Method and device for predicting collapse form of loose granules

Abstract

The invention relates to the technical field of slump prediction, in particular to a method and device for predicting the slump form of loose granules, and the method comprises the steps: obtaining an initial loose granule distribution curve; discretizing the initial loose particle distribution curve to obtain a first discretization array; performing adjacent comparison on the first discretization array and performing reassignment to obtain a second discretization array; searching the norm of the first discretization array to obtain a norm index, and establishing an end condition according to the norm index; performing cycle calculation on the second discretization array according to the end condition to obtain a third discretization array; and generating an optimized loose particle distribution curve according to the third discretization array, wherein the optimized loose particle distribution curve is used for reflecting a prediction result of the collapse form of the loose particles. The method is used for overcoming the defects that in the prior art, the collapse form is difficult to accurately predict and the cost is high, and high precision and high efficiency of collapse form prediction are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of collapse prediction, and in particular to a method and a device for predicting the collapse morphology of loose particles. Background Art

[0002] With the improvement of the mining technology level of mineral resources, the progress of blasting technology has become the key to improving the production efficiency of mines. As an important means of mining, the prediction of blast pile morphology in the design process of blasting mining has become a key issue. Blasting operations often deviate from expectations due to complex ore body structures, diverse blasting parameters, and uncertain environmental conditions, affecting the efficiency and safety of subsequent operations. Therefore, accurate prediction and control of blast pile morphology is the core requirement for improving the efficiency of mining engineering.

[0003] Traditional prediction of pile explosions mostly relies on engineers' experience and test data. However, this method is difficult to deal with complex blasting physical phenomena, such as stress wave propagation, rock mass fragmentation and fragment movement. In addition, traditional test methods are costly and time-consuming, and it is difficult to meet the requirements of efficiency and accuracy in large-scale mining projects. Therefore, it is particularly important to develop a loose particle collapse prediction algorithm based on modern computing technology to replace empirical prediction.

[0004] In recent years, with the development of computer science, mathematical modeling and big data technology, explosion prediction algorithms based on numerical simulation, machine learning and artificial intelligence have gradually emerged. Numerical simulation techniques such as finite element method, discrete element method and fluid dynamics method can accurately simulate the explosion process by establishing complex physical models. However, numerical simulation has a large amount of calculation and takes a long time, making it difficult to quickly apply in actual engineering. To solve this problem, machine learning and artificial intelligence technologies have also been introduced into the field of explosion prediction.

[0005] At present, the collapse prediction algorithm is moving towards the development direction of integrating multiple technologies. By integrating numerical simulation and machine learning technology, combined with various information such as ore body structure, blasting parameters and environmental factors, a new loose particle prediction algorithm is urgently needed to provide more accurate predictions under complex conditions. This technology will not only improve the efficiency and safety of mine blasting operations, but also provide strong support for the intelligent and digital transformation of mining engineering. In the future, the loose particle collapse prediction algorithm is expected to become an important tool in mining and other related fields, promoting the technological progress of mining engineering. Summary of the invention

[0006] The present invention provides a method and device for predicting the collapse morphology of loose particles, which are used to solve the defects of the prior art that the collapse morphology is difficult to accurately predict and the cost is high, and achieve high accuracy and efficiency in the collapse morphology prediction.

[0007] The present invention provides a method for predicting the collapse morphology of loose particles, comprising:

[0008] Obtaining initial loose particle distribution curve;

[0009] Discretizing the initial loose particle distribution curve to obtain a first discretization array;

[0010] Compare adjacent arrays of the first discretized arrays and reassign values ​​to obtain a second discretized array;

[0011] Finding the norm of the first discretized array to obtain a norm index, and establishing an end condition according to the norm index;

[0012] The second discretization array is calculated cyclically according to the end condition to obtain a third discretization array;

[0013] An optimized loose particle distribution curve is generated according to the third discretization array, and the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

[0014] According to a method for predicting collapse morphology of loose particles provided by the present invention, after obtaining the initial loose particle distribution curve, the method further includes:

[0015] Initial conditions are established as requirements for discretization of the initial loose particle distribution curve, wherein the initial conditions include: uniformity and relative microscopicity.

[0016] According to a method for predicting collapse morphology of loose particles provided by the present invention, the initial loose particle distribution curve is discretized to obtain a first discretization array, including:

[0017] The initial loose particle distribution curve is divided into a number of equally spaced sampling points x i =a+i·s(i=0,1,2,…,n-1), where the step length a and b represent the values ​​of the left and right endpoints of the interval after the curve is digitized;

[0018] Calculate the function value y according to the sampling point i =f(x i )(i=0,1,2,…,n-1), get the first discretized array [y i ](i=0,1,…,n-1).

[0019] According to a method for predicting collapse morphology of loose particles provided by the present invention, the adjacent comparison and re-assignment of the first discretized array to obtain the second discretized array includes:

[0020] According to the first discretization array [y i ](i=0,1,…,n-1) calculate the forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2);

[0021] Forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2) information comparison, according to the comparison result and the difference array characteristics, from the first discretization array subset [y i ](i=1,…,n-2), adjust the first discretization array [y i ](i=0,1,…,n-1) corresponding element values, and obtain the second discretized array.

[0022] According to a method for predicting the collapse morphology of loose particles provided by the present invention, the forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2) information comparison, according to the comparison result and the difference array characteristics, from the first discretized array subset [y i ](i=1,…,n-2), adjust the first discretization array [y i ](i=0,1,…,n-1) corresponding to the value of the element, and obtain the second discretization array, including:

[0023] Get the backward difference array [y Hi ] and the pre-difference array The element y in Hi and Qi+1 , y Hi and Qi+1 Divide by the step size and take the absolute value to get |y Hi / s| and |y Qi+1 / s|, judge |y Hi / s| and |y Qi+1 Does the absolute value of / s| exceed the absolute value of the tangent of the angle of repose? If it does not exceed the limit, continue to compare backwards;

[0024] |y Hi / s| and |y Qi+1 The absolute value of / s| exceeds the absolute value of the tangent of the angle of repose. , then |y Hi / s| and |y Qi+1 / s| for comparison, if |y Hi / s|>|y Qi+1 / s|, and y Hi>0, let Where (δ≥1) is the iteration factor, then for [y i ](i=1,…,n-2) array elements to operate: Control the convergence speed. If |y Hi / s|>|y Qi+1 / s|, and y Hi <0, let but: If |Y Hi / s|<|y Qi+1 / s|, and y Qi+1 <0, let but: If |y Hi / s|<|y Qi+1 / s|, and y Qi+1 >0, let but: In the first discretization array [y i ](i=1,…,n-2) After traversing and completing this operation, the second discretized array [Δ k ](k=1,2,…).

[0025] According to a method for predicting the collapse morphology of loose particles provided by the present invention, the norm indicators include: a single-cycle height difference extreme value and a single-cycle collapse sum, and the end condition includes: performing conditional verification based on the single-cycle collapse sum and accelerating the iterative calculation process according to the cycle height difference extreme value.

[0026] According to a method for predicting collapse morphology of loose particles provided by the present invention, after generating an optimized loose particle distribution curve according to the third discretization array, it also includes: verifying the optimized loose particle distribution curve with actual measurement data.

[0027] The present invention also provides a loose granular body collapse morphology prediction device, comprising:

[0028] A curve acquisition module is used to obtain the initial loose particle distribution curve;

[0029] A discrete processing module, used for discretizing the initial loose particle distribution curve to obtain a first discretization array;

[0030] A comparison and assignment module, used for comparing adjacent elements of the first discretized array and reassigning values ​​to obtain a second discretized array;

[0031] A norm search module, used for searching the norm of the first discretized array to obtain a norm index, and establishing an end condition according to the norm index;

[0032] A loop calculation module, used for loop calculation of the second discretization array according to the end condition to obtain a third discretization array;

[0033] The curve generation module is used to generate an optimized loose particle distribution curve according to the third discretization array, and the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

[0034] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-mentioned methods for predicting the collapse morphology of loose particles when executing the program.

[0035] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for predicting the collapse morphology of loose particles described in any one of the above items is implemented.

[0036] The present invention provides a method and device for predicting the collapse morphology of loose particles. The method obtains an initial loose particle distribution curve and discretizes the initial loose particle distribution curve to obtain a first discretized array. The initial curve is discretized to obtain a discretized array, so that it can be used for subsequent data processing and optimization. The first discretized array is compared adjacently and reassigned to obtain a second discretized array. The array can be effectively sorted by comparing adjacently and reassigning values ​​to ensure that each element is in the correct position, which can help optimize the data structure and improve data processing efficiency. The norm of the first discretized array is searched to obtain a norm index. The end condition is established according to the norm index. The norm index is obtained by searching the array for the norm. The norm index provides a powerful mathematical tool for theoretical analysis and numerical solution of optimization problems. The end condition is established according to the norm index to provide a conditional basis for subsequent optimization of the loose particle distribution curve. According to the end condition, the second discretization array is calculated cyclically to obtain the third discretization array. The preset end condition is used as the exit condition of the judgment array. If the condition is not met, the calculation continues in a loop. If the condition is met, the calculation ends, so that the collapse data reflected by the array is as close to the actual situation as possible to ensure the accuracy of the prediction. The optimized loose particle distribution curve is generated according to the third discretization array. The optimized loose particle distribution curve is used to reflect the collapse morphology prediction results of the loose particle body. The corresponding optimized loose particle distribution curve is generated by the third discretization array after data processing. Compared with the initial loose particle distribution curve, the optimized loose particle distribution curve is more in line with the actual collapse situation. The prediction results of the collapse morphology of the loose particle body are reflected by the optimized loose particle distribution curve, so that it has high accuracy and efficiency in collapse morphology prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or 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 creative work.

[0038] Figure 1 It is a schematic flow chart of the method for predicting the collapse morphology of loose particles provided by the present invention;

[0039] Figure 2 It is a discretized schematic diagram of the loose granular body collapse morphology prediction method provided by the present invention;

[0040] Figure 3 It is a comparative logic schematic diagram of the loose granular body collapse morphology prediction method provided by the present invention;

[0041] Figure 4 It is a prediction result diagram of the loose granular body collapse morphology prediction method provided by the present invention;

[0042] Figure 5 It is a collapse verification diagram of the collapse morphology prediction method of loose granular bodies provided by the present invention;

[0043] Figure 6 It is a structural schematic diagram of a loose granular body collapse morphology prediction device provided by the present invention;

[0044] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0046] Combine the following Figure 1-Figure 5 The method for predicting collapse morphology of loose particles according to the first embodiment of the present invention is described.

[0047] like Figure 1 As shown, a method for predicting the collapse morphology of loose particles in this embodiment specifically includes the following steps (the numbering of each step in the present invention is only used to distinguish the steps, and does not limit the specific execution order of each step):

[0048] Step S1: Obtaining an initial loose particle distribution curve.

[0049] The loose particle distribution curve is used to reflect the collapse morphology of loose particles in blasting work. The loose particle distribution curve obtained by traditional methods is quite different from the actual situation and it is difficult to meet the accuracy requirements of collapse morphology prediction. Therefore, the present invention needs to obtain the initial loose particle distribution curve and further optimize it.

[0050] Step S2: discretizing the initial loose particle distribution curve to obtain a first discretization array.

[0051] The discretized array is obtained by discretizing the initial curve so that it can be used for subsequent data processing and optimization.

[0052] Step S3: Compare adjacent elements of the first discretized array and reassign values ​​to obtain a second discretized array.

[0053] By comparing adjacent elements and reassigning values, the array can be effectively sorted to ensure that each element is in the correct position, which can help optimize the data structure and improve data processing efficiency.

[0054] Step S4: searching for the norm of the first discretized array to obtain a norm index, and establishing an end condition according to the norm index.

[0055] The norm index is obtained by searching the norm of the array. The norm index provides a powerful mathematical tool for the theoretical analysis and numerical solution of the optimization problem. The end condition is established according to the norm index, which provides a conditional basis for the subsequent optimization of the loose particle distribution curve.

[0056] Step S5: looping and calculating the second discretization array according to the end condition to obtain a third discretization array.

[0057] The preset end condition is used as the exit condition of the judgment array. If the condition is not met, the calculation will continue in a loop. If the condition is met, the calculation will end, so that the collapse data reflected by the array is as close to the actual situation as possible to ensure the accuracy of the prediction.

[0058] Step S6: generating an optimized loose particle distribution curve according to the third discretization array, wherein the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

[0059] The corresponding optimized loose particle distribution curve is generated by the third discretized array after data processing. The optimized loose particle distribution curve is more consistent with the actual collapse situation than the initial loose particle distribution curve. The optimized loose particle distribution curve reflects the collapse morphology prediction result of the loose particle, so that it has high accuracy and efficiency in collapse morphology prediction.

[0060] The first embodiment of the present invention provides a method for predicting the collapse morphology of loose particles. The method obtains an initial loose particle distribution curve and discretizes the initial loose particle distribution curve to obtain a first discretized array. The discretized array obtained by discretizing the initial curve can be used for subsequent data processing and optimization. The first discretized array is compared adjacently and reassigned to obtain a second discretized array. By comparing adjacently and reassigning values, the array can be effectively sorted to ensure that each element is in the correct position, which can help optimize the data structure and improve data processing efficiency. The norm of the first discretized array is searched to obtain a norm index. The end condition is established according to the norm index. The norm index is obtained by searching the array for the norm. The norm index provides a powerful mathematical tool for theoretical analysis and numerical solution of optimization problems. The end condition is established according to the norm index to provide a conditional basis for subsequent optimization of the loose particle distribution curve. According to the end condition, the second discretization array is calculated cyclically to obtain the third discretization array. The preset end condition is used as the exit condition of the judgment array. If the condition is not met, the calculation continues in a loop. If the condition is met, the calculation ends, so that the collapse data reflected by the array is as close to the actual situation as possible to ensure the accuracy of the prediction. The optimized loose particle distribution curve is generated according to the third discretization array. The optimized loose particle distribution curve is used to reflect the collapse morphology prediction results of the loose particle body. The corresponding optimized loose particle distribution curve is generated by the third discretization array after data processing. Compared with the initial loose particle distribution curve, the optimized loose particle distribution curve is more in line with the actual collapse situation. The prediction results of the collapse morphology of the loose particle body are reflected by the optimized loose particle distribution curve, so that it has high accuracy and efficiency in collapse morphology prediction.

[0061] In this embodiment, after obtaining the initial loose particle distribution curve, the method further includes:

[0062] Initial conditions are established as requirements for discretization of the initial loose particle distribution curve, wherein the initial conditions include: uniformity and relative microscopicity.

[0063] Initial conditions are established as a requirement for discretization of the initial loose particle distribution curve. According to the principle of mechanics, the particles in the blast pile are evenly distributed, and the particles are relatively microscopic relative to the overall blast pile. Only through the limitation of the above conditions can the subsequent collapse theory be derived. The initial conditions specifically include uniformity and relative microscopicity. First, uniformity: that is, the loose particles within the entire curve description range are relatively uniform, and the differences in particle or block size can be equivalently homogenized, and no singular values ​​will be generated within the curve range; second, relative microscopicity, that is, the loose particles are relatively small in size relative to the collapse body described by the entire curve, and the size effect of the particles will not drastically change the formation law of the entire collapse curve.

[0064] First, it is determined that in rock blasting, the loose particle units in the blast pile have the characteristics of homogenization and relative microscopicity relative to the entire blast pile. Secondly, the kinetic energy obtained by the rock after blasting and the ideal ballistic equation are derived using the dynamic formula:

[0065]

[0066] In the formula: L is the throwing distance; V is the maximum throwing speed, obtained by calculation or measurement; g is the acceleration of gravity; θ is the throwing angle; H is the throwing height.

[0067] The throwing distance of the corresponding volume of rocks is obtained from this formula. The initial morphology of the blast pile, i.e., the initial loose particle distribution curve, is obtained according to the cumulative superposition of the kinetic energy given to different rocks by different parts of explosives. This curve does not represent the final shape of the blast pile. Due to the effect of gravity, there is a subsequent physical process of collapse. Next, the final shape of the collapse of the loose particles in the blast pile is predicted.

[0068] By taking the initial conditions as the requirements for discretization of the initial loose particle distribution curve, the discretized array obtained by processing is more convenient for subsequent data processing and optimization, thereby improving the accuracy of prediction.

[0069] In this embodiment, the discretization process of the initial loose particle distribution curve to obtain a first discretization array includes:

[0070] The initial loose particle distribution curve is divided into a number of equally spaced sampling points x i =a+i·s(i=0,1,2,…,n-1), where the step length a and b represent the values ​​of the left and right endpoints of the interval after the curve is digitized;

[0071] Calculate the function value y according to the sampling point i =f(x i )(i=0,1,2,…,n-1), get the first discretized array [y i ](i=0,1,…,n-1).

[0072] The initial loose particle distribution curve is discretized to obtain the first discretization array. The physical basis of this step is as follows: Figure 2 As shown, the following formula can be obtained by analyzing the mechanical principles:

[0073] ∑F=-ρg(H1-H2-μdx)dh

[0074] Where ∑F is the driving force for the collapse of the initial loose particles; ρ is the average density of the explosion pile; g is the acceleration of gravity; μ is the friction coefficient; H1 and H2 are the discrete heights of the explosion pile.

[0075] According to the above formula principle, the initial loose particles can be divided into multiple equally spaced blocks, that is, discretized into a representative set x i =a+i·s(i=0,1,2,…,n-1), where the step length Then use the segmented sampling points to calculate the corresponding function value y i =f(x i )(i=0,1,2,...,n-1), the array composed of these function values, namely the first discretized array [y i ](i=0,1,…,n-1), so that it can be used for subsequent data processing and optimization.

[0076] In this embodiment, the comparing adjacent elements of the first discretized array and reassigning values ​​to obtain the second discretized array includes:

[0077] According to the first discretization array [y i ](i=0,1,…,n-1) calculate the forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2);

[0078] Forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2) information comparison, according to the comparison result and the difference array characteristics, from the first discretized array subset [y i ](i=1,…,n-2), adjust the first discretization array [y i ](i=0,1,…,n-1) corresponding element values, and obtain the second discretized array.

[0079] After the discretization is completed, according to the first discretization array [y i ](i=0,1,…,n-1) calculate the forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2), to prepare for the subsequent process.

[0080] For two difference arrays [y Hi ]、[y Qi ] to compare information, and based on the comparison results and the characteristics of the differential array, select the subset [y i ](i=1,…,n-2), adjust the first discretization array [y i ](i=0,…,n-1) corresponding element values, and obtain the second discretized array.

[0081] By comparing adjacent elements and reassigning values, the array can be effectively sorted to ensure that each element is in the correct position, which can help optimize the data structure and improve data processing efficiency.

[0082] In this embodiment, the forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2) information comparison, according to the comparison result and the difference array characteristics, from the first discretized array subset [y i ](i=1,…,n-2), adjust the first discretization array [y i ](i=0,1,…,n-1) corresponding to the value of the element, and obtain the second discretization array, including:

[0083] Get the backward difference array [y Hi ] and the pre-difference array The element y in Hi and Qi+1 , y Hi and Qi+1 Divide by the step size and take the absolute value to get |y Hi / s| and |y Qi+1 / s|, judge |y Hi / s| and |y Qi+1 Does the absolute value of / s| exceed the absolute value of the tangent of the angle of repose? If it does not exceed the limit, continue to compare backwards;

[0084] |y Hi / s| and |y Qi+1 The absolute value of / s| exceeds the absolute value of the tangent of the repose angle , then |y Hi / s| and |y Qi+1 / s| to compare, if |y Hi / s|>|y Qi+1 / s|, and y Hi >0, let Where (δ≥1) is the iteration factor, then for [y i ](i=1,…,n-2) array elements to operate: Control the convergence speed. If |y Hi / s|>|y Qi+1 / s|, and y Hi <0, let but: If |Y Hi / s|<|y Qi+1 / s|, and y Qi+1 <0, let but: If |y Hi / s|<|y Qi+1 / s|, and y Qi+1 >0, let but: In the first discretization array [y i ](i=1,…,n-2) After traversing and completing this operation, the second discretized array [Δ k ](k=1,2,…).

[0085] The detailed process is to determine the two difference arrays [y Hi ]、[y Qi ] element y Hi With y Qi+1 , divide them by the step size and take the absolute value to get |y Hi / s| and |y Qi+1 / s|, to determine whether the two absolute values ​​exceed the absolute value of the tangent of the repose angle If the value does not exceed this value, continue to compare backwards; if there is more than , then |y Hi / s| and |y Qi+1 / s| to compare. If |y Hi / s|>|y Qi+1 / s|, and y Hi >0, let Where (δ≥1) is the iteration factor, then for [y i ](i=1,…,n-2) array elements to operate: Control the convergence speed; if |y Hi / s|>|y Qi+1 / s|, and y Hi <0, let but: If |y Hi / s|<|y Qi+1 / s|, and y Qi+1 <0, let but: If |y Hi / s|<|y Qi+1 / s|, and y Qi+1 >0, let but: In the array [y i ](i=1,…,n-2) After traversing and completing this operation, the second discretized array [Δ k ](k=1,2,…).

[0086] Compare the adjacent elements in the entire array and reassign values ​​according to the comparison results. According to the principle of mechanics, the direction of the driving force is determined by the height difference with the adjacent discrete bodies of the explosive pile, so this step can be calculated. The comparison logic is as follows: Figure 3 As shown, the characteristics of different differential array elements are compared to determine the collapse direction.

[0087] In this embodiment, the norm indicators include: a single cycle height difference extreme value and a single cycle collapse sum, and the end condition includes: performing conditional verification according to the single cycle collapse sum and accelerating the iterative calculation process according to the cycle height difference extreme value.

[0088] The steps of finding the norm indicator include: finding the extreme value of the height difference in a single cycle, and obtaining a new array by performing a union operation on the two arrays. That is, calculate the merged array each time for n loops The infinite norm of ∥y Ci || ∞ ; Find the collapse sum of a single cycle, that is, calculate the array [Δ k ](k=1,2,…) The norm of each time ‖Δ k ‖1. Using the norm index found, according to the preset physical conditions (such as the angle of repose Cohesion c) Set ending conditions.

[0089] The end conditions are divided into two conditions: one is to compare the maximum height difference and step ratio D determined by the cohesion c of the collapse imax / s and the absolute value of the tangent of the angle of repose The relative size of ∑D determines whether it collapses and whether the collapse is completed. i (i=1,2,...,n) Whether the final total collapse reaches the set threshold, optimizing the cycle ratio of these two end conditions can increase the algorithm accuracy or reduce the calculation time.

[0090] According to the end condition, the second discretization array is calculated repeatedly to obtain the third discretization array. The preset end condition is used as the exit condition of the judgment array. If the condition is not met, the calculation continues to be cyclical. If the condition is met, the calculation ends. The prediction results for the rows 1, 2, 3, 4, and the row spacing 4.0, 4.5, 5.0m are as follows: Figure 4 As shown, it can be seen that the prediction method of the present invention has high stability.

[0091] In this embodiment, after generating the optimized loose particle distribution curve according to the third discretization array, the method further includes: verifying the optimized loose particle distribution curve with actual measurement data.

[0092] The loose particle distribution curve of the actual working condition 6 rows and 6m spacing of the blast pile was compared with the actual measurement data to verify the accuracy of the prediction results. The results are as follows: Figure 5 As shown, it can be seen that the prediction results can make a relatively accurate prediction of the actual results, thus ensuring the accuracy of the prediction method of the present invention.

[0093] The following is a description of the loose granular body collapse morphology prediction device provided by the present invention. The loose granular body collapse morphology prediction device described below and the loose granular body collapse morphology prediction method described above can be referenced to each other.

[0094] like Figure 6 As shown, the second embodiment of the present invention further provides a loose granular body collapse morphology prediction device, comprising:

[0095] The curve acquisition module 210 is used to acquire the initial loose particle distribution curve.

[0096] The discrete processing module 220 is used to discretize the initial loose particle distribution curve to obtain a first discretization array.

[0097] The comparison and assignment module 230 is used to compare adjacent elements of the first discretization array and re-assign values ​​to obtain a second discretization array.

[0098] The norm search module 240 is used to search the norm of the first discretized array to obtain a norm indicator, and establish an end condition according to the norm indicator.

[0099] The loop calculation module 250 is used to loop calculate the second discretization array according to the end condition to obtain a third discretization array.

[0100] The curve generating module 260 is used to generate an optimized loose particle distribution curve according to the third discretization array, and the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

[0101] The second embodiment of the present invention provides a loose particle collapse morphology prediction device, which obtains an initial loose particle distribution curve and discretizes the initial loose particle distribution curve to obtain a first discretized array. The discretized array obtained by discretizing the initial curve can be used for subsequent data processing and optimization. The first discretized array is compared adjacently and reassigned to obtain a second discretized array. By comparing adjacently and reassigning values, the array can be effectively sorted to ensure that each element is in the correct position, which can help optimize the data structure and improve data processing efficiency. The norm of the first discretized array is searched to obtain a norm index, and an end condition is established based on the norm index. The norm index is obtained by searching the array for the norm. The norm index provides a powerful mathematical tool for theoretical analysis and numerical solution of optimization problems. The end condition is established based on the norm index to provide a conditional basis for subsequent optimization of the loose particle distribution curve. According to the end condition, the second discretization array is calculated cyclically to obtain the third discretization array. The preset end condition is used as the exit condition of the judgment array. If the condition is not met, the calculation continues in a loop. If the condition is met, the calculation ends, so that the collapse data reflected by the array is as close to the actual situation as possible to ensure the accuracy of the prediction. The optimized loose particle distribution curve is generated according to the third discretization array. The optimized loose particle distribution curve is used to reflect the collapse morphology prediction results of the loose particle body. The corresponding optimized loose particle distribution curve is generated by the third discretization array after data processing. Compared with the initial loose particle distribution curve, the optimized loose particle distribution curve is more in line with the actual collapse situation. The prediction results of the collapse morphology of the loose particle body are reflected by the optimized loose particle distribution curve, so that it has high accuracy and efficiency in collapse morphology prediction.

[0102] Optionally, the curve acquisition module 210 is specifically used for:

[0103] Obtaining initial loose particle distribution curve;

[0104] Initial conditions are established as requirements for discretization of the initial loose particle distribution curve, wherein the initial conditions include: uniformity and relative microscopicity.

[0105] Optionally, the discrete processing module 220 is specifically configured to:

[0106] The initial loose particle distribution curve is divided into a number of equally spaced sampling points x i =a+i·s(i=0,1,2,…,n-1), where the step length a and b represent the values ​​of the left and right endpoints of the interval after the curve is digitized;

[0107] Calculate the function value y according to the sampling point i =f(xi )(i=0,1,2,…,n-1), get the first discretized array [y i ](i=0,1,…,n-1).

[0108] Optionally, the comparison and assignment module 230 is specifically used for:

[0109] According to the first discretization array [y i ](i=0,1,…,n-1) calculate the forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2);

[0110] Forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2) information comparison, according to the comparison result and the difference array characteristics, from the first discretized array subset [y i ](i=1,…,n-2), adjust the first discretization array [y i ](i=0,1,…,n-1) corresponding element values, and obtain the second discretized array.

[0111] Optionally, the comparison and assignment module 230 is further specifically configured to:

[0112] According to the first discretization array [y i ](i=0,1,…,n-1) calculate the forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2);

[0113] Get the backward difference array [y Hi ] and the pre-difference array The element y in Hi and Qi+1 , y Hi and Qi+1 Divide by the step size and take the absolute value to get |y Hi / s| and |y Qi+1 / s|, judge |y Hi / s| and |y Qi+1 Does the absolute value of / s| exceed the absolute value of the tangent of the angle of repose? If it does not exceed the limit, continue to compare backwards;

[0114] |y Hi / s| and |y Qi+1 The absolute value of / s| exceeds the absolute value of the tangent of the repose angle , then |y Hi / s| and |y Qi+1 / s| for comparison, if |y Hi / s|>|y Qi+1 / s|, and y Hi >0, let Where (δ≥1) is the iteration factor, then for [y i ](i=1,…,n-2) array elements to operate: Control the convergence speed. If |y Hi / s|>|y Qi+1 / s|, and y Hi <0, let but: If |y Hi / s|<|y Qi+1 / s|, and y Qi+1 <0, let but: If |y Hi / s|<|y Qi+1 / s|, and y Qi+1 >0, let but: In the first discretization array [y i ](i=1,…,n-2) After traversing and completing this operation, the second discretized array [Δ k ](k=1,2,…).

[0115] Optionally, the norm index includes: a single cycle height difference extreme value and a single cycle collapse sum, and the end condition includes: performing conditional verification according to the single cycle collapse sum and accelerating the iterative calculation process according to the cycle height difference extreme value.

[0116] Optionally, the curve generating module 260 is specifically used for:

[0117] An optimized loose particle distribution curve is generated according to the third discretization array, and the optimized loose particle distribution curve is verified with actual measurement data. The optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

[0118] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the loose particle body collapse morphology prediction method, which includes:

[0119] Obtaining initial loose particle distribution curve;

[0120] Discretizing the initial loose particle distribution curve to obtain a first discretization array;

[0121] Compare adjacent arrays of the first discretized arrays and reassign values ​​to obtain a second discretized array;

[0122] Finding the norm of the first discretized array to obtain a norm index, and establishing an end condition according to the norm index;

[0123] The second discretization array is calculated cyclically according to the end condition to obtain a third discretization array;

[0124] An optimized loose particle distribution curve is generated according to the third discretization array, and the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

[0125] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0126] On the other hand, the present invention further provides a computer program product, the computer program product comprising a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the loose particle collapse morphology prediction method provided by the above methods, the method comprising:

[0127] Obtaining initial loose particle distribution curve;

[0128] Discretizing the initial loose particle distribution curve to obtain a first discretization array;

[0129] Compare adjacent arrays of the first discretized arrays and reassign values ​​to obtain a second discretized array;

[0130] Finding the norm of the first discretized array to obtain a norm index, and establishing an end condition according to the norm index;

[0131] The second discretization array is calculated cyclically according to the end condition to obtain a third discretization array;

[0132] An optimized loose particle distribution curve is generated according to the third discretization array, and the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

[0133] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the loose particle body collapse morphology prediction method provided by the above methods, the method comprising:

[0134] Obtaining initial loose particle distribution curve;

[0135] Discretizing the initial loose particle distribution curve to obtain a first discretization array;

[0136] Compare adjacent arrays of the first discretized arrays and reassign values ​​to obtain a second discretized array;

[0137] Finding the norm of the first discretized array to obtain a norm index, and establishing an end condition according to the norm index;

[0138] The second discretization array is calculated cyclically according to the end condition to obtain a third discretization array;

[0139] An optimized loose particle distribution curve is generated according to the third discretization array, and the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

[0140] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0141] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0142] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the collapse morphology of loose particles, characterized in that: include: Obtaining initial loose particle distribution curve; Discretizing the initial loose particle distribution curve to obtain a first discretization array; Compare adjacent arrays of the first discretized arrays and reassign values ​​to obtain a second discretized array; Finding the norm of the first discretized array to obtain a norm index, and establishing an end condition according to the norm index; The second discretization array is calculated cyclically according to the end condition to obtain a third discretization array; An optimized loose particle distribution curve is generated according to the third discretization array, and the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

2. The method for predicting collapse morphology of loose particles according to claim 1, characterized in that: After obtaining the initial loose particle distribution curve, the method further includes: Initial conditions are established as requirements for discretization of the initial loose particle distribution curve, wherein the initial conditions include: uniformity and relative microscopicity.

3. The method for predicting collapse morphology of loose particles according to claim 1, characterized in that: The discretization process of the initial loose particle distribution curve to obtain a first discretization array includes: The initial loose particle distribution curve is divided into a number of equally spaced sampling points x i =a+i·s(i=0,1,2,…,n-1), where the step length a and b represent the values ​​of the left and right endpoints of the interval after the curve is digitized; Calculate the function value y according to the sampling point i =f(x i )(i=0,1,2,…,n-1), get the first discretized array [y i ](i=0,1,…,n-1).

4. The method for predicting collapse morphology of loose particles according to claim 3, characterized in that: The step of comparing adjacent elements of the first discretized array and reassigning values ​​to obtain a second discretized array includes: According to the first discretization array [y i ](i=0,1,…,n-1) calculate the forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2); Forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2) information comparison, according to the comparison result and the difference array characteristics, from the first discretized array subset [y i ](i=1,…,n-2), adjust the first discretization array [y i ](i=0,1,…,n-1) corresponding element values, and obtain the second discretized array.

5. The method for predicting collapse morphology of loose particles according to claim 4, characterized in that: The forward difference array With the backward difference array [y Hi ](i=0,1,…,n-2) information comparison, according to the comparison result and the difference array characteristics, from the first discretized array subset [y i ](i=1,…,n-2), adjust the first discretization array [y i ](i=0,1,…,n-1) corresponding to the value of the element, and obtain the second discretization array, including: Get the backward difference array [y Hi ] and the pre-difference array The element y in Hi and Qi+1 , y Hi and Qi+1 Divide by the step size and take the absolute value to get |y Hi / s| and |y Qi+1 / s|, judge |y Hi / s| and |y Qi+1 Does the absolute value of / s| exceed the absolute value of the tangent of the angle of repose? If it does not exceed the limit, continue to compare backwards; |y Hi / s| and |y Qi+1 The absolute value of / s| exceeds the absolute value of the tangent of the repose angle , then |y Hi / s| and |y Qi+1 / s| for comparison, if |y Hi / s|>|y Qi+1 / s|, and y Hi >0, let Where (δ≥1) is the iteration factor, then for [y i ](i=1,…,n-2) array elements to operate: Control the convergence speed. If |y Hi / s|>|y Qi+1 / s|, and y Hi <0, let but: If |y Hi / s|<|y Qi+1 / s|, and y Qi+1 <0, let but: If |y Hi / s|<|y Qi+1 / s|, and y Qi+1 >0, let but: In the first discretization array [y i ](i=1,…,n-2) After traversing and completing this operation, the second discretized array [Δ k ](k=1,2,…).

6. The method for predicting collapse morphology of loose particles according to claim 1, characterized in that: The norm index includes: a single cycle height difference extreme value and a single cycle collapse sum, and the end condition includes: performing condition verification according to the single cycle collapse sum and accelerating the iterative calculation process according to the cycle height difference extreme value.

7. The method for predicting collapse morphology of loose particles according to claim 1, characterized in that: After generating the optimized loose particle distribution curve according to the third discretization array, the method further includes: verifying the optimized loose particle distribution curve with actual measurement data.

8. A device for predicting the collapse morphology of loose particles, characterized in that: include: A curve acquisition module is used to obtain the initial loose particle distribution curve; A discrete processing module, used for discretizing the initial loose particle distribution curve to obtain a first discretization array; A comparison and assignment module, used for comparing adjacent elements of the first discretized array and reassigning values ​​to obtain a second discretized array; A norm search module, used for searching the norm of the first discretized array to obtain a norm index, and establishing an end condition according to the norm index; A loop calculation module, used for loop calculation of the second discretization array according to the end condition to obtain a third discretization array; The curve generation module is used to generate an optimized loose particle distribution curve according to the third discretization array, and the optimized loose particle distribution curve is used to reflect the prediction result of the collapse morphology of the loose particles.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for predicting the collapse morphology of loose particles as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting collapse morphology of loose particles as described in any one of claims 1 to 7 is implemented.

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