilibrium ratio evaluation factor under different parameters;

[0069] Step 205: extract the minimum balance ratio evaluation factor and determine the optimal parameters;

[0070] Step 206: Generate a point multi-attribute 3D model based on the optimal parameters.

[0071] Specifically, in step 201, the attribute information of the known boreholes in the coal mine is sorted. Here, the apparent resistivity R is taken as an example and sorted into the (x, y, z, R) spatial coordinate data format, where x, y, z are three-dimensional spatial coordinates, the total known data point set is N, there are n data points in total, and the longest straight-line distance of the data distribution in the study area is L;

[0072] In step 202, based on the known data point set N, the data points are numbered as N1, N2...Nn, and the apparent resistivities R of the corresponding attributes are R1, R2...Rn;

[0073] Select the unknown point to be calculated as the center of the circle, the search radius r is half of the longest straight-line distance of the data distribution in the study area, extract the corresponding known point information for calculation, calculate the power index p, the maximum value of the power index p is 10, the minimum value is 1, the step length Sp = 1, and 10 groups of data are obtained, namely p = 1, p = 2...p = 10 data groups Rp1, Rp2...Rp10;

[0074] In step 203, further, based on the power index p=1 data group Rp1, the weight coefficient m is calculated and analyzed, the minimum value of the weight coefficient m is 1, the maximum value is L, the weight coefficient calculation step Sm=1, the weight coefficient m is calculated to be an integer, and the weight coefficient data group Rm is obtained;

[0075] In step 204, the apparent resistivities R1, R2, ..., Rn of the known point data are normalized so that the apparent resistivity R of the known point data is distributed in the range of 0 to 1 (Formula 3), and the normalized apparent resistivity NF of the known point data is obtained. R , the normalized apparent resistivity NF R Every 0.1 is divided into 10 intervals NF R1 NF R2 ...NF R10 , the first interval ratio of the known data point is P R1 , calculate the proportion P of each interval R1 , P R2 ...P R10 (Formula 4);

[0076]

[0077] Formula (3) is the normalization formula for apparent resistivity, where R1 is the apparent resistivity of a known point, Rmin is the minimum apparent resistivity among the known points, Rmax is the maximum apparent resistivity among the known points, and FR1 is the normalized value of R1;

[0078]

[0079] (4) is the ratio of the apparent resistivity of the known point in each interval, P Ri is the ratio to be calculated, N(NF Ri ) is the number of normalized known points in different intervals (when i=1, it indicates the number of points with normalized apparent resistivity values ​​in the interval of 0-0.1, and i=2 indicates the number of points in the interval of 0.1-0.2), and N is the total number of known points.

[0080] After that, the calculated apparent resistivity set Rc is normalized so that the apparent resistivity Rc of the data obtained by weight calculation is distributed in the range of 0 to 1 (Formula 5), ​​and the apparent resistivity NF of the calculated data after normalization is obtained.Rc , the apparent resistivity NF generated by normalization calculation Rc Every 0.1 is divided into 10 intervals NF Rc1 NF Rc2 ...NF Rc10 , calculate the proportion of the first interval of data as P Rc1 , get the proportion P of each interval of the calculated data Rc1 , P Rc2 ...P Rc10 (Formula 6);

[0081]

[0082] Among them, formula (5) is the normalized formula of apparent resistivity after calculation, R C1 To calculate the point apparent resistivity, R Cmin is the minimum apparent resistivity at the calculation point, R Cmax is the maximum apparent resistivity at the calculation point, F RC1 is the normalized value of RC1;

[0083]

[0084] Among them, formula (6) is the ratio of the apparent resistivity of the calculated completion point in each interval, P RCi is the ratio to be calculated, N(NF Rci ) is the number of known points after normalization in different intervals (when i = 1, it means the number of points with normalized apparent resistivity values ​​in the interval of 0-0.1), NC is the total number of apparent resistivities after calculation;

[0085] Furthermore, it is known that the first interval ratio of the data point is P R1 And calculate the first interval ratio of data as P Rc1 The absolute value of the difference P R1-Rc1 Similarly, the absolute values ​​of the differences between the second interval, the third interval, and the tenth interval are obtained, which are P R2-Rc2 , P R3-Rc3 ...P R10-Rc10 The sum of the absolute values ​​of the differences between the two groups in 10 groups is the apparent resistivity equilibrium ratio evaluation factor P. R (Formula 2).

[0086] In step 203, step 202 is the equilibrium ratio evaluation method flow, and step 202 is repeated to obtain equilibrium ratio evaluation factors of different properties, such as porosity equilibrium ratio evaluation factor Pφ, analytical water equilibrium ratio evaluation factor PMad, and ash equilibrium ratio evaluation factor PAad.

[0087] In step 204, the apparent resistivity equilibrium ratio evaluation factor data set P under p=1 is extracted. Rp1Minimum weighting factor m min1 , and P Rp1 The weighting coefficient m when the second minimum value min2 , compared with the weighting coefficient m when p = 1 minimum value min1 The weighting coefficient m when the sum is the second smallest value min2 , analyze whether there are other data points in the apparent resistivity equilibrium ratio evaluation factor data set PRp1 under p = 1 between the two data. If so, proceed to the next step. Otherwise, the weighting coefficient m is the minimum value. min1 That is, the optimal parameter m of the weighted coefficient m under p = 1 yp1 .

[0088] Furthermore, the weighting coefficient m is extracted when p=1 is the minimum value. min1 The weighting coefficient m when the sum is the second smallest value min2 The corresponding balanced ratio evaluation factor data set P Rp1 Chinese Data P Rp1 , m = 1 and P Rp1 ,m=2, with weighting coefficient m min1 and m min2 The weighted coefficient step length Sm2 = Sm / 10 is calculated, and the weighted coefficient data set Rm2 is calculated again, and then the apparent resistivity equilibrium ratio evaluation factor data set P calculated for the second time is obtained. Rp1 ;

[0089] Repeat the above step 204 to obtain the optimal parameter m of the weighting coefficient m under the final power index p = 1 yp1 ;

[0090] Repeat step 203 and step 204 above to obtain the optimal parameter m of the weighting coefficient m under different power exponents. yp2 、m yp3 ,...m yp10 ; Compare the apparent resistivity equilibrium ratio evaluation factor P of 10 groups of optimal parameters of weighted coefficient m R , Balanced ratio evaluation factor P R The weighting coefficient m corresponding to the minimum value is the optimal weighting coefficient m y ; The corresponding power index p is the optimal weighting coefficient p y .

[0091] In step 205, based on the optimal weighting coefficient m y and the optimal power index p y , the weight formula Wi can be determined, and the apparent resistivity value R*(u) of the unknown point can be obtained, thereby obtaining the apparent resistivity value of each point in the three-dimensional space, and finally calculating the apparent resistivity three-dimensional model.

[0092] Figure 3The optimal parameter calculation process sequence of the weighted coefficient under the power index p=1 of a coal mine multi-attribute spatial feature modeling method described in an embodiment of the present invention;

[0093] 301: Arrange the data set under the power index p=1;

[0094] 302 weighting coefficient m, value is [1, L], step length Sm = 1, and data set Rm is obtained;

[0095] 303: Calculate the weighted coefficient data set Rm of the polynomial evaluation factor data set P RP1 ;

[0096] 304: Obtain the weighting coefficient m of the minimum value and the second minimum value of the evaluation factor min1 and m min2 ;

[0097] 305: Judge m min1 and m min2 Whether there are other data points;

[0098] 306: Evaluation factor P of minimum and second minimum weight coefficients RP1,m =1 and P RP1,m =2;

[0099] 307: Select area [m min1 ,m min2 ], the new step size is S m =0.1×S m ;

[0100] 308: Calculate and obtain a new weighting coefficient data set Rm2 and an evaluation factor data set P RP1-2 ;

[0101] 309: If there are no other data points in 305, calculate the optimal weighting coefficient m when the power index P=1 yp1 .

[0102] In summary, the inverse weighted interpolation method is used to ensure that the optimal three-dimensional spatial distribution characteristics are finally obtained and the relevant errors are reduced.

[0103] Figure 4 This is a structural diagram of a coal mine multi-attribute spatial feature modeling system described in an embodiment of the present invention.

[0104] join Figure 4As shown, a coal mine multi-attribute spatial feature modeling system includes: a processing module 41, used to determine a multi-attribute spatial data point set, organize it into an unprecedented coordinate data format of (x, y, z, R), process the data point set, and calculate power index related parameters to obtain 10 groups of power index data groups; a calculation module 42, used to calculate the weighted coefficient data under different power indexes according to the control variable method, and calculate the equilibrium proportion evaluation factor under different parameters; a generation module 43, used to extract the minimum equilibrium proportion evaluation factor, determine the most appropriate parameters, and generate a point multi-attribute three-dimensional model based on the most appropriate parameters.

[0105] A storage medium provided by the present invention includes the above-mentioned coal mine multi-attribute spatial feature modeling system.

[0106] The present invention finds a multi-attribute spatial feature modeling method suitable for coal mines by calculating the inverse distance weighted interpolation and adopting the balanced proportion evaluation method, thereby reducing the problem of the final attribute three-dimensional spatial distribution feature data error caused by human subjective factors.

[0107] The above only describes some exemplary embodiments of the present embodiment by way of illustration. It is undoubted that, for those skilled in the art, the described embodiments can be modified in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A coal mine multi-attribute spatial feature modeling method, characterized in that: include: Determine a multi-attribute spatial data point set, organize it into (x, y, z, R) spatial coordinate data format, process the data point set, and calculate power index related parameters to obtain 10 power index data sets; The weighted coefficient data under different power exponents are calculated according to the control variable method, and the balanced proportion evaluation factors under different parameters are calculated; Extract the minimum equilibrium ratio evaluation factor, determine the optimal parameters, and generate a multi-attribute 3D model of each point based on the optimal parameters: Among them, p is the power index, m is the weighting coefficient, and d i is the three-dimensional space distance between the unknown point and the known point, W i is the weight of this point.

2. The multi-attribute spatial feature modeling method according to claim 1, characterized in that: The multi-attribute spatial data point set is determined, sorted into (x, y, z, R) spatial coordinate data format, the data point set is processed, and power index related parameters are calculated to obtain 10 groups of power index data groups including: Step 1: Obtain the borehole attribute information of apparent resistivity R and organize it into (x, y, z, R) spatial coordinate data format, where x, y, z are three-dimensional spatial coordinates, the total known data point set is N, there are n data points in total, and the longest straight-line distance of data distribution in the study area is L; Step 2: Based on the known data point set N, number the data points as N1, N2...N n , the apparent resistivity R of the corresponding attributes are R1, R2...R n ; Step 3: Select the unknown point to be calculated as the center of the circle, the search radius r is half of the longest straight line distance of the data distribution in the study area, extract the corresponding known point information for calculation, calculate the power index p, the maximum value of the power index p is 10, the minimum value is 1, the step length Sp = 1, and 10 groups of data are obtained, namely p = 1, p = 2...p = 10 data groups R p1 , R p2 ...R p10 .

3. The multi-attribute spatial feature modeling method according to claim 1, characterized in that: The weighted coefficient data under different power exponents are calculated according to the control variable method to obtain the balanced ratio evaluation factors under different parameters, including: Step 4: At power index p = 1, data set R p1 The weighted coefficient m is calculated and analyzed based on the above. The minimum value of the weighted coefficient m is 1 and the maximum value is L. The step length of the weighted coefficient is S. m =1, calculate the weighting coefficient m as an integer, and obtain the weighting coefficient data set R m , using the following formula: Among them, R*(u) is the unknown point information to be obtained, R(ui) is the known point data, and W i is the weight of the point; Step 5: The apparent resistivity R1, R2...R n Normalize the data so that the apparent resistivity R of the known point data is distributed in the range of 0 to 1, and obtain the apparent resistivity NF of the known point data after normalization. R , the normalized apparent resistivity NF R Every 0.1 is divided into 10 intervals NF R1 NF R2 ...NF R10 , the first interval ratio of the known data point is P R1 , calculate the proportion P of each interval R1 , P R2 ...P R10 ; Formula (3) is the normalization formula for apparent resistivity, where R1 is the apparent resistivity of a known point, R min is the minimum apparent resistivity among the known points, R max is the maximum apparent resistivity at a known point, F R1 is the normalized value of R1; Formula (4) is the ratio of the apparent resistivity of the known point in each interval, P Ri is the ratio to be calculated, N(NF Ri ) is the number of normalized known points in different intervals (when i=1, it means the number of points with normalized apparent resistivity values ​​in the interval of 0-0.1), and N is the total number of known points; Step 6: Normalize the calculated apparent resistivity set Rc so that the apparent resistivity Rc of the data obtained by weight calculation is distributed in the range of 0 to 1, and obtain the apparent resistivity NFRc of the normalized calculated data. The apparent resistivity NFRc generated by the normalized calculation is divided into 10 intervals NFRc1, NFRc2...NFRc10 every 0.1, and the proportion of the first interval of the calculated data is PRc1, and the proportion of each interval of the calculated data is PRc1, PRc2...PRc10; Among them, formula (5) is the normalized formula of the calculated apparent resistivity, RC1 is the apparent resistivity of the calculation point, RCmin is the minimum apparent resistivity of the calculation point, RCmax is the maximum apparent resistivity of the calculation point, and FRC1 is the normalized value of RC1; Among them, formula (6) is the ratio of the apparent resistivity of the calculated completion point in each interval, P RCi is the ratio to be calculated, N(NF Rci ) is the number of known points after normalization in different intervals (when i = 1, it means the number of points with normalized apparent resistivity values ​​in the interval of 0-0.1), NC is the total number of apparent resistivities after calculation; Step 7: The absolute value P of the difference between the first interval ratio of the known data point PR1 and the first interval ratio of the calculated data PRc1 R1 -R c1 Similarly, the absolute values ​​of the differences between the second interval, the third interval, and the tenth interval are obtained, which are P R2 -R c 2. P R3 -R c3 ...P R10 -R c10 The sum of the absolute values ​​of the differences between the two groups in 10 groups is the apparent resistivity equilibrium ratio evaluation factor P. R .

4. The coal mine multi-attribute spatial feature modeling method according to claim 1, characterized in that: The weighted coefficient data under different power exponents are calculated according to the control variable method to obtain the balanced ratio evaluation factor under different parameters, which also includes: Step 8: Repeat steps 5 to 7 to obtain the equilibrium ratio evaluation factors of different properties, such as the porosity equilibrium ratio evaluation factor Pφ, the analytical water equilibrium ratio evaluation factor PMad, and the ash equilibrium ratio evaluation factor PAad; Step 9: According to the balanced ratio evaluation method flow, based on the weighted coefficient data set Rm, the apparent resistivity balanced ratio evaluation factor data set P under p = 1 is calculated Rp1 ; Step 10: Extract the apparent resistivity equilibrium ratio evaluation factor data set P under p = 1 Rp1 Minimum weighting factor m min1 , and P Rp The weighting coefficient m for the first small value min 2. Compare the weighting coefficient m when p=1 minimum value min1 The weighting coefficient m when the sum is the second smallest value min2 , analyze whether there are other data points in the apparent resistivity equilibrium ratio evaluation factor data set PRp1 under p = 1 between the two data. If so, proceed to step 11. Otherwise, the weighting coefficient m is the minimum value. min1 That is, the optimal parameter m of the weighted coefficient m under p = 1 yp1 ; Step 11: Extract the weighting coefficient m when p=1 is the minimum value min1 The weighting coefficient m when the sum is the second smallest value min2 The corresponding balanced ratio evaluation factor data set P Rp1 Chinese Data P Rp1 , m = 1 and P Rp1 ,m=2, with weighting coefficient m min1 and m min2 The weighted coefficient step length Sm2 = Sm / 10 is calculated, and the weighted coefficient data set Rm2 is calculated again, and then the apparent resistivity equilibrium ratio evaluation factor data set P calculated for the second time is obtained. Rp1 .

5. The coal mine multi-attribute spatial feature modeling method according to claim 1, characterized in that: The extracting of the minimum equilibrium ratio evaluation factor, determining the optimal parameters, and generating the multi-attribute three-dimensional model of each point based on the optimal parameters includes: Repeat steps 10 to 11 to obtain the optimal parameter m of the weighting coefficient m under the final power index p = 1 yp1 ; Repeat steps 9 to 11 to obtain the optimal parameter m of the weighting coefficient m under different power exponents. yp2 、m yp3 ,...m yp10 ; Compare the apparent resistivity equilibrium ratio evaluation factor P of 10 groups of optimal parameters of weighted coefficient m R , Balanced ratio evaluation factor P R The weighting coefficient m corresponding to the minimum value is the optimal weighting coefficient m y ; The corresponding power index p is the optimal weighting coefficient p y ; Based on the optimal weighting coefficient m y and the optimal power index p y , the weight formula W can be determined i , the apparent resistivity value R*(u) of the unknown point is obtained, thereby obtaining the apparent resistivity value of each point in the three-dimensional space, and finally calculating the three-dimensional model of apparent resistivity.

6. A coal mine multi-attribute spatial feature modeling device, characterized in that: include: A processing module is used to determine a multi-attribute spatial data point set, organize it into a (x, y, z, R) spatial coordinate data format, process the data point set, and calculate power index related parameters to obtain 10 power index data groups; A calculation module is used to calculate the weighted coefficient data under different power exponents according to the control variable method, and calculate the balanced proportion evaluation factor under different parameters; The generation module is used to extract the minimum equilibrium ratio evaluation factor, determine the optimal parameters, and generate a multi-attribute 3D model of each point based on the optimal parameters: Among them, p is the power index, m is the weighting coefficient, d i is the three-dimensional space distance between the unknown point and the known point, W i is the weight of this point.

7. The multi-attribute spatial feature modeling device according to claim 1, characterized in that: The processing module comprises: Acquisition module: used to obtain the apparent resistivity R borehole attribute information, organized into (x, y, z, R) spatial coordinate data format, x, y, z are three-dimensional spatial coordinates, a total of N known data points, a total of n data points, the longest straight-line distance of the data distribution in the study area is L; The numbering module is used to number the data points N1, N2...N based on the known data point set N. n , the apparent resistivity R of the corresponding attributes are R1, R2...R n ; The search module is used to select the unknown point to be calculated as the center of the circle. The search radius r is half of the longest straight line distance of the data distribution in the study area. The corresponding known point information is extracted for calculation. The power index p is calculated. The maximum value of the power index p is 10 and the minimum value is 1. The step length Sp = 1, and 10 groups of data are obtained, namely p = 1, p = 2...p = 10 data groups R p1 , R p2 ...R p10 .

8. A storage medium, characterized in that: It comprises the private protocol application identification device as described in any one of claims 6-7.

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