Crushing energy consumption quantitative evaluation method based on muck pile lumpiness distribution

Abstract

The invention provides a quantitative evaluation method for crushing energy consumption based on muck pile lumpiness distribution, which relates to the technical field of mining engineering and comprises the following steps: acquiring lumpiness distribution data of muck piles and acquiring energy consumption; discrete element simulation and energy consumption-lumpiness distribution relation modeling are carried out; simulating energy consumption correction and actual energy consumption mapping; determining target lumpiness distribution and constructing a characteristic curve; performing morphological difference quantification on the lumpiness distribution curve; and constructing a four-stage quantitative evaluation system. According to the method, a lumpiness sample is generated according to field muck pile lumpiness distribution, discrete element simulation and multivariate nonlinear regression modeling are performed, and a correction factor matrix calibrated by field data is combined, so that the deviation between simulated 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 invention relates to the technical field of mining engineering, and in particular to a method for quantitatively evaluating crushing energy consumption based on blast pile fragmentation distribution. Background Art

[0002] In the mining process, the quantitative relationship between the blast pile size distribution and crushing energy consumption has always been a difficult issue in the industry. Traditional crushing energy consumption evaluation methods often lack a systematic analysis of the blast pile size distribution characteristics, and it is difficult to accurately establish a mapping relationship between size parameters (such as median particle size, characteristic particle size, uniformity, etc.) and energy consumption. In the existing technology, the correlation analysis between size distribution and crushing energy consumption lacks a precise quantitative model, the evaluation system is highly subjective, and it is difficult to achieve coordinated optimization of the blasting-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 to quantify the differences in the morphology of the size distribution curve, resulting in the inability to form a scientific energy consumption evaluation system and blasting parameter optimization strategy. Therefore, there is an urgent need to establish an energy consumption quantitative evaluation method that integrates numerical simulation, mathematical modeling and pattern recognition to provide a scientific basis for energy conservation and consumption reduction in mines. Summary of the Invention

[0003] In response to the technical problems mentioned in the above background technology, a method for quantitatively evaluating crushing energy consumption based on blast pile fragmentation distribution is provided. By acquiring blast pile fragmentation distribution data and combining it with discrete element simulation, the present invention constructs an energy consumption-fragmentation distribution relationship model and correction mechanism to achieve accurate prediction of crushing energy consumption. At the same time, by quantifying the differences in the morphology of fragmentation distribution curves, a comprehensive evaluation system is established, providing a scientific basis for optimizing mine blasting parameters and regulating crushing equipment, thereby reducing crushing energy consumption and improving production efficiency.

[0004] The technical means adopted in the present invention are as follows: A method for quantitatively evaluating crushing energy consumption based on explosive pile fragmentation distribution includes the following steps: Step 1: Acquiring the particle size distribution data and energy consumption of the explosive pile; acquiring the particle size distribution data of the explosive pile, synchronously collecting the operating parameters of the crusher, and calculating the actual crushing energy consumption of the crusher; the particle size distribution data of the explosive pile includes: particle size and the undersize percentage of the corresponding particle size; Step 2: Discrete element simulation and energy consumption-fragmentation distribution relationship modeling: Construct a 3D crusher model in EDEM software, set key parameters based on the simulated actual crusher parameters, generate different fragmentation distribution samples based on the on-site blast pile distribution, obtain simulated energy consumption under the simulation experimental conditions set according to the different blast pile fragmentation distribution samples through discrete element simulation, define the fragmentation distribution characteristic vector, and use multivariate nonlinear regression fitting to obtain the relationship between the simulated energy consumption and the fragmentation distribution characteristic vector; Step 3: Mapping simulated energy consumption correction with actual energy consumption; establishing a correction factor matrix, obtaining an energy consumption correction model through field data calibration, and optimizing the correction factor through the least squares method to establish a mapping relationship between simulated energy consumption and actual energy consumption; Step 4: Determine the target size distribution and construct the characteristic curve; select the minimum energy consumption value in the simulation working condition and make corrections to determine the corresponding target size distribution, and use the cubic spline interpolation method to fit the discrete data points corresponding to the target size distribution, that is, the discrete relationship between the size and the undersize percentage, into the target size curve, so that the target size distribution curve meets the requirements of curve smoothness and characteristic point fitting accuracy; the accuracy requirement is the key particle size. 、 The absolute error of the undersize percentage fitting is ≤0.5%, and the absolute error of the non-critical particle size fitting is ≤1.0%; the difference in curvature change rate at the connection point of adjacent segments is ≤0.1 , and the total curve curvature variance ≤ 0.001; Step 5: Quantifying morphological differences in particle size distribution curves: Discretizing the measured particle size distribution curve and the target curve (i.e., the target particle size distribution) obtained through on-site screening and image processing into a set of equally spaced sampling points, pre-processing the horizontal coordinates of the measured particle size distribution curve and the target particle size distribution curve to align the particle size coordinates, and calculating the Fréchet distance to quantify morphological differences. Step 6: Construct a four-level quantitative evaluation system; introduce the normalized morphological difference index into the original value of the Fréchet distance calculated in step 5, establish a comprehensive evaluation index in combination with the energy consumption correction error, and divide the evaluation standards into four levels.

[0005] Furthermore, the undersize percentage is calculated by the formula Calculate, where m i Indicates the i The actual crushing energy consumption is calculated by the formula Calculate, where P m Indicates the motor power, Q Indicates the processing volume.

[0006] Furthermore, the key parameters include: rotor speed, crushing chamber gap and material particle density; the simulated energy consumption is calculated by the formula Calculate; where, express Moment crushing force vector, represents the particle motion velocity vector, Represents the simulation time step.

[0007] Furthermore, the blockiness distribution feature vector is: ;in, Indicates the particle size corresponding to 50% cumulative percentage under the sieve; Indicates the particle size corresponding to 80% of the cumulative percentage under the sieve. represents the coefficient of variation; coefficient of variation .

[0008] Furthermore, the energy consumption correction model is: ;in, Indicates the corrected crushing energy consumption; represents the energy consumption of discrete element simulation; k 1, k 2, k 3 represents the energy consumption correction factor; represents the median particle size of the simulated explosion pile; Indicates the median particle size of the actual explosive pile; The coefficient of variation of the actual explosion pile size distribution; represents the coefficient of variation of the simulated explosion pile fragmentation distribution; the correction factor matrix , Indicates the corresponding median particle size correction weight, Indicates the corresponding coefficient of variation correction weight, Represents a constant term, which is optimized by the least squares method to satisfy ;in, Indicates the actual crushing energy consumption, Indicates the corrected crushing energy consumption.

[0009] Furthermore, the target blockness curve is fitted by cubic spline interpolation method to meet and ;in, , Indicates the sieve size range, Function expression of target blockiness distribution curve, Indicates the The target undersize percentage at each interpolation node.

[0010] Furthermore, the Fréchet distance calculation step includes: Discretize the measured curve and the target curve into a set of equally spaced sampling points , ; The equidistant sampling points on the uniform particle size coordinate axis are represented as , Align the particle size coordinates according to 100mm equidistant sampling; The Fréchet distance between two blockiness distribution curves is defined as , through recursion Solve, initial conditions ;in, FIndicates the definition of the Fréchet distance, M Indicates the number of points after discretization of the measured curve, M ∗ Indicates the number of points after discretization of the target curve, Indicates the row and the column in 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 row and the column in 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 row and the column in 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 row and the column in the dynamic programming matrix, representing the Fréchet distance between the first points of the measured curve and the first points of the target curve.

[0011] Furthermore, 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.

[0012] Furthermore, the four-level evaluation criteria are: 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; S > 0.7 is extremely high energy consumption; where represents the comprehensive evaluation index.

[0013] Compared with the prior art, the present invention has the following advantages: 1. The present invention generates a block size sample, discrete element simulation and multi-variable non-linear regression modeling according to the on-site fragmentation size distribution, and combines the correction factor matrix calibrated with on-site data, so that the deviation between the simulated energy consumption and the actual energy consumption is controlled within 5%, achieving high-precision quantification of the crushing energy consumption.

[0014] 2. The present invention defines the blockiness distribution feature vector [ , , ], combined with the cubic spline interpolation method to construct the target fragmentation curve, and achieved the systematic characterization and curve fitting of parameters such as fragmentation distribution uniformity and characteristic particle size.

[0015] 3. The present invention uses Fréchet distance to calculate the difference in fragmentation distribution curves and establishes a four-level quantitative evaluation system in combination with energy consumption correction error. This system can intuitively reflect the degree of deviation between the fragmentation distribution of the blasting pile and the target state, and provide clear grading suggestions for blasting process optimization.

[0016] 4. Through comprehensive evaluation indicators and four-level evaluation standards, the present invention can put forward targeted suggestions such as maintaining parameters, fine-tuning the hole network, optimizing the charge or redesigning the plan, thereby improving the intelligence and energy-saving level of mine blasting technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0019] Figure 2 This is the blockiness distribution curve of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] like Figure 1-2 As shown, the present invention provides a method for quantitatively evaluating crushing energy consumption based on the fragmentation distribution of explosive piles, comprising the following steps: Step 1: Acquisition of the particle size distribution data and energy consumption of the explosive pile; acquisition of the particle size distribution data of the explosive pile, simultaneous acquisition of the operating parameters of the crusher, and calculation of the actual crushing energy consumption of the crusher; the particle size distribution data of the explosive pile includes: particle size and the undersize percentage of the corresponding particle size.

[0023] In this application, the undersize percentage is calculated by the formula Calculate, where m i Indicates the i The actual crushing energy consumption is calculated by the formula Calculate, where P m Indicates the motor power, Q Indicates the processing volume.

[0024] Step 2: Discrete element simulation and modeling of the relationship between energy consumption and fragmentation distribution; construct a three-dimensional model of the crusher in EDEM software, set key parameters according to the simulated real-life crusher parameters, generate different fragmentation distribution samples according to the on-site blast pile distribution, obtain simulated energy consumption under simulation experimental conditions set according to different blast pile fragmentation distribution samples through discrete element simulation, define the fragmentation distribution characteristic vector, and use multivariate nonlinear regression fitting to fit the simulated energy consumption and fragmentation distribution characteristic vector to obtain the relationship between the simulated energy consumption and fragmentation distribution characteristic vector.

[0025] As an example, the key parameters include: rotor speed, crushing chamber gap and material particle density; the simulated energy consumption is calculated by the formula Calculate; where, express Moment crushing force vector, represents the particle motion velocity vector, Represents the simulation time step.

[0026] Step 3: Mapping of simulated energy consumption correction and actual energy consumption; establishing a correction factor matrix, obtaining an energy consumption correction model through field data calibration, and optimizing the correction factor through the least squares method to establish a mapping relationship between simulated energy consumption and actual energy consumption.

[0027] As a preferred embodiment, in this application, the blockiness distribution feature vector is: ;in, Indicates the particle size corresponding to 50% cumulative percentage under the sieve; Indicates the particle size corresponding to 80% of the cumulative percentage under the sieve. represents the coefficient of variation; coefficient of variation .

[0028] Step 4: Determine the target size distribution and construct the characteristic curve; select the minimum energy consumption value in the simulation working condition and make corrections to determine the corresponding target size distribution. Use the cubic spline interpolation method to fit the target size curve to the discrete data points corresponding to the target size distribution, that is, the discrete relationship between the size and the percentage under the sieve, so that the target size distribution curve meets the requirements of curve smoothness and characteristic point fitting accuracy; the accuracy requirement is the key particle size. 、 The absolute error of the undersize percentage fitting is ≤0.5%, and the absolute error of the non-critical particle size fitting is ≤1.0%; the difference in curvature change rate at the connection point of adjacent segments is ≤0.1 , and the variance of the total curve curvature is ≤0.001.

[0029] Preferably, the energy consumption correction model is: ;in, Indicates the corrected crushing energy consumption, represents the discrete element simulation energy consumption, k 1, k 2, k 3 represents the energy consumption correction factor, represents the median particle size of the simulated explosion pile; Indicates the median particle size of the actual explosive pile; The coefficient of variation of the actual explosion pile size distribution; represents the coefficient of variation of the simulated explosion pile fragmentation distribution; the correction factor matrix , Indicates the corresponding median particle size correction weight, Indicates the corresponding coefficient of variation correction weight, Represents a constant term, which is optimized by the least squares method to satisfy ;in, Indicates the actual crushing energy consumption, Indicates the corrected crushing energy consumption.

[0030] Step 5: Quantify the morphological differences of the blockiness distribution curves; discretize the measured blockiness distribution curves and the target curve (i.e., the target blockiness distribution) obtained by on-site screening and image processing into a set of equally spaced sampling points, pre-process the horizontal coordinates of the measured blockiness distribution curves and the target blockiness distribution curves to align the particle size coordinates, and calculate the Fréchet distance to quantify the morphological differences.

[0031] Preferably, the target blockness curve is fitted by cubic spline interpolation method, satisfying and ;in, , Indicates the sieve size range, Function expression of target blockiness distribution curve, Indicates the The target undersize percentage at each interpolation node.

[0032] As a preferred embodiment, in this application, the Fréchet distance calculation step includes: Discretize the measured curve and the target curve into a set of equally spaced sampling points , ; The equidistant sampling points on the uniform particle size coordinate axis are represented as , Align the particle size coordinates according to 100mm equidistant sampling; The Fréchet distance between two blockiness distribution curves is defined as , through recursion Solve, initial conditions ;in, F represents the definition of Fréchet distance, M represents the number of points after the measured curve is discretized, M ∗ Indicates the number of points after discretization of the target curve, Indicates the first Rank The elements of the column represent the measured curve Point in front of target curve The Fréchet distance between points, Indicates the first Rank The elements of the column represent the measured curve Point in front of target curve The Fréchet distance between points, Indicates the first Rank The elements of the column represent the measured curve Point in front of target curve The Fréchet distance between points, Represents 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.

[0033] Step 6: Construction of a four-level quantitative evaluation system; introducing a normalized shape difference index to the original value of the Fréchet distance calculated in Step 5, establishing a comprehensive evaluation index in combination with the energy consumption correction error, and dividing the four-level evaluation criteria. The comprehensive evaluation index is , where , ; where represents the comprehensive evaluation index, represents the normalized shape 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.

[0034] Example 1 The complete process of quantitative evaluation of the crushing energy consumption of a certain iron ore blast pile includes the following steps: I. Acquisition of blast pile size distribution data and collection of actual energy consumption [[ID=3)7]] At the site of an open-pit iron mine, the size distribution of the blast pile after blasting is detected. It is determined that the maximum size of the blast pile is 750 mm, and the size range of the screening test is set to 50 - 750 mm, covering the main size intervals in the actual production of the mine. The size distribution data is obtained through a standard screening test. The specific steps are as follows: The blast pile ore is screened by particle size, and the mass of the material under each sieve level is weighed , the percentage of undersize is calculated , and the size and the corresponding percentage of undersize distribution data are obtained.

[0035] The operating parameters of the crusher are collected synchronously: The motor power is continuously recorded within 30 minutes, with an average value of 500 kW (instantaneous power fluctuation ±5%), and the throughput <000028)9>. According to the formula , the actual crushing energy consumption is calculated, and 15 groups of data are obtained.

[0036] II. Discrete element simulation and construction of energy consumption - size distribution model A three-dimensional model of a gyratory crusher is constructed in EDEM software, and the key parameters are set: the rotor speed =200rpm, crushing chamber clearance s =50mm, material particle density , to ensure that the model parameters are consistent with the on-site equipment. Based on the on-site measured fragmentation distribution data, 15 groups of fragmentation distribution samples are generated. Each set of samples covers a particle size range of 50-750mm. The simulated energy consumption under each working condition is calculated by discrete element simulation. , the calculation formula is: ,in, for Moment crushing force vector, Represents the particle motion velocity vector and simulation time step Set to the minimum step size that satisfies the convergence condition.

[0037] Based on 15 sets of simulation data, the relationship between energy consumption and blockiness feature vector is fitted by multivariate nonlinear regression. Define blockiness feature vector , and the multivariate nonlinear regression fitting is used to obtain: .

[0038] 3. Simulated Energy Consumption Correction and Actual Energy Consumption Mapping The correction factor matrix is ​​calibrated using the least squares method , the objective function is , where N=15 sets of field calibration samples. Through iterative solution, the correction factor is obtained , , , so that the correction error converges to within 3%.

[0039] According to the revised model , substitute the simulated value and the measured value into the calculation, the energy consumption after correction is consistent with the actual energy consumption, satisfying Require.

[0040] 4. Target Blockness Distribution Determination and Characteristic Curve Construction In the discrete element simulation condition, select the simulated energy consumption E sim Minimum working condition (corresponding to E sim-min =2200kJ / t), after correction we get =2250kJ / t, its corresponding particle size distribution Determine the target distribution.

[0041] The target blockiness curve is fitted using cubic spline interpolation to minimize the square integral of the second-order derivative: , the integration interval is 50-750mm, ensuring that the curve is smooth and continuous within the measured screening size and meets the characteristic point fitting conditions The comparison between the fitted target curve and the measured curve is shown in Figure 2 .

[0042] 5. Quantification of the differences in the morphology of the fragmentation distribution curve The measured blockiness curve With target curve Discretize into a set of equally spaced sampling points (number of sampling points M = 100) and align the particle size coordinates d k (50-750mm is divided into 100mm equal intervals) and the point set is obtained and .

[0043] Calculate the Fréchet distance by recursive formula . Initial conditions , boundary conditions , , the final distance is =0.25.

[0044] Calculate the theoretical maximum Fréchet distance F max Take the Euclidean distance between the upper limit particle size (750mm) and the 100% undersize value (0mm), and its energy consumption correction error , the normalized index is obtained by the formula =0.15.

[0045] 6. Comprehensive evaluation and process recommendations Calculation of comprehensive evaluation indicators Combining the energy consumption correction error and the normalized index, the comprehensive index S = 0.102 ≤ 0.3, corresponding to the "low energy consumption" level in the four-level evaluation standard. At this point, the measured curve overlaps with the target curve by >90%, the deviation in the main particle size range is <15%, and there is no excessive large-piece content. Based on these evaluation results, it is recommended to maintain the current blasting plan without adjusting the hole grid parameters or charge structure. This plan has already achieved optimal crushing energy consumption, and continued implementation will ensure a balance between production efficiency and energy savings.

[0046] Example 2 Mid-level energy consumption evaluation based on particle size distribution deviation After a mine adjusted the blasting hole network parameters, the maximum fragment size of the blast pile was 800mm. The fragment size parameters were: , , Crusher actual energy consumption E real = =2640kJ / t.

[0047] Discrete element simulation E sim =2800kJ / t, through the modified modelE corr =2800×(0.75×350 / 350+1.1×0.25 / 0.25+0.25), after optimization E corr =2800×1.1=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=0.6×0.4+0.4×0.1667≈0.347, evaluation level ordinary energy consumption, it is recommended to fine-tune the unit consumption of explosives.

[0048] Example 3 Characteristics and energy consumption of over-standard blasting piles: Due to insufficient blasting charge, the maximum fragment size of a certain iron mine is 500mm, but the large fragment rate exceeds the target curve by 25%. The fragment size parameters are: d 50 =400mm, d 80 =600mm, Cv =0.3. Actual crushing energy consumption E real =600×3600 / 700≈3085.7kJ / t.

[0049] Simulation evaluation and optimization suggestions: Discrete element simulation results E sim =3500kJ / 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%. Fréchet 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.

[0050] The serial 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 description of each embodiment has its own focus. For parts not described in detail in a particular embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented by other means.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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 quantitative evaluation method for crushing energy consumption based on the fragmentation distribution of explosive piles, characterized by: The following steps are involved: Step 1: Obtaining the fragment size distribution data and energy consumption of the explosive pile; Obtain the fragment size distribution data of the explosive pile, synchronously collect the operating parameters of the crusher, and calculate the actual crushing energy consumption of the crusher; The particle size distribution data of the blast pile includes: particle size and the undersize percentage of the corresponding particle size; Step 2: Discrete element simulation and energy consumption-fragmentation distribution relationship modeling: Construct a 3D crusher model in EDEM software, set key parameters based on the simulated actual crusher parameters, generate different fragmentation distribution samples based on the on-site blast pile distribution, obtain simulated energy consumption under the simulation experimental conditions set according to the different blast pile fragmentation distribution samples through discrete element simulation, define the fragmentation distribution characteristic vector, and use multivariate nonlinear regression fitting to obtain the relationship between the simulated energy consumption and the fragmentation distribution characteristic vector; Step 3: Mapping simulated energy consumption correction with actual energy consumption; establishing a correction factor matrix, obtaining an energy consumption correction model through field data calibration, and optimizing the correction factor through the least squares method to establish a mapping relationship between simulated energy consumption and actual energy consumption; Step 4: Determine the target size distribution and construct the characteristic curve; select the minimum energy consumption value in the simulation working condition and make corrections to determine the corresponding target size distribution, and use the cubic spline interpolation method to fit the discrete data points corresponding to the target size distribution, that is, the discrete relationship between the size and the undersize percentage, into the target size curve, so that the target size distribution curve meets the requirements of curve smoothness and characteristic point fitting accuracy; the accuracy requirement is the key particle size. 、 The absolute error of the undersize percentage fitting is ≤0.5%, and the absolute error of the non-critical particle size fitting is ≤1.0%; the difference in curvature change rate at the connection point of adjacent segments is ≤0.1 , and the variance of the total curve curvature is ≤ 0.001; Step 5: Quantify the morphological differences of the particle size distribution curves. The measured particle size distribution curves and the target curve (i.e., the target particle size distribution) obtained by on-site screening and image processing are discretized into a set of equally spaced sampling points. The horizontal coordinates of the measured particle size distribution curves and the target particle size distribution curves are preprocessed to align the particle size coordinates. The Fréchet distance is then calculated to quantify the morphological differences. Step 6: Constructing a four-level quantitative evaluation system; introducing the normalized morphological difference index into the original value of the Fréchet distance calculated in step 5, combining the energy consumption correction error to establish a comprehensive evaluation index, and dividing the evaluation standards into four levels.

2. The method for quantitative evaluation of crushing energy consumption based on explosive pile size distribution according to claim 1 is characterized in that: The undersize percentage is given by the formula Calculate, where m i Indicates the i The quality of the material under the grade screen, Indicates the j The actual crushing energy consumption is calculated by the formula Calculate, where P m Indicates the motor power, Q Indicates the processing volume.

3. The method for quantitatively evaluating crushing energy consumption based on explosive pile size distribution according to claim 1 is characterized in that: The key parameters include: rotor speed, crushing chamber gap and material particle density; the simulated energy consumption is calculated by the formula Calculate; where, express Moment crushing force vector, represents the particle motion velocity vector, Represents the simulation time step.

4. The method for quantitative evaluation of crushing energy consumption based on explosive pile size distribution according to claim 1 is characterized in that: The blockiness distribution feature vector is: ;in, Indicates the particle size corresponding to 50% cumulative percentage under the sieve; Indicates the particle size corresponding to 80% of the cumulative percentage under the sieve. represents the coefficient of variation; coefficient of variation .

5. The method for quantitative evaluation of crushing energy consumption based on explosive pile size distribution according to claim 1 is characterized in that: The energy consumption correction model is: ;in, represents the energy consumption of discrete element simulation; k 1, k 2, k 3 represents the energy consumption correction factor; represents the median particle size of the simulated explosion pile; Indicates the median particle size of the actual explosive pile; The coefficient of variation of the actual explosion pile size distribution; represents the coefficient of variation of the simulated explosion pile fragmentation distribution; the correction factor matrix , Indicates the corresponding median particle size correction weight, Indicates the corresponding coefficient of variation correction weight, Represents a constant term, which is optimized by the least squares method to satisfy ;in, Indicates the actual crushing energy consumption.

6. The method for quantitative evaluation of crushing energy consumption based on explosive pile size distribution according to claim 1 is characterized in that: The target blockness curve is fitted by cubic spline interpolation method, satisfying and ;in, , Indicates the sieve size range, Function expression of target blockiness distribution curve, Indicates the The target undersize percentage at each interpolation node.

7. The method for quantitatively evaluating crushing energy consumption based on explosive pile size distribution according to claim 1 is characterized in that: The Fréchet distance calculation step comprises: Discretize the measured curve and the target curve into a set of equally spaced sampling points , ; The equidistant sampling points on the uniform particle size coordinate axis are represented as , Align the particle size coordinates according to 100mm equidistant sampling; The Fréchet distance between two blockiness distribution curves is defined as , through recursion Solve, initial conditions ;in, F represents the definition of Fréchet distance, M represents the number of points after the measured curve is discretized, M ∗ Indicates the number of points after discretization of the target curve, Indicates the first Rank The elements of the column represent the measured curve Point in front of target curve The Fréchet distance between points, Indicates the first Rank The elements of the column represent the measured curve Point in front of target curve The Fréchet distance between points, Indicates the first Rank The elements of the column represent the measured curve Point in front of target curve The Fréchet distance between points, Indicates the first Rank The elements of the column represent the measured curve Point in front of target curve Fréchet distance between points.

8. The method for quantitatively evaluating crushing energy consumption based on explosive pile size distribution according to claim 1 is characterized in that: The comprehensive evaluation index is ,in, , ;in, represents the comprehensive evaluation index, represents the normalized morphological difference index, F max represents the theoretical maximum Fréchet distance, Indicates the energy consumption correction error, Represents the weight adjustment factor.

9. The method for quantitative evaluation of crushing energy consumption based on explosive pile size distribution according to claim 1 is 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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