A method for quantitatively evaluating crushing energy consumption based on the distribution of explosive pile size

Abstract

The application provides a kind of based on the broken energy consumption quantification evaluation method of blasting pile size distribution, it is related to the technical field of mining engineering, including the following steps: the size distribution data acquisition and energy consumption collection of blasting pile;Discrete element simulation and energy consumption-size distribution relationship modeling;Model energy consumption correction and actual energy consumption mapping;Target size distribution determination and characteristic curve construction;Size distribution curve form difference quantification;Four-level quantification evaluation system construction.The application generates size sample according to the size distribution of on-site blasting pile, discrete element simulation and multivariate nonlinear regression modeling, combined with the correction factor matrix calibrated by field data, so that the deviation between model energy consumption and actual energy consumption is controlled within 5%, and high-precision quantification of crushing energy consumption is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mining engineering, in particular, especially to a broken energy consumption quantitative evaluation method based on blast pile size distribution. BACKGROUND

[0002] In the process of mining, the quantitative relationship between blast pile size distribution and broken energy consumption has always been an industry difficulty. The traditional broken energy consumption evaluation method often lacks systematic analysis of the characteristics of blast pile size distribution, and it is difficult to accurately establish the mapping relationship between size parameters (such as median particle size, characteristic particle size, uniformity, etc.) and energy consumption. In the prior art, the correlation analysis of size distribution and broken energy consumption lacks accurate quantitative models, and the evaluation system is highly subjective, making it difficult to realize the coordinated optimization of blasting and crushing process. There is a deviation between the discrete element simulation energy consumption and the actual production energy consumption, and there is a lack of quantitative means for the shape difference of the size distribution curve, which makes it impossible to form a scientific energy consumption evaluation system and blasting parameter optimization strategy. Therefore, it is urgent to establish an energy consumption quantitative evaluation method that combines numerical simulation, mathematical modeling and pattern recognition to provide a scientific basis for mine energy saving and consumption reduction. SUMMARY

[0003] According to the technical problems mentioned in the above background art, a broken energy consumption quantitative evaluation method based on blast pile size distribution is provided. The present application realizes accurate prediction of broken energy consumption by obtaining blast pile size distribution data and combining discrete element simulation to construct energy consumption-size distribution relationship model and correction mechanism; at the same time, by quantifying the shape difference of the size distribution curve, a comprehensive evaluation system is established to provide a scientific basis for mine blasting parameter optimization and crushing equipment control, so as to reduce broken energy consumption and improve production efficiency.

[0004] The technical means adopted by the present application are as follows:

[0005] A broken energy consumption quantitative evaluation method based on blast pile size distribution, comprising the following steps:

[0006] Step 1: blast pile size distribution data acquisition and energy consumption collection; obtain the size distribution data of the blast pile, simultaneously collect the operating parameters of the crusher, and calculate the actual broken energy consumption of the crusher; the size distribution data of the blast pile includes: size and corresponding size percentage of undersize;

[0007] Step 2: Discrete element simulation and energy consumption-size distribution relationship modeling; a three-dimensional model of the crusher is constructed in the EDEM software, key parameters are set according to the simulation of real on-site crusher parameters, different size distribution samples are generated according to the on-site stockpile distribution, the simulation energy consumption under the simulation experiment conditions set according to different stockpile size distribution samples is obtained through discrete element simulation, a size distribution feature vector is defined, and a relationship between the simulation energy consumption and the size distribution feature vector is obtained by using multivariate nonlinear regression fitting;

[0008] Step 3: Simulation energy consumption correction and actual energy consumption mapping; a correction factor matrix is established, an energy consumption correction model is obtained through on-site data calibration, and the correction factor is optimized by the least square method to establish a mapping relationship between the simulation energy consumption and the actual energy consumption;

[0009] Step 4: Target size distribution determination and feature curve construction; the minimum energy consumption is selected in the simulation working condition and is corrected to determine the corresponding target size distribution, and the discrete relationship between the size and the percentage of undersize of the target size distribution is fitted into a target size curve by using a cubic spline interpolation method, so that the target size distribution curve meets the smoothness and the accuracy requirements of the feature point fitting; the accuracy requirements are that the fitting absolute error of the percentage of undersize of the key particle size 、 is ≤0.5%, the fitting absolute error of the non-key particle size is ≤1.0%, the curvature change rate difference at the connection point of adjacent segments is ≤0.1 , and the total curve curvature variance is ≤0.001; Step 5:

[0010] Size distribution curve shape difference quantification; the measured size distribution curve obtained by the on-site screening method and the image processing method is discretized into an equidistant sample point set with the target curve, that is, the target size distribution, the abscissa of the measured size distribution curve and the target size distribution curve is pretreated to realize the alignment of the particle size coordinates, and the Fréchet distance is calculated to quantify the shape difference;

[0011] Step 6: Construction of a four-level quantitative evaluation system; the Fréchet distance original value calculated in the step 5 is introduced into the normalized shape difference index, a comprehensive evaluation index is established combined with the energy consumption correction error, and a four-level evaluation standard is divided.

[0012] Further, the percentage of undersize is calculated by the formula , wherein m i represents the mass of the undersize material of the i th screen, the actual crushing energy consumption is calculated by the formula , wherein P m represents the motor power, Q represents the processing capacity.

[0013] Further, the key parameters include: rotor speed, crushing cavity gap and material particle density; the simulation energy consumption is calculated by formula ; wherein, represents the momentary crushing force vector, represents the particle motion velocity vector, represents the simulation time step.

[0014] Further, the lump size distribution feature vector is: ; wherein, represents the particle size corresponding to the 50% cumulative percentage under the screen; represents the particle size corresponding to the 80% cumulative percentage under the screen, represents the coefficient of variation; the coefficient of variation .

[0015] Further, the energy consumption correction model is: ; wherein, represents the corrected crushing energy consumption; represents the discrete element simulation energy consumption; k 1, k 2, k 3 all represent energy consumption correction factors; represents the median particle size of the simulated stockpile; represents the median particle size of the actual stockpile; represents the coefficient of variation of the actual stockpile lump size distribution; represents the coefficient of variation of the simulated stockpile lump size distribution; the correction factor matrix , represents the correction weight corresponding to the median particle size, represents the correction weight corresponding to the coefficient of variation, represents a constant term, which is optimized by least squares method to satisfy ; wherein, represents the actual crushing energy consumption, represents the corrected crushing energy consumption.

[0016] Further, the target lump size curve is fitted by a cubic spline interpolation method, satisfying and ; wherein, , represents the screen size range, the functional expression of the target lump size distribution curve, represents the target percentage under the screen at the th interpolation node.

[0017] Further, the Fréchet distance calculation step comprises:

[0018] Discretize the measured curve and the target curve into equidistant sample point sets , ; represent the equidistant sample points on the uniform particle size coordinate axis as , Align the particle size coordinate according to 100 mm equidistant sampling;

[0019] The Fréchet distance of the two size distribution curves is defined as , which is solved by recursion , with initial conditions ; wherein F represents the Fréchet distance definition, M represents the number of points after discretization of the measured curve, M ∗ represents the number of points after discretization of the target curve, represents the element in the dynamic programming matrix in the th row and the th column, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve, represents the element in the dynamic programming matrix in the th row and the th column, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve, represents the element in the dynamic programming matrix in the th row and the th column, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve, represents the element in the dynamic programming matrix in the th row and the th column, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve.

[0020] Further, the comprehensive evaluation index is , wherein , ; wherein represents the comprehensive evaluation index, represents the normalized morphological difference index, F max represents the theoretical maximum Fréchet distance, represents the energy consumption correction error, represents the weight adjustment factor.

[0021] Further, the four-level evaluation standard is: S≤0.3 is low energy consumption; 0.3<S≤0.5 is ordinary energy consumption; 0.5<S≤0.7 is high energy consumption; and S>0.7 is extremely high energy consumption; wherein, represents a comprehensive evaluation index.

[0022] Compared with the prior art, the present application has the following advantages:

[0023] 1. According to the on-site blasting lump size distribution, the present application generates a lump size sample, discrete element simulation and multi-element nonlinear regression modeling, and combines the correction factor matrix calibrated by the on-site data to control the deviation of the simulation energy consumption and the actual energy consumption within 5%, thereby realizing high-precision quantification of the crushing energy consumption.

[0024] 2. The present application defines the lump size distribution feature vector [ , , ] and combines the cubic spline interpolation method to construct the target lump size curve, thereby realizing systematic characterization and curve fitting of the lump size distribution uniformity, characteristic particle size and other parameters.

[0025] 3. The present application calculates the difference of the lump size distribution curve by using the Frechet distance, establishes a four-level quantitative evaluation system in combination with the energy consumption correction error, can directly reflect the deviation degree of the blasting lump size distribution and the target state, and provides clear classification suggestions for blasting process optimization.

[0026] 4. The present application can propose suggestions such as maintaining parameters, fine-tuning the hole pattern, optimizing the charge or redesigning the scheme by using the comprehensive evaluation index and the four-level evaluation standard, thereby improving the intelligentization and energy saving level of the mine blasting process. DETAILED DESCRIPTION

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0028] Figure 1 is a schematic diagram of the overall process of the present application.

[0029] Figure 2 is a lump size distribution curve diagram of the present application. DETAILED DESCRIPTION

[0030] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of embodiments of the present application, rather than all embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work should fall within the protection scope of the present application.

[0031] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product or device.

[0032] As Figures 1-2 shown, the present application provides a method for quantitatively evaluating the energy consumption of crushing based on the size distribution of a blast pile, comprising the following steps:

[0033] Step 1: Obtain the size distribution data of the blast pile and collect the energy consumption; obtain the size distribution data of the blast pile, simultaneously collect the operating parameters of the crusher, and calculate the actual crushing energy consumption of the crusher; the size distribution data of the blast pile includes the size of the size and the corresponding undersize percentage.

[0034] In the present application, the undersize percentage is calculated by the formula , wherein m i represents the mass of undersize material of the i th stage; the actual crushing energy consumption is calculated by the formula , wherein, P m represents the motor power, Q represents the processing capacity.

[0035] Step 2: Discrete element simulation and energy-consumption-size-distribution relationship modeling; a three-dimensional model of the crusher is constructed in EDEM software, key parameters are set according to simulation of real-site crusher parameters, different size-distribution samples are generated according to real-site stockpile distribution, discrete element simulation is used to obtain simulation energy consumption under simulation experiment conditions set according to different stockpile size-distribution samples, a size-distribution feature vector is defined, and a relationship between simulation energy consumption and the size-distribution feature vector is obtained by using multivariate nonlinear regression fitting.

[0036] As preferred, the key parameters include: rotor speed, crushing cavity clearance, and material particle density; the simulation energy consumption is calculated by the formula ; wherein, represents momentary crushing force vector, represents particle motion velocity vector, represents simulation time step.

[0037] Step 3: Simulation energy consumption correction and actual energy consumption mapping; a correction factor matrix is established, an energy consumption correction model is obtained by field data calibration, and correction factors are optimized by least square method to establish a mapping relationship between simulation energy consumption and actual energy consumption.

[0038] As a preferred embodiment, in the present application, the size-distribution feature vector is: ; wherein, represents particle size corresponding to 50% cumulative percentage under sieve; represents particle size corresponding to 80% cumulative percentage under sieve, represents coefficient of variation; the coefficient of variation .

[0039] Step 4: Target size-distribution determination and feature curve construction; the minimum energy consumption is selected in the simulation condition and corrected to determine the corresponding target size-distribution, a cubic spline interpolation method is used to fit a target size-distribution curve for discrete data points of the target size-distribution, i.e., discrete relationship between size and percentage under sieve, the target size-distribution curve meets the requirements of curve smoothness and feature point fitting accuracy; the accuracy requirement is that the fitting absolute error of the percentage under sieve of key particle size , is ≤0.5%, the fitting absolute error of non-key particle size is ≤1.0%, the curvature change rate difference at adjacent segment connection points is ≤0.1 , and the total curve curvature variance is ≤0.001.

[0040] As preferred, the energy consumption correction model is: ; wherein, represents corrected crushing energy consumption, represents discrete element simulation energy consumption,k 1, k 2, k 3 represent the energy consumption correction factor, represents the median particle size of the simulated stockpile; represents the median particle size of the actual stockpile; represents the coefficient of variation of the actual stockpile size distribution; represents the coefficient of variation of the simulated stockpile size distribution; the correction factor matrix , represents the correction weight corresponding to the median particle size, represents the correction weight corresponding to the coefficient of variation, represents the constant term, which is optimized by the least squares method to satisfy ; wherein, represents the actual crushing energy consumption, represents the corrected crushing energy consumption.

[0041] Step 5: Quantify the difference in the shape of the size distribution curve; discretize the measured size distribution curve obtained by field screening method and image processing method and the target curve, i.e. the target size distribution, into an equidistant sampling point set, align the particle size coordinates of the measured size distribution curve and the target size distribution curve, and calculate the Fréchet distance to quantify the difference in shape.

[0042] Preferably, the target size curve is fitted by a cubic spline interpolation method, satisfying and ; wherein, , represents the size range of the screen hole, the functional expression of the target size distribution curve, represents the target undersize percentage at the th interpolation node.

[0043] As a preferred embodiment, in this application, the Fréchet distance calculation step includes:

[0044] Discretize the measured curve and the target curve into an equidistant sampling point set , ; represent the equidistant sampling points on the uniform particle size coordinate axis as , Align the particle size coordinates according to 100mm equidistant sampling;

[0045] The Fréchet distance of the two size distribution curves is defined as , which is solved by the recursive formula with the initial condition ; wherein, F represents the definition of the Fréchet distance, M represents the number of points after discretization of the measured curve,M ∗ Indicates the number of points after discretization of the target curve, Indicates the element in the th row and th column of the dynamic programming matrix, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve, Indicates the element in the th row and th column of the dynamic programming matrix, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve, Indicates the element in the th row and th column of the dynamic programming matrix, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve, Indicates the element in the th row and th column of the dynamic programming matrix, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve.

[0046] Step 6: Construction of a four-level quantitative evaluation system; introducing a normalized morphological difference index to the original Fréchet distance value calculated in Step 5, and establishing a comprehensive evaluation index in combination with the energy consumption correction error, and dividing four-level evaluation criteria. The comprehensive evaluation index is where, ; where, represents the comprehensive evaluation index, represents the normalized morphological difference index, F max represents the theoretical maximum Fréchet distance, represents the energy consumption correction error, represents the weight adjustment factor. The four-level evaluation criteria are: S≤0.3 for low energy consumption; 0.3<S≤0.5 for ordinary energy consumption; 0.5<S≤0.7 for high energy consumption; S>0.7 for extremely high energy consumption; where, represents the comprehensive evaluation index.

[0047] Example 1

[0048] A complete process for quantitative evaluation of the crushing energy consumption of a blast pile in an iron ore mine, including the following steps:

[0049] I. Acquisition of blast pile fragment size distribution data and collection of actual energy consumption

[0050] At an open-pit iron mine, the size distribution of the blasted ore pile was tested, determining the maximum size to be 750mm. The size range for the screening test was set at 50-750mm, covering the main size ranges encountered in actual mine production. The size distribution data was obtained through a standard screening test, with the following steps: the blasted ore pile was graded by particle size and sieved, and the mass of material from each sieve grade was weighed. Calculate the percentage of screened-out cells. Obtain the block size and corresponding screening percentage Distribution data.

[0051] Synchronous acquisition of crusher operating parameters: Continuous recording of motor power over 30 minutes The average value is 500kW (instantaneous power fluctuation ±5%), and the processing capacity is... According to the formula Calculate the actual crushing energy consumption and obtain 15 sets of data.

[0052] II. Discrete Element Simulation and Energy Consumption-Block Distribution Model Construction

[0053] Construct a 3D model of the gyratory crusher in EDEM software and set key parameters: rotor speed. =200rpm, crushing chamber gap s =50mm, material particle density To ensure the model parameters are consistent with the field equipment, 15 sets of block size distribution samples were generated based on the field-measured block size distribution data. Each sample group covers a block size range of 50-750 mm. The simulated energy consumption under various operating conditions is calculated using discrete element method (DEM) simulation. The calculation formula is: ,in, for The breaking force vector at any moment Represents the particle velocity vector, simulation time step Let it be the minimum step size that satisfies the convergence condition.

[0054] Based on 15 sets of simulation data, a multivariate nonlinear regression model was used to fit the relationship between simulated energy consumption and block size feature vector. The block size feature vector was defined. The following results were obtained by fitting the data using multivariate nonlinear regression: .

[0055] III. Simulated Energy Consumption Correction and Actual Energy Consumption Mapping

[0056] The correction factor matrix is ​​calibrated using the least squares method. The objective function is N=15 sets of field calibration samples were used. The correction factor was obtained through iterative solution. , , , the modified error converges to within 3%.

[0057] According to the modified model , the calculated energy consumption is consistent with the actual energy consumption, meeting the requirements.

[0058] Four, target size distribution determination and characteristic curve construction

[0059] In the discrete element simulation conditions, select the simulation energy E sim The minimum operating condition (corresponding E sim-min =2200kJ / t), after modification, get =2250kJ / t, its corresponding size distribution determined as the target distribution.

[0060] Using cubic spline interpolation method to fit the target size curve, minimize the second derivative square integral: , the integral interval is 50-750mm, to ensure the curve is smooth and continuous within the measured screen size, meet the fitting conditions of characteristic points. The comparison of the target curve and the measured curve after fitting is shown in Figure 2 .

[0061] Five, size distribution curve shape difference quantification

[0062] The measured size curve and the target curve are discretized into equidistant sampling point sets (sampling point number M=100), align the particle size coordinates d k (50-750mm is divided into 100mm equidistantly), get the point sets and .

[0063] The Fréchet distance is calculated by the recursive formula . The initial condition , boundary condition , , the final distance is =0.25.

[0064] The theoretical maximum Fréchet distance F max Take the Euclidean distance of the upper limit particle size (750mm) and the 100% undersize value (0mm), and the energy consumption correction error , get the normalized index =0.15.

[0065] Six, comprehensive evaluation and process recommendations Comprehensive evaluation index calculation

[0066] Combined with the energy consumption correction error and the normalization index, the comprehensive index S = 0.102 ≤ 0.3, corresponding to the "low energy consumption" level in the four-level evaluation standard, at this time the coincidence degree of the measured curve and the target curve is > 90%, the main particle size segment deviation is < 15%, and there is no problem of excessive large block content. Based on the evaluation results, it is recommended to maintain the current blasting scheme, and there is no need to adjust the hole network parameters or the charge structure. The crushing energy consumption under this scheme has reached the optimal state, and the balance of production efficiency and energy saving effect can be guaranteed by continuing to implement it.

[0067] Example 2

[0068] Medium level energy consumption evaluation of block size distribution deviation

[0069] After adjusting the blasting hole network parameters in a certain mine, the maximum block size of the blast pile is 800mm, and the block size parameters are: , , Actual energy consumption of the crusher E real = 2640kJ / t.

[0070] Discrete element simulation gives E sim = 2800kJ / t, by modifying the model E corr = 2800x(0.75x350 / 350+1.1x0.25 / 0.25+0.25), after optimization E corr = 2800x1.1 = 3080kJ / t, error = 3080-2640 / 2640≈16.67%. The Fréchet distance between the measured curve and the target curve F = 0.6, N F = 0.4, comprehensive index S = 0.6x0.4+0.4x0.1667≈0.347, evaluation level ordinary energy consumption, suggest fine-tuning explosive unit consumption.

[0071] Example 3

[0072] Excessive blast pile characteristics and energy consumption: Due to insufficient blasting charge, the maximum block size of a certain iron mine is 500mm but the large block rate exceeds the target curve by 25%, and the block size parameters are: d 50 = 400mm, d 80 = 600mm, Cv = 0.3. Actual crushing energy consumption E real ​=600×3600 / 700≈3085.7kJ / t.

[0073] Simulation evaluation and optimization suggestions: Discrete element simulation results E sim =3500 kJ / t, after correction E corr =3500×(0.7×400 / 400+1.2×0.3 / 0.3+0.2)=3500×1.1=3850kJ / t, error E err ≈24.76%. Fraser distance F =0.85, N F =0.57, comprehensive index S =0.6×0.57+0.4×0.2476≈0.461, the evaluation level is high energy consumption, and it is recommended to optimize the charge structure to reduce the content of large pieces.

[0074] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above embodiments of the present invention, the descriptions of each embodiment have their own emphasis; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. It should be understood that the disclosed technical content in the several embodiments provided in this application can be implemented in other ways.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantitatively evaluating crushing energy consumption based on the distribution of blasted pile size, characterized in that, Includes the following steps: Step 1: Acquisition of block size distribution data and energy consumption data of the blast pile; acquire block size distribution data of the blast pile, simultaneously collect the operating parameters of the crusher, and calculate the actual crushing energy consumption of the crusher; The block size distribution data of the explosive pile includes: block size and the percentage of blocks that pass the screening for the corresponding block size; Step 2: Discrete Element Simulation and Energy Consumption-Pack Size Distribution Relationship Modeling; Construct a 3D model of the crusher in EDEM software, set key parameters according to the crusher parameters in the simulated real-world environment, generate different block size distribution samples based on the on-site burst pile distribution, obtain the simulated energy consumption under the simulation experimental conditions set according to the different burst pile block size distribution samples through discrete element simulation, define the block size distribution feature vector, and use multivariate nonlinear regression fitting on the simulated energy consumption and the block size distribution feature vector to obtain the relationship between the simulated energy consumption and the block size distribution feature vector; Step 3: Mapping simulated energy consumption to actual energy consumption; establish a correction factor matrix, obtain an energy consumption correction model through field data calibration, optimize the correction factor using the least squares method, and establish a mapping relationship between simulated energy consumption and actual energy consumption; Step 4: Determining the target block size distribution and constructing the characteristic curve; In the simulated operating conditions, select the minimum energy consumption and make corrections to determine the corresponding target block size distribution. Then, use cubic spline interpolation to fit the discrete data points corresponding to the target block size distribution—that is, the discrete relationship between block size and the percentage of undersize cells—to the target block size curve. Ensure that the target block size distribution curve meets the requirements for curve smoothness and the accuracy of characteristic point fitting; the accuracy requirement is at the critical granular level. , The absolute error of the fitting for the percentage of particles sieved undersize is ≤0.5%, and the absolute error of the fitting for non-critical particle sizes is ≤1.0%; the difference in the rate of change of curvature at the connection points of adjacent segments is ≤0.

1. And the variance of the curvature of the entire curve is ≤0.001; Step 5: Quantification of morphological differences in block size distribution curves; The measured block size distribution curves and the target curve (i.e., the target block size distribution) obtained by on-site sieving and image processing methods are discretized into an equidistant set of sampling points. The abscissas of the measured block size distribution curves and the target block size distribution curves are preprocessed to align the particle size coordinates, and the Friesian distance is calculated to quantify the morphological differences. Step 6: Construction of a four-level quantitative evaluation system; The original value of the Frescher distance calculated in Step 5 is introduced with a normalized morphological difference index, and a comprehensive evaluation index is established by combining the energy consumption correction error, and the evaluation standards are divided into four levels.

2. The method for quantitative evaluation of crushing energy consumption based on the distribution of blasted pile size according to claim 1, characterized in that, The percentage of screened-out cells is determined by the formula. Calculation, where m i Indicates the first i Quality of material under the primary screen Indicates the first j The mass of material under the primary screen; the actual crushing energy consumption is expressed by the formula. Calculation, where P m Indicates motor power. Q This indicates the processing volume.

3. The method for quantitative evaluation of crushing energy consumption based on the distribution of blasted pile size according to claim 1, characterized in that, The key parameters include: rotor speed, crushing chamber clearance, and material particle density; the simulated energy consumption is calculated using the formula... Calculate; where, express The breaking force vector at any moment Represents the velocity vector of the particle. This indicates the simulation time step.

4. The method for quantitative evaluation of crushing energy consumption based on the distribution of blasted pile size according to claim 1, characterized in that, The block size distribution feature vector is: ;in, This indicates the particle size corresponding to 50% of the cumulative percentage passing through the sieve; This indicates the particle size corresponding to 80% of the cumulative undersize percentage. The coefficient of variation is represented by the coefficient of variation. .

5. The method for quantitative evaluation of crushing energy consumption based on the distribution of blasted pile size according to claim 1, characterized in that, The energy consumption correction model is as follows: ;in, This represents the energy consumption of discrete element simulation; k 1, k 2, k 3 represents the energy consumption correction factor; This represents the median particle size in the simulated burst reactor; This represents the median particle size of the actual exploded pile; The coefficient of variation represents the actual distribution of the size of the burst pile. The coefficient of variation represents the distribution of the simulated blast pile size; the correction factor matrix , This indicates the corresponding median particle size correction weight. This indicates the weighting adjusted for the coefficient of variation. Represents the constant term, which is optimized using the least squares method to satisfy... ;in, This indicates the actual energy consumption for crushing.

6. The method for quantitative evaluation of crushing energy consumption based on the distribution of blasted pile size according to claim 1, characterized in that, The target block size curve is fitted using cubic spline interpolation, satisfying... and ;in, , Indicates the range of sieve aperture sizes. The functional expression for the target block size distribution curve. Indicates the first The percentage of targets filtered out at each interpolation node.

7. The method for quantitative evaluation of crushing energy consumption based on the distribution of blasted pile size according to claim 1, characterized in that, The Fraser distance calculation steps include: The measured curve and the target curve are discretized into a set of equidistant sampling points. , Representing equidistant sampling points on a uniform particle size coordinate axis as , Align particle size coordinates by sampling at 100mm equidistant intervals; The Fréchet distance between the two block size distribution curves is defined as... Through recursive formula Solve for the initial conditions. ;in, F This represents the definition of the Fraser distance. M This represents the number of points after discretization of the measured curve. M ∗ This represents the number of points after discretization of the target curve. Represents the first element in the dynamic programming matrix. Line number The elements of the column represent the measured curves before... Point and target curve Fraser distance between points Represents the first element in the dynamic programming matrix. Line number The elements of the column represent the measured curves before... Point and target curve Fraser distance between points Represents the first element in the dynamic programming matrix. Line number The elements of the column represent the measured curves before... Point and target curve Fraser distance between points Represents the first element in the dynamic programming matrix. Line number The elements of the column represent the measured curves before... Point and target curve The Frechet distance between points.

8. The method for quantitative evaluation of crushing energy consumption based on the distribution of blasted pile size according to claim 1, characterized in that, The comprehensive evaluation index is: ,in, , ;in, This represents the comprehensive evaluation indicators. F represents the normalized morphological difference index. max This represents the theoretical maximum Frescher distance. Indicates energy consumption correction error. This represents the weighting adjustment factor.

9. The method for quantitative evaluation of crushing energy consumption based on the distribution of blasted pile size according to claim 1, characterized in that, The four-level evaluation criteria are as follows: S ≤ 0.3 indicates low energy consumption; 0.3 < S ≤ 0.5 indicates ordinary energy consumption; 0.5 < S ≤ 0.7 indicates high energy consumption; S > 0.7 indicates extremely high energy consumption; where, represents the comprehensive evaluation index.

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