CN113569487A - Method for predicting step blasting throwing effect based on BP neural network - Google Patents
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Abstract
The invention relates to a method for predicting a step blasting throwing effect based on a BP neural network, belonging to the field of blasting engineering. The method comprises the following steps: and designing a model test according to the actual working condition, carrying out tests under different groups, and recording test data. Introducing test data into a neural network model for training by using a BP neural network model and taking the step height, the coal seam thickness, the goaf upper opening width, the minimum resistance line, the hole pitch, the row pitch, the inter-hole differential time and the coal seam slope angle as input parameters and taking the throwing rate, the explosive pile loosening coefficient and the farthest throwing distance as output parameters; the trained neural network model can predict the step blasting throwing effect. The invention provides a method for predicting the step blasting throwing effect based on a BP neural network, which can accurately predict the step blasting throwing effect under a specific working condition and judge the feasibility of a blasting scheme. The method provides reference for the step blasting of the mine and improves the reliability of the throwing blasting.
Description
Technical Field
The invention relates to a method for predicting a step blasting throwing effect based on a BP neural network, belonging to the field of blasting engineering.
Background
The throwing blasting technology for open coal mine usually adopts measures of large aperture, deep hole and high step, large explosive loading amount, no coupling explosive loading and the like, and partial rocks are thrown to a goaf by utilizing energy generated by explosive explosion without moving mining and loading equipment. The part of the rock thrown to the goaf is called effective throwing amount, and the ratio of the effective throwing amount to the total rock blasting amount is called effective throwing rate, which is one of the important indexes for measuring the effect of the throwing blasting. On the other hand, the throwing blasting technology is only a part of the production process link and needs to be matched with other stripping and mining equipment for operation, the form of the blasting pile after the throwing blasting is an important factor influencing the operation efficiency of subsequent equipment, and the improvement of the effective throwing rate and the control of the form of the blasting pile of the throwing blasting are key ways for saving the production cost and improving the operation efficiency of the subsequent equipment. Obviously, the step-throw blasting parameters play an important role in the effective throwing rate and the throwing blasting pile form, and the step-throw blasting parameters need to be optimized and researched.
In the throwing blasting, the effective throwing rate is determined according to the blasting form, and the dumping workload of the system is further determined; the dragline stands on the leveled blasting pile during operation, and the blasting pile form influences the design of the parameters of the reverse pile working face and the engineering quantity of constructing and expanding a platform, so that the research on the characteristics of the blasting pile form is the most important basic work in the optimization design of the throwing blasting process, and a method for realizing the prediction of the throwing effect after the throwing blasting is urgently needed.
Disclosure of Invention
The invention aims to provide a method for predicting the step blasting throwing effect based on a BP (back propagation) neural network, wherein a neural network model passing a test can predict the step blasting throwing condition under a preset working condition by utilizing the generalization capability of the neural network model, judge the feasibility of a blasting scheme and determine whether the throwing requirement is met; the method provides reference for the step blasting of the mine and improves the reliability of the throwing blasting.
The technical scheme adopted by the invention is as follows: a method for predicting step blasting throwing effect based on BP neural network comprises the following steps:
the method comprises the following steps: designing a plurality of groups of test models according to actual working conditions, carrying out tests under different groups, and recording test data;
step two: constructing a BP neural network model: taking the step height, the coal seam thickness, the goaf upper opening width, the minimum resistance line, the hole pitch, the row pitch, the inter-hole differential time and the coal seam slope angle as input parameters, taking the throwing rate, the blasting loosening coefficient and the farthest throwing distance as output parameters, and carrying out normalization processing;
step three, determining the structural parameters of the BP neural network: preprocessing the input parameters and the output parameters after normalization processing, and then dividing the preprocessed input parameters and output parameters into a training sample set and a testing sample set; training the BP neural network model by using the data in the training sample set, adjusting the network parameters of the BP neural network model, and after the training is successful, testing the trained BP neural network model by using the data in the test data set so as to verify the correctness of the neural network model;
and step four, predicting the step blasting throwing condition under the specified working condition by using the trained BP neural network model, judging the feasibility of the blasting scheme, and determining whether the throwing requirement is met.
Specifically, in the first step, each group of test models comprises a throwing blasting step 1, a goaf 2, a reverse pile accumulation body 3 and a background grating plate 4, downward blast holes 1.2 are arranged on the throwing blasting step 1, a simulated coal seam 1.1 is located under the throwing blasting step 1, and the background grating plate 4 is vertically arranged behind the throwing blasting step 1, the goaf upper opening 2 and the reverse pile accumulation body 3.
Specifically, in the step one, the tests in different groups are as follows: and carrying out multiple groups of tests by adjusting one or more than two values of the minimum resistance line, the hole pitch, the row pitch, the micro-difference time between the main control row holes, the coal bed slope angle and the unit consumption of explosive.
Specifically, the selected BP neural network type is a single hidden layer BP neural network, the BP neural network is trained by using data in a training sample set, the data in the training sample set is reordered every training, and the training is stopped when the error tolerance or the upper limit of the training times is reached.
Specifically, in the third step, when testing the trained BP neural network model, the simulation function is used to obtain the network output for network model testing, and then whether the error between the output and the true value meets the requirement is checked.
The invention has the beneficial effects that:
the program can realize the training of the original data of the BP neural network prediction model with the high-step throwing blasting effect, dynamically display the network training process and give a training result.
2, the performance of the trained network can be checked, and a relative error curve chart representing the network performance is given.
And 3, when blasting design parameters are given, the blasting effect can be predicted, and a predicted numerical value result is displayed.
And 4, when the actual measurement blasting effect is obtained, error analysis can be realized.
And 5, self-updating of the network can be realized, when a new blasting design and a corresponding credible blasting effect measured value are obtained, the parameters can be correspondingly added into the original model database, the sample database is increased along with the increase of the use times of the program, and the program prediction precision is gradually improved.
6, the operation is simple, and the prediction result is more reliable.
Drawings
FIG. 1 is a general flow diagram of an implementation of the present invention;
FIG. 2 is a diagram of a test model;
FIG. 3 is a schematic diagram of a neural network model building process;
FIG. 4 is a graph of error drop during BP network training;
FIG. 5 is a graph of true versus predicted error;
FIG. 6 illustrates a diagram of a pile-bursting shape prediction;
in the figure: 1-throwing blasting step, 2-goaf, 3-reverse stacking accumulation body, 4-background grid plate, 1.1-simulated coal bed and 1.2 downward blast hole.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
Example 1: as shown in fig. 1 to 6, a method for predicting a step blasting throwing effect based on a BP neural network includes the following steps:
the method comprises the following steps: designing a plurality of groups of test models according to actual working conditions (the models are calculated according to field engineering geological conditions and parameters in proportion as shown in figure 2, the test models are built by concrete, model data are mine real data and calculated in proportion), obtaining the space geometric parameters of a throwing blasting front step surface and a goaf in a midwest part (west two area) of a Daisei open pit coal mine stope according to the scanning data of a scanner, carrying out tests under different groups according to the field engineering geological conditions and production requirements, and recording the test data;
step two: constructing a BP neural network model (the implementation process is shown in figure 1): the specific implementation steps are as follows: taking the step height, the coal seam thickness, the goaf upper opening width, the minimum resistance line, the hole pitch, the row pitch, the inter-hole differential time and the coal seam slope angle as input parameters, taking the throwing rate, the blasting loosening coefficient and the farthest throwing distance as output parameters, and carrying out normalization processing;
step three, determining the structural parameters of the BP neural network: preprocessing the input parameters and the output parameters after normalization processing, and then dividing the preprocessed input parameters and output parameters into a training sample set and a testing sample set; training the BP neural network model by using the data in the training sample set (as shown in figures 3 and 4), adjusting network parameters of the BP neural network model, and after the training is successful, testing the trained BP neural network model by using the data in the test data set so as to verify the correctness of the neural network model;
and step four, predicting the step blasting throwing condition under the specified working condition by using the trained BP neural network model (and comparing with the actual condition to obtain a relative error shown in a figure 5), judging the feasibility of the blasting scheme, and determining whether the throwing requirement is met. The form of the blasting pile is predicted and shown in figure 6.
The relative error of the neural network prediction values is shown in fig. 5. It can be seen from the relative error of the predicted value in fig. 5 that the predicted value obtained by the BP network is substantially consistent with the experimental value, and the relative error is within 0.3%.
Further, in the first step, each group of test models comprises a throwing blasting step 1, a goaf 2, a reverse pile accumulation body 3 and a background grating plate 4, downward blast holes 1.2 are arranged on the throwing blasting step 1, a simulated coal seam 1.1 is located right below the throwing blasting step 1, and the background grating plate 4 is vertically arranged behind the throwing blasting step 1, the goaf 2 and the reverse pile accumulation body 3.
Further, in the step one, the tests under different groups are: and carrying out multiple groups of tests by adjusting one or more than two values of the minimum resistance line, the hole pitch, the row pitch, the micro-difference time between the main control row holes, the coal bed slope angle and the unit consumption of explosive.
Further, the selected BP neural network type is a single hidden layer BP neural network, the BP neural network is trained by using data in a training sample set, in order to obtain a good learning effect, the data in the training sample set is reordered by each training, and the training is stopped when an error tolerance or an upper limit of training times is reached (as shown in fig. 4).
Further, in the third step, when testing the trained BP neural network model, the simulation function is used to obtain the network output for network model testing, and then whether the error between the output and the true value meets the requirement is checked.
It can be seen from the above embodiments that, as long as the spatial geometric parameters and the like of the corresponding mining area are measured in advance, and then the neural network type is selected and trained by using the test data, the neural network can well learn the internal implication rule thereof, and make correct prediction on the untested working condition.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes and modifications can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (5)
1. A method for predicting step blasting throwing effect based on BP neural network is characterized in that: the method comprises the following steps:
the method comprises the following steps: designing a plurality of groups of test models according to actual working conditions, carrying out tests under different groups, and recording test data;
step two: constructing a BP neural network model: taking the step height, the coal seam thickness, the goaf upper opening width, the minimum resistance line, the hole pitch, the row pitch, the inter-hole differential time and the coal seam slope angle as input parameters, taking the throwing rate, the blasting loosening coefficient and the farthest throwing distance as output parameters, and carrying out normalization processing;
step three, determining the structural parameters of the BP neural network: preprocessing the input parameters and the output parameters after normalization processing, and then dividing the preprocessed input parameters and output parameters into a training sample set and a testing sample set; training the BP neural network model by using the data in the training sample set, adjusting the network parameters of the BP neural network model, and after the training is successful, testing the trained BP neural network model by using the data in the test data set so as to verify the correctness of the neural network model;
and step four, predicting the step blasting throwing condition under the specified working condition by using the trained BP neural network model, judging the feasibility of the blasting scheme, and determining whether the throwing requirement is met.
2. The method for predicting the bench blasting throwing effect based on the BP neural network as claimed in claim 1, wherein: in the first step, each group of test models comprises a throwing blasting step (1), a goaf (2), a reverse pile accumulation body (3) and a background grating plate (4), downward blast holes (1.2) are arranged on the throwing blasting step (1), a simulation coal seam (1.1) is located under the throwing blasting step (1), and the background grating plate (4) is vertically arranged behind the throwing blasting step (1), the goaf upper opening (2) and the reverse pile accumulation body (3).
3. The method for predicting the bench blasting throwing effect based on the BP neural network as claimed in claim 1, wherein: in the first step, the tests under different groups are as follows: and carrying out multiple groups of tests by adjusting one or more than two values of the minimum resistance line, the hole pitch, the row pitch, the micro-difference time between the main control row holes, the coal bed slope angle and the unit consumption of explosive.
4. The method for predicting the bench blasting throwing effect based on the BP neural network as claimed in claim 1, wherein: the selected BP neural network type is a single hidden layer BP neural network, the BP neural network is trained by using data in a training sample set, the data in the training sample set is reordered every training, and the training is stopped when the error tolerance or the upper limit of the training times is reached.
5. The method for predicting the bench blasting throwing effect based on the BP neural network as claimed in claim 1, wherein: in the third step, when testing the trained BP neural network model, the simulation function is used for obtaining the network output to test the network model, and then whether the error between the output and the true value meets the requirement is checked.
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| CN115630257A (en) * | 2022-12-19 | 2023-01-20 | 中南大学 | Blasting funnel volume prediction method |
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| CN115630257A (en) * | 2022-12-19 | 2023-01-20 | 中南大学 | Blasting funnel volume prediction method |
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