Method for predicting overall distribution curve of blasting lumpiness by considering blasting in-situ block

Abstract

The invention discloses a blasting lumpiness overall distribution curve prediction method considering blasting in-situ blocks. The method comprises the following steps: obtaining structural surface distribution characteristics of a rock mass in a to-be-blasted area; constructing a three-dimensional numerical model of the rock mass based on the distribution characteristics of the structural surface; blasting design parameters are introduced into the three-dimensional numerical model, and the segmentation characteristics of the rock mass under the combined action of the structural plane and blast hole arrangement are analyzed; according to the segmentation features, blasting in-situ block information is extracted, and the size is counted; defining the maximum size of the blasting in-situ block as the size upper limit of an R-R distribution function; establishing a mathematical relationship between the average size of the blasting in-situ blocks and the characteristic curve parameters in the R-R distribution function; based on the size upper limit and the mathematical relationship, constructing a prediction model of the overall distribution curve of the rock lumpiness after blasting; and predicting the blasting fragmentation overall distribution curve according to the prediction model to obtain a prediction result. According to the method, the accuracy and practicability of blasting lumpiness overall distribution prediction are improved.

Description

Technical Field

[0001] This invention belongs to the field of rock blasting, and particularly relates to a method for predicting the overall distribution curve of blasted block size considering in-situ blasted blocks. Background Technology

[0002] The blasting block size distribution curve refers to the cumulative mass distribution curve of the blasted block size under the sieve, used to describe the overall block size distribution of the blast pile. If a clear functional expression can characterize the relationship between the blast pile distribution curve and the blasting parameters, the overall block size distribution of the blast pile can be predicted before detonation.

[0003] Currently, the most commonly used distribution function for predicting blasting block size in engineering is the RR distribution function. However, the RR distribution function has drawbacks such as the distribution curve tending towards infinity at the large rock ends and the parameters not considering the rock mass structure. To improve these shortcomings, researchers have proposed introducing the maximum blasting block size x. max A three-parameter cumulative function Swecbrc distribution curve was constructed. However, on the one hand, the three parameters increase the computational complexity and instability; on the other hand, x... max The value is taken as the smaller of the maximum size of the in-situ block, the row spacing of the blast holes, and the spacing between the holes. However, in actual blasting, the maximum size of the block after blasting is often smaller than these three values, resulting in inaccurate results.

[0004] Primary structural planes (such as joints and fissures) within a rock mass constitute intersecting structural planes, dividing the undisturbed rock mass into in-situ rock blocks with specific geometric dimensions. The essence of rock blasting is to utilize explosive energy to drive cracks to preferentially propagate and penetrate along these natural "pre-fabricated fracture surfaces," generating new fractures and thus separating the in-situ rock blocks from the parent rock, forming a group of movable fragments after blasting. Therefore, the size distribution of in-situ blocks influences and even determines the final size distribution of the blasted rock blocks. However, existing methods for determining in-situ block sizes are derived from geological survey methods and do not consider the influence of blasting design parameters, resulting in determined in-situ block sizes that are not applicable to the blasting field. Therefore, the shortcomings of existing distribution curves and the lack of a definition for in-situ blasted block sizes restrict research on predicting the overall block size distribution curve in field blasting. Currently, there is a lack of a method for predicting the overall block size distribution curve that considers the size of in-situ blocks in the blasted rock mass. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for predicting the overall distribution curve of blasted block size considering in-situ blasted blocks, comprising: Obtain the structural surface distribution characteristics of the rock mass in the area to be blasted; Based on the structural surface distribution characteristics, a three-dimensional numerical model of the rock mass is constructed; Blasting design parameters are introduced into the three-dimensional numerical model to analyze the segmentation characteristics of the rock mass under the combined effect of structural planes and borehole layout. Based on the segmentation features, extract the in-situ blasted block information and calculate its size; The maximum size of the blasted in-situ block is defined as the upper limit of the size of the RR distribution function; Establish the mathematical relationship between the average size of the in-situ blasted block and the parameters of the characteristic curve in the RR distribution function; Based on the aforementioned size limit and the aforementioned mathematical relationship, a predictive model for the overall distribution curve of rock block size after blasting is constructed. The overall distribution curve of blasted block size is predicted based on the prediction model, and the prediction results are obtained.

[0006] Preferably, the process of obtaining the structural plane distribution characteristics of the rock mass in the area to be blasted includes: Collect structural surface image data of the area to be blasted; Extract the geometric parameters of the structural surfaces, including dip, dip angle, trace length, and spacing; Statistical analysis was performed on the geometric parameters to establish a probability distribution model for each group of structural surfaces.

[0007] Preferably, the process of constructing a three-dimensional numerical model of the rock mass includes: Based on a probability distribution model, the Monte Carlo method is used to randomly generate structured surface networks; By embedding the structural surface network into three-dimensional space, a three-dimensional numerical model of the rock mass that reflects the actual geological conditions is obtained.

[0008] Preferably, the process of analyzing the segmentation characteristics of the rock mass under the combined effect of structural planes and borehole arrangement includes: Blasting design parameters are introduced into the three-dimensional numerical model; wherein, the blasting design parameters include borehole position, spacing, and row spacing; Simulates the cutting effect of structural planes and blast-induced fractures on the rock mass during blasting; Identify and extract the rock blocks formed by the cutting as in-situ blasting blocks.

[0009] Preferably, the process of extracting information about the in-situ blasted blocks and calculating their dimensions includes: Statistical analysis of the volume and equivalent particle size of all blasted in-situ blocks; Determine the maximum and average size of the in-situ blasted block; Generate the size distribution curve of the blasted in-situ block.

[0010] Preferably, the process of defining the maximum size of the blasted in-situ block as the upper limit of the size of the RR distribution function includes: The size variable in the RR distribution function is restricted to not exceeding the maximum size of the blasted in-situ block; The expression for the RR distribution function is modified so that it cuts off at the maximum size of the blasted in-situ block.

[0011] Preferably, the process of establishing the mathematical relationship between the average size of the blasted in-situ block and the parameters of the characteristic curve in the RR distribution function includes: A formula for calculating the parameters of the characteristic curve is constructed using the average size of the in-situ blasted block as the independent variable. By introducing proportionality coefficients and exponential coefficients, we obtain parameterized mathematical relationships.

[0012] Preferably, the mathematical relationship is calibrated using field test data, including: Blasting tests were conducted under different rock mass structures and blasting parameters. Obtain actual block size distribution data and inversely determine the values ​​of the proportional coefficient and exponential coefficient.

[0013] Preferably, the process of constructing a predictive model for the overall distribution curve of rock fragment size after blasting includes: By combining the aforementioned upper limit of size and the aforementioned mathematical relationship, a modified RR distribution function is formed; The modified RR distribution function is used as a prediction model for the overall distribution of blasting block size.

[0014] Preferably, the method is implemented before blasting to predict the block size distribution of the blast pile and guide the optimization design of blasting parameters.

[0015] Compared with the prior art, the present invention has the following advantages and technical effects: This invention provides a method for predicting the overall distribution curve of rock block size after blasting. Based on obtaining the distribution characteristics of rock mass structural surfaces in the area to be blasted, a three-dimensional numerical model of the rock mass in the area is constructed according to the Monte Carlo method. By analyzing the segmentation state of the rock mass under the combined effect of structural surfaces and borehole arrangement, the initial fragmentation characteristics of the rock mass are quantitatively characterized using in-situ blasting block parameters. The maximum size of the in-situ blasting block is defined as the upper limit of the RR distribution function, and a mathematical relationship is established between the average size of the in-situ block and the characteristic curve parameters in the distribution function. In this way, a predictive model for the overall distribution curve of rock block size after blasting is constructed, thereby improving the accuracy and practicality of predicting the overall distribution of blasted block size.

[0016] This invention integrates the "in-situ rock mass block size" system into the blasting block size prediction process, realizing full-chain modeling from "structural plane - blasting parameters - in-situ block - distribution function". This makes the prediction results naturally constrained by the true upper limit of the rock mass, significantly reducing the risk of underestimation of ultra-large blocks caused by the unbounded assumption of the traditional RR model, and providing a unique and stable reliable block size distribution benchmark curve for blasting design.

[0017] This invention combines on-site images with probabilistic statistics, enabling quantitative understanding of the geometric information of natural discontinuities in the rock mass before blasting. This avoids reliance on empirical assumptions in subsequent modeling, thereby improving the consistency between the three-dimensional numerical model and the actual geological conditions, and reducing prediction errors caused by deviations in geological input.

[0018] This invention utilizes Monte Carlo 3D modeling and generates a network of tens of millions of structural surfaces in one go using random sampling. It can reproduce the complex cutting relationships of real rock masses in a computer, providing a high-fidelity geometric carrier for subsequent "blasting + original rock" coupled segmentation calculations, and making the prediction results spatially interpretable.

[0019] This invention explicitly introduces the blasting effect into the original rock model, and for the first time unifies and quantifies "blast-induced fractures" and "original structural surfaces," solving the problem that traditional predictions only consider blasting energy and ignore the existing block size of the original rock, making the prediction starting point closer to the actual pre-blast state. Attached Figure Description

[0020] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the orientation of a certain structural surface set according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a three-dimensional geological numerical model of the blasting area according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a three-dimensional numerical model considering blasting parameters in an embodiment of the present invention; Figure 4 This is a schematic diagram comparing the prediction results of the new model and the Kuznetsov model in an embodiment of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0023] like Figure 1 As shown, this embodiment provides a method for predicting the overall distribution curve of blasted block size considering in-situ blasted blocks, including: Obtain the structural surface distribution characteristics of the rock mass in the area to be blasted; Based on the distribution characteristics of structural planes, a three-dimensional numerical model of the rock mass is constructed; Blasting design parameters are introduced into a three-dimensional numerical model to analyze the segmentation characteristics of rock mass under the combined effect of structural planes and borehole layout. Based on the segmentation features, extract the information of the in-situ blasted blocks and calculate their dimensions; The maximum size of the blasted in-situ block is defined as the upper limit of the size of the RR distribution function; Establish the mathematical relationship between the average size of the in-situ blasted block and the parameters of the characteristic curve in the RR distribution function; Based on the upper limit of size and mathematical relationships, a predictive model for the overall distribution curve of rock block size after blasting is constructed. The overall distribution curve of blasted block size is predicted based on the prediction model, and the prediction results are obtained.

[0024] Furthermore, the process of obtaining the structural plane distribution characteristics of the rock mass in the area to be blasted includes: Collect structural surface image data of the area to be blasted; Extract the geometric parameters of the structural surfaces, including dip, dip angle, trace length, and spacing; Statistical analysis of geometric parameters was performed to establish probability distribution models for each group of structural surfaces.

[0025] Furthermore, in this embodiment, before each blasting operation, image data of the free face is collected, and combined with the description of the fracture development characteristics of the blasting area in the geological exploration report, the geometric parameters of the original structural surface are extracted, including information such as dip direction, dip angle, trace length and spacing.

[0026] Structures with similar attitudes are grouped into a single structure group, and a probabilistic model of the geometric parameters for each group is determined. By statistically grouping the collected primary structure information, the dominant orientation of the structure is identified, thus determining the group classification of the structure.

[0027] Based on the statistical analysis of structural plane groupings, statistical analysis is performed on the dip and dip angle of each structural plane group. For example... Figure 2 As shown, the distribution histograms of the dip and tilt angles of a certain structural surface group and their fitted probability density curves are displayed. It can be seen that the fitted functions of the dip and tilt angles of this structural surface group both follow a normal distribution.

[0028] Furthermore, the process of constructing a three-dimensional numerical model of the rock mass includes: Based on a probability distribution model, the Monte Carlo method is used to randomly generate structured surface networks; By embedding the structural surface network into three-dimensional space, a three-dimensional numerical model of the rock mass that reflects the actual geological conditions is obtained.

[0029] Furthermore, in this embodiment, generating a structural surface in a three-dimensional numerical model requires inputting the dip direction, dip angle, spacing, and trace length information of that structural surface. After determining the fitting function f(x) for the structural surface parameters of the area to be blasted through on-site investigation, a random number generation method is used to obtain variables conforming to the relevant distribution form. That is, generating the structural surface dip direction, dip angle, spacing, and trace length information based on the distribution function is called the Monte Carlo method. (1) In the formula v i j i , t i and c i These represent the dip direction, dip angle, spacing, and trace length of the i-th structural surface, respectively.

[0030] The generated structural surface information is then input into numerical simulation software to generate a three-dimensional numerical geological model constructed from the original structural surfaces, such as... Figure 2 As shown. Considering blasting parameters, based on the assumption that interconnected fractures will form between the centers of the blast holes, the regenerated fractures formed by the blasting network are taken into account, thus generating a blasting structure surface that conforms to the hole spacing, hole row spacing, and hole depth. For example, if 9 blast holes are evenly distributed in 3 rows, with 3 blast holes in each row, a hole spacing of 4m, a row spacing of 3m, and a hole depth of 8m, then a three-dimensional numerical geological model considering blasting parameters is established as follows. Figure 3 As shown, the area to be blasted is divided by the initial structural plane and the blast-induced fracture structural plane, and the resulting maximum block size is definitely smaller than the maximum size of the initial in-situ block, the borehole spacing, and the borehole row spacing.

[0031] Furthermore, the process of analyzing the segmentation characteristics of the rock mass under the combined effect of structural planes and borehole arrangement includes: Blasting design parameters are introduced into the three-dimensional numerical model; these parameters include borehole location, spacing, and row spacing. Simulates the cutting effect of structural planes and blast-induced fractures on the rock mass during blasting; Identify and extract the rock blocks formed by the cutting as in-situ blasting blocks.

[0032] Furthermore, the process of extracting information about the in-situ blasted blocks and calculating their dimensions includes: Statistical analysis of the volume and equivalent particle size of all blasted in-situ blocks; Determine the maximum and average size of the in-situ blasted block; Generate the size distribution curve of the blasted in-situ block.

[0033] This embodiment directly outputs the maximum usable block size X of the rock mass before blasting. B-max and average block size X BThis provides a physical upper limit and characteristic scale for the subsequent distribution function, avoiding the lengthy process of deriving the upper limit through multiple rounds of blasting tests required by traditional empirical formulas, thus saving engineering time and costs.

[0034] Furthermore, the process of defining the maximum size of the blasted in-situ block as the upper limit of the size of the RR distribution function includes: The size variable in the RR distribution function is restricted to not exceeding the maximum size of the in-situ blasted block; The expression for the RR distribution function is modified so that it cuts off at the maximum size of the in-situ blasted block.

[0035] Furthermore, this embodiment, based on the construction of a three-dimensional model reflecting both the original structural plane and the blast-induced fracture structural plane, extracts in-situ block size information considering blasting parameters based on numerical simulation results. The sizes of all segmented rock blocks are statistically analyzed, and the original block size distribution curve of the area to be blasted is plotted. Based on this, the maximum size X of all original blocks considering blasting is obtained. B-max and average size X B .

[0036] The RR distribution function is a simple two-parameter distribution with a straightforward form, making it widely used for predicting pre-blast block size distribution. Its cumulative distribution curve is expressed as follows: (2) In the formula x 50 The sieve size is the screening size when the cumulative mass under the blasted block size equals 50%, and is called the characteristic size parameter. n is called the uniformity parameter, which, as a characteristic curve parameter, determines the shape of the cumulative distribution curve. x represents the cumulative mass under the blasted block size when F is reached. RR The screening size corresponding to *100% has no upper limit in the initial RR distribution function, which reduces the prediction accuracy of the distribution function. By using the size of the blasted in-situ block as the upper limit of the screening size, the RR distribution function formula is revised as follows: (3) In this embodiment, "X" B-max As a correction to the "RR size upper limit," the theoretical curve, which originally tended towards infinity, is truncated at the physically achievable maximum value, eliminating the "artificial height" tail at the far end, and making the predicted curve >X. B-max Automatic zeroing of intervals improves the prediction accuracy of large blocks and reduces the amount of secondary crushing in mines.

[0037] Furthermore, the process of establishing the mathematical relationship between the average size of the blasted in-situ block and the parameters of the characteristic curve in the RR distribution function includes: A formula for calculating the parameters of the characteristic curve is constructed using the average size of the in-situ blasted block as the independent variable. By introducing proportionality coefficients and exponential coefficients, we obtain parameterized mathematical relationships.

[0038] Furthermore, in this embodiment, the formula for calculating the characteristic curve parameter n in the RR distribution function is as follows: (4) In the formula, B is the borehole spacing (m); D is the borehole diameter (m); S is the borehole spacing (m); W is the standard deviation of drilling accuracy (m); L is the borehole depth (m); and H is the step height (m). The calculation of n does not consider the rock mass structure characteristics of the blasting area. That is, the same blasting design parameters and the same x are used for different geological areas. 50 When the overall distribution of blasted blocks is completely uniform, the results will deviate significantly from reality. Therefore, the average distribution size of in-situ blasted blocks is introduced to modify the characteristic curve distribution parameter n'. (5) In the formula, k is the proportionality coefficient and k1 is the exponential coefficient. k and k1 are unknowns. Substituting the field investigation and blasting design parameters, the final expression for n' can be calculated as follows: (6) Formulas (3), (4), and (6) together constitute a new method for predicting the overall distribution curve of blasted block size. The new characteristic curve distribution function not only defines the upper limit of the RR distribution function through the maximum size of the in-situ blasted block, but also considers the structural characteristics of the rock mass, thus resolving the shortcomings of the initial RR distribution without increasing the parameters, thereby improving the accuracy and practicality of predicting the overall distribution of blasted block size. Comparison of the initial RR distribution, the new model, and field measurement data is shown below. Figure 4 As shown, the new model significantly improves prediction accuracy.

[0039] This embodiment establishes a mathematical relationship between the average size of the in-situ block and the characteristic parameters, directly writing the rock mass structure information into the distribution function exponent. This achieves the physical correspondence that "the more complete the rock mass, the larger n′, and the steeper the curve," overcoming the shortcomings of traditional RR uniformity parameter n, which can only be obtained through test blast regression and lacks physical meaning.

[0040] Furthermore, the mathematical relationships were calibrated using field experimental data, including: Blasting tests were conducted under different rock mass structures and blasting parameters. Obtain actual block size distribution data and inversely determine the values ​​of the proportional coefficient and exponential coefficient.

[0041] In this embodiment, the field test calibration coefficients can be locked with only a small number of blasting tests to determine the proportional coefficient k and the exponential coefficient k1. The coefficients can be directly applied to different blocks in the same mining area, achieving "one-time calibration and multiple reuses", which significantly reduces the test cost and ensures the reliability of the model's extrapolation under the same geological environment.

[0042] Furthermore, the process of constructing a predictive model for the overall distribution curve of rock fragment size after blasting includes: By combining the upper limit of the combined size and mathematical relationships, a modified RR distribution function is formed; The modified RR distribution function is used as the prediction model for the overall distribution of blasting block size.

[0043] This embodiment uses the "modified RR distribution function" as the final prediction model. While maintaining the advantage of simple two-parameter form, it contains dual physical constraints of upper limit and rock mass structure. It can directly output the cumulative mass fraction of any screen after blasting, providing real-time block size input for subsequent processes such as mineral processing, transportation, and heap leaching, and opening up an integrated optimization interface for "blasting-mineral processing".

[0044] Furthermore, the method is implemented before blasting to predict the block size distribution of the blast pile and guide the optimization design of blasting parameters.

[0045] This embodiment enables mines to obtain the future blasting block size distribution before drilling and charging. On-site, parameters such as hole pattern, unit consumption, and delay can be adjusted accordingly to achieve closed-loop control of "prediction before blasting," reducing the number of trial blasts, lowering energy consumption, carbon emissions, and safety costs, and achieving both economic and environmental benefits.

[0046] This embodiment proposes a method for predicting the overall distribution curve of rock block size after blasting. Based on the investigation of the distribution law of rock mass structural planes in the area to be blasted, a three-dimensional numerical model of the rock mass in the area is constructed based on the Montauka principle. Then, blasting design parameters are introduced into the model, and the segmentation characteristics of the rock mass under the combined action of structural planes and borehole layout are analyzed through the model. The segmentation characteristics are quantitatively described using in-situ blasting block parameters. The maximum size of the in-situ blasting block is defined as the upper limit of the RR distribution function, and the relationship between the average size of the in-situ blasting block and the parameters in the distribution function is established. Thus, a new method for predicting the overall distribution curve of rock block size after blasting is constructed, which improves the accuracy and practicality of predicting the overall distribution of blasted block size.

[0047] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the overall distribution curve of the fragmentation of a blast considering the fragmentation of the in-situ blocks of a blast, characterized by, The method comprises the following steps: obtaining the structural plane distribution characteristics of the rock mass in the blasting area; constructing a three-dimensional numerical model of the rock mass based on the structural plane distribution characteristics; introducing blasting design parameters into the three-dimensional numerical model to analyze the segmentation characteristics of the rock mass under the combined action of the structural plane and the blasthole arrangement; extracting the blasting in-situ block information and counting the size according to the segmentation characteristics; defining the maximum size of the blasting in-situ block as the size upper limit of the R-R distribution function; establishing a mathematical relationship between the average size of the blasting in-situ block and the characteristic curve parameters in the R-R distribution function; constructing a prediction model of the overall distribution curve of the rock block size after blasting based on the size upper limit and the mathematical relationship; predicting the overall distribution curve of the blasting block size according to the prediction model to obtain a prediction result.

2. The method according to claim 1, wherein the process of obtaining the structural plane distribution characteristics of the rock mass in the blasting area comprises: collecting structural plane image data of the blasting area; extracting the geometric parameters of the structural plane, including the trend, dip angle, trace length and spacing; statistically analyzing the geometric parameters to establish a probability distribution model of each group of structural planes.

3. The method according to claim 1, wherein the process of constructing a three-dimensional numerical model of the rock mass comprises: generating a structural plane network randomly based on the probability distribution model using the Monte Carlo method; embedding the structural plane network in a three-dimensional space to obtain a three-dimensional numerical model of the rock mass reflecting the actual geological conditions.

4. The method according to claim 1, wherein the process of analyzing the segmentation characteristics of the rock mass under the combined action of the structural plane and the blasthole arrangement comprises: introducing blasting design parameters into the three-dimensional numerical model; wherein the blasting design parameters include blasthole position, spacing and row spacing; simulating the cutting effect of the structural plane and the blast-induced fracture on the rock mass during blasting; identifying and extracting the rock blocks formed by cutting as blasting in-situ blocks.

5. The method according to claim 1, wherein the process of extracting the blasting in-situ block information and counting the size comprises: counting the volume and equivalent particle diameter of all blasting in-situ blocks; determining the maximum size and average size of the blasting in-situ block; generating a size distribution curve of the blasting in-situ block.

6. The method according to claim 1, wherein the process of defining the maximum size of the blasting in-situ block as the size upper limit of the R-R distribution function comprises: limiting the size variable in the R-R distribution function to not more than the maximum size of the blasting in-situ block; correcting the expression of the R-R distribution function so that it is cut off at the maximum size of the blasting in-situ block.

7. The method according to claim 1, wherein the process of establishing a mathematical relationship between the average size of the blasting in-situ block and the characteristic curve parameters in the R-R distribution function comprises: constructing a calculation formula of the characteristic curve parameters with the average size of the blasting in-situ block as the independent variable; introducing a proportional coefficient and an exponential coefficient to obtain a parameterized mathematical relationship.

8. The method according to claim 1, wherein the mathematical relationship is calibrated through field test data, comprising: conducting blasting tests under different rock mass structures and blasting parameters; ​ ​ ​ ​ ​ ​ ​ The actual block size distribution data is acquired, and the inversion is performed to determine the values of the proportional coefficient and the exponential coefficient.

9. The method of claim 1, wherein, The process of constructing the prediction model of the overall distribution curve of the rock block size after blasting comprises: The upper limit of the size and the mathematical relationship are combined to form a corrected R-R distribution function; The corrected R-R distribution function is used as the prediction model of the overall distribution of the blasting block size.

10. The method of claim 1, wherein, The method is implemented before blasting, and is used for predicting the block size distribution of the blasting pile and guiding the optimization design of the blasting parameters.

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