CN105718668A - Open pit mine cast blasting effect analysis method - Google Patents
Open pit mine cast blasting effect analysis method Download PDFInfo
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Abstract
The invention discloses an open pit mine cast blasting effect analysis method which is characterized in that an AHP-CM evaluation method is provided for making research on a relation of an open pit mine cast blasting operation effect and a cast blasting influence factor and determining the cast blasting effect. The method comprises the following steps: determining that cast blasting effect influence factors mainly comprise unit explosive consumption, a limit vibration speed, an effective cast rate and a bulk factor according to related documents and open pit mine cast blasting field experiences, and grading cast blasting effects; representing a relation between the cast blasting factors and the cast blasting effects by virtue of the characteristic of a cloud model (CM) that data discreteness, fuzziness and randomness can be represented; obtaining weights of influence of these factors on the cast blasting effects by virtue of AHP; finally obtaining cast blasting effect grades of each group of influence factors. The method can be used for cast blasting effect analysis.
Description
Technical field
The present invention relates to Mineral Engineering, particularly relate to carry out effect analysis to throwing is quick-fried.
Background technology
Pinpoint blasting (cast blasting) (being called for short " throwing quick-fried ") is the explosion skill that rock is cast aside original place by engine request
Art.Conventional orientation is thrown quick-fried, have one side, bilateral, on to, multidirectional throwing, also have reinforcement throwing or side throwing opposite side to loosen
The types such as explosion.Throw and quick-fried can be conducive to speeding up the work by a large amount of cubic meter of stone throwings to engine request place, the reduction of erection time, non-
Artificial or common mechanical can be compared.It is efficient a, blasting technique for saving cost of winning that opencut is thrown quick-fried, utilizes explosive quick-fried
The fried energy produced is by explosion rock mass throwing to bare ground, and the most effectively throwing rate is up to more than 30%, thus subtracts
Lack peel-off device operation and stripping cost has been greatly saved.
Opencut throws quick-fried effect to be affected by factors, can be divided into explosive, geological conditions and blasting scheme generally.Fried
Medicine refers to explosive type, embedding manner, Pop Diameter, explosive specific consumption etc.；Different geological conditions is relatively big on explosion impact,
The physico mechanical characteristic of rock mass itself, joint, crack and trend within rock mass also affect demolition effect；Different implementation of blastings
Scheme, also affects such as presplit blasting and hole pattern yardstick and throws quick-fried effect.In above-mentioned factor, some can be by people's control, and other are then
It is difficult to, even the most not unknown by prospecting in detail.So the relation thrown between quick-fried effect and influence factor has necessarily
Ambiguity, randomness and discreteness.
Throwing quick-fried effect for understanding, some scholars have been carried out correlational study..But these researchs are inevitably, and
Ignore the ambiguity of data, randomness and discreteness to a certain extent.Because these methods can not retain this of data
A little characteristics and calculate further, and actually these characteristics do exist among data, should not be left in the basket.
In order to solve to throw quick-fried effect and ambiguity, randomness and the discreteness in influence factor's relation, propose to use cloud mould
Type binding hierarchy analytic process processes this problem.Cloud model can represent the ambiguity of data, randomness and discreteness, and then obtains
Represent the cloud model of factor value.AHP is utilized to obtain the weight relationship between each factor.Finally give and be made up of respectively different factor values
Throw quick-fried scheme works and be subordinate to the degree of each effect category.Thus provide foundation for throwing quick-fried program decisions.
Summary of the invention
1 cloud model rationale
If U is a quantitative domain represented with exact value, C is the qualitativing concept on U, if quantitative value x ∈ U, and x is qualitative
Stochastic implementation of concept C, x degree of membership μ (x) ∈ [0,1] to C, is the random number with steady tendency, i.e.,,.Then x distribution on domain U referred to as cloud is designated as C(x), each x is referred to as water dust (x, a μ
(x)).
The numerical characteristic of cloud reflects the quantitative characteristic of qualitativing concept, characterizes with expectation Ex, entropy En and super entropy He, is designated as C
(Ex, En, He).Expect Ex representation theory domain space the most representational qualitativing concept value.Entropy En be qualitativing concept ambiguity and with
The comprehensive measurement of machine.Super entropy He describes the uncertainty measure of entropy, reflects the cohesion degree of water dust in domain space.
The algorithm or the hardware that generate water dust are referred to as cloud generator, send out including forward cloud, reverse cloud, X condition cloud and Y condition cloud
Raw device.Normal Cloud Generator achieves scope and the regularity of distribution obtaining quantitative data in the qualitative information that prophesy value is expressed, tool
There are forward direction, direct feature.Here using Normal Cloud Generator, its water dust process generating requirement is as follows:
1) generating with En for expectation, He is the normal random number En ' of standard deviation；
2) generation one is with En for expectation, and the absolute value of En ' is the normal random number x of standard deviationi, xiIt is referred to as domain space U's
One water dust；
3) calculate, thenForDegree of membership about C.
4) circulation 1) ~ 3), generate n water dust, then stop.
The quantitative value that impact is thrown quick-fried Indexes of Evaluation Effect by forward cloud model carries out cloud.The numerical characteristic meter of cloud model
Calculation formula is:
(1)
In formula: i is constant, specifically can adjust according to the fuzzy threshold degree of index variable itself, value 0.01 here.
For the quantitative target variable of the most monolateral boundary, such as xj∈(aj,+∞), can be first according to the maximum upper limit of data
Or lower limit, determine its default boundary parameter or expected value, then refer again to formula (1) and calculate the numerical characteristic of cloud model.As commented
Valency factor xi4 opinion ratings interval be respectively(0, a),(a, b),(b, c) and(c ,+∞), the most each opinion rating pair
Answer cloud model numerical characteristic, be shown in Table 1.
Table 1: the numerical characteristic computation rule of cloud model
The opencut of 2 AHP-CM throws the structure of quick-fried Evaluation Model on Effectiveness
The step of 2.1 evaluation models
1) determine that the factor of quick-fried effect is thrown in impact, and use AHP to determine the weight of each factor index；
2) require to determine cloud numerical characteristic according to the quick-fried effect assessment of throwing；
3) generate the normal distribution random number water dust with composition cloud, and then generate cloud model；
4) calculate each desired value and belong to the degree of membership of cloud at different levels；
5) degree of membership is multiplied by corresponding index weights and obtains Comprehensis pertaining；
6) rank that Comprehensis pertaining maximum is corresponding is final assessment rank.
The factor throwing quick-fried effect assessment determines
Research according to Practical Project, it is contemplated that the science of index system, system optimization, general comparability and practicality, recognizes
At least include for throwing the appraisement system of quick-fried effect: explosive specific consumption, limit vibration velocity, effective throwing rate and the coefficient of volumetric expansion.
Effectively throwing rate directly affects process system workload and cost.Effectively throwing rate is the biggest, and throwing stone amount is the biggest, more subtracts
Peel off cost less.Opencut throws quick-fried middle explosive specific consumption to be affected opencut economic indicator and throws quick-fried effect.Explosive unit disappears
Quick-fried casting distance is thrown in consumption impact, and it can be reflected in effective throwing rate index.The coefficient of volumetric expansion affects bucket shovel shovel dress efficiency, quick-fried
Heap settling height and auxiliary equipment workload etc..Except to obtain good demolition effect, also to reduce blasting vibration effect as far as possible
Reply side slope and the stabilizing influence of surface buildings, so velocity of vibration is also considered as.To sum up obtain impact and throw quick-fried effect
Index: 1) explosive specific consumption R/kg/m3；2) limit velocity of vibration V/cm/s；3) effectively jettisoning rate J/100%；4) pine
Dissipate coefficient E.
Opencut is thrown quick-fried effect assessment grade as comment layer, respectively with very well (), good (), general () and poor () represent, as shown in table 2.
Table 2: opencut throws quick-fried effectiveness indicator and classification
The weight calculation of 2.3 each influence factors
After cloud model obtains each factor index score, the weight of these indexs to be determined, and then determine comprehensive evaluation result, this
In use analytic hierarchy process (AHP) agriculture products weight.When using AHP method to be evaluated with decision-making, following step substantially can be divided into
Rapid:
1) relation between each fundamental in analysis and evaluation system, sets up the recursive hierarchy structure of system.
2) each element of same level is compared two-by-two about the importance of a certain criterion in last layer time, construct two
Two multilevel iudge matrixes.
3) consistency check of judgment matrix.Judgment matrix is carried out consistency check, to determine whether weight distribution closes
Reason.
4) calculated by comparison element for the relative weighting of this criterion, i.e. Mode of Level Simple Sequence by judgment matrix.
5) calculate each layer key element synthesis (always) weight to system purpose (general objective), and each alternative is carried out level
Total sequence and consistency check.
Each index that impact is thrown quick-fried effect carries out expert analysis mode, by obtaining the importance degree of expert analysis mode matrix, warp
The weight of each factor: w={0.3036,0.1024,0.3793,0.2148} is obtained after crossing consistency check and normalization.
Judgment matrix is as shown in table 3.
Table 3 influence index judgment matrix
2.4 cloud models throwing quick-fried index
Give cloud model numerical characteristic computation rule and the given throwing quick-fried effectiveness indicator classification of table 2 according to table 1, obtain throwing quick-fried effect
The cloud model numerical characteristic value of index, as shown in table 4.
The cloud model numerical characteristic of table 4 demolition effect index
After the cloud model numerical characteristic of above-mentioned each index determines, use Normal Cloud Generator, 4 indexs are generated corresponding cloud
Model, as shown in Figure 1.In figure, abscissa represents the value of each influence index, and vertical coordinate represents the degree of membership of cloud model.
Accompanying drawing explanation
Fig. 1 opencut throws the cloud model of quick-fried effect assessment grade.
Detailed description of the invention
In order to application process and the effectiveness thereof of evaluation methodology are described, the application example in certain ore deposit is given below.
Certain opencut is Mining Group subordinate colliery, Fuxin, and the different side slopes at this open coal mine implement four times and throw quick-fried work
Industry.Situation according to side slope form and rock mass condition have chosen different explosive specific consumptions, quick-fried with throwing during throwing is quick-fried
After end, limit velocity of vibration, effectively jettisoning rate, the coefficient of volumetric expansion etc. are measured.The quick-fried effect of throwing throwing quick-fried operation for four times is commented
Valency parameter is as shown in table 5.
Table 5: opencut throws quick-fried effectiveness indicator numerical value
| Sample | R/kg/m3 | V/cm/s | J/100% | E |
| 1 | 0.66 | 1.38 | 53 | 1.24 |
| 2 | 0.75 | 1.77 | 36 | 1.13 |
| 3 | 0.76 | 1.59 | 33 | 1.22 |
| 4 | 0.8 | 1.98 | 27 | 1.03 |
Calculate data x of i-th indexiIt is under the jurisdiction of certain degree of membership throwing quick-fried gradation of effects.With explosive unit consumption in sample 1
The calculating process of amount explanation degree of membership.Obtain this index value by Normal Cloud Generator and be subordinate to degree of membership μ of the quick-fried effect of each throwing
=0.8990, μ=0, μ=0, μ=0, according to maximum membership grade principle, this index of sample 1 is subordinate to (I) very well.
For 4 indexs of sample 1,4 subjection degree of levels of this index should be multiplied by according to the weight of each index,
Then the angle value addition that is subordinate to of corresponding level is obtained comprehensive evaluation result.Its advantage is to take into full account that sample index is under the jurisdiction of respectively
The degree of individual rank, is transferred to comprehensive evaluation result by each degree of membership, just fully can be under the jurisdiction of spy at different levels by reflected sample
Levy.(III) level comprehensive evaluation result=0.3036 × 0+0.1024 × 0.0004+0.3793 × 0+0.2148 of such as sample 1 ×
0.0018=0.0004.Again because (I), (II), (III) of sample 1, the Comprehensis pertaining of (IV) be 0.3013 respectively,
0.1921,0.0004,0, maximum membership degree 0.3013 is (I) level, so the quick-fried gradation of effects of the throwing of overall merit sample 1 is (I)
Level.The degree of membership evaluating data of each index is as shown in table 6, and overall merit data are as shown in table 7.
The degree of membership evaluating data of each index of table 6
Table 7 overall merit data
| (I) | (II) | (III) | (IV) | Final appraisal results | |
| 1 | 0.3013 | 0.1921 | 0.0004 | 0 | I |
| 2 | 0.2956 | 0.2611 | 0.2584 | 0.1842 | I |
| 3 | 0.2926 | 0.2828 | 0.6317 | 0.3152 | III |
| 4 | 0.2512 | 0.0000 | 0.0108 | 0.4210 | IV |
It practice, the structure of said method and application are a kind of multiple attribute decision making (MADM)s.The certain methods of this method with proposition is entered
Row compares.Propose a kind of cooperative game-cloud AHP method, and be applied to constructing metro tunnel way choice, the method
Feature is to be analyzed the selection of multidigit expert, finally gives preferred plan.And AHP-CM method relatively cooperative game-cloud
AHP method is simply too much, is suitable for the analysis of expert opinion under equal weight.Give a kind of based on Triangular Fuzzy Number and TOPSIS
Multiple attributive decision making method, the method uses fuzzy number to process the ambiguity of data, but for randomness and the place of discreteness
Reason is obviously appropriate not as cloud model, represents that the ability throwing quick-fried data is not as AHP-CM in this way.Give a kind of AHP can open up
Integrated approach is evaluated in order to highway safety grade, and the method is suitable for resolving contradiction the analysis of opposite problem, and discrete to data
Property, randomness and ambiguity represent that ability is not good enough.Propose a kind of Neural Network Optimization genetic algorithm blasting parameter is carried out
Selecting, feature is the suitable function as genetic algorithm of the neutral net after utilizing training, is suitable for the scheme choosing of multiple-input and multiple-output
Select problem.But complicated more than AHP-CM, and need substantial amounts of training data.Give based on the direct fuzzy set of structural elements and
The blasting scheme system of selection of GA algorithm, although this method relatively Neural Network Optimization genetic algorithm can obtain tying the most accurately
Really, but equally exist and calculate complicated and that data volume is big problem.To sum up, utilize cloud model can reflect simultaneously data discreteness,
Ambiguity and the feature of randomness, without using increasingly complex algorithm, or the combination of algorithm, it is not required that substantial amounts of number
According to, opencut is thrown quick-fried analysis and is provided the foundation by this.Determine the weight relationship between each factor in combination with AHP, just can get
The different effectiveness rankings throwing quick-fried scheme, decrease data analysis and calculate cost.Foundation is provided for throwing quick-fried engineering.
Claims (5)
1. an opencut throws quick-fried effect analysis method, it is characterised in that throws quick-fried operation effectiveness for research opencut and throws with impact
Relation between quick-fried factor, determines that the quick-fried effect of throwing is good and bad, proposes the evaluation methodology of a kind of step analysis-cloud model (AHP-CM)；
It comprises the steps: first to throw quick-fried field experience according to pertinent literature and opencut, determine main impact throw quick-fried effect because of
Element is: explosive specific consumption, limit vibration velocity, effective throwing rate and the coefficient of volumetric expansion, and carries out classification to throwing quick-fried effect；Utilize cloud
Model can represent the feature of data discrete, ambiguity, randomness, represents the relation thrown quick-fried factor and throw between quick-fried effect；Profit
These factors are obtained for throwing the weight of quick-fried influential effect with AHP；Finally give the throwing quick-fried effect classification of each group of influence factor；This
Invention can be used for carrying out effect analysis to throwing is quick-fried.
Cloud model the most according to claim 1, it is characterised in that the numerical characteristic computation rule of cloud model.
3. an opencut throws quick-fried effect analysis method, it is characterised in that the step of evaluation model, including:
1) determine that the factor of quick-fried effect is thrown in impact according to list of references and practical experience, and use AHP to determine each factor index
Weight；
2) require to determine cloud numerical characteristic according to the quick-fried effect assessment of throwing；
3) generate the normal distribution random number water dust with composition cloud, and then generate cloud model；
4) calculate each desired value and belong to the degree of membership of cloud at different levels；
5) degree of membership is multiplied by corresponding index weights and obtains Comprehensis pertaining；
6) rank that Comprehensis pertaining maximum is corresponding is final assessment rank.
4. an opencut throws quick-fried effect analysis method, it is characterised in that the factor throwing quick-fried effect assessment determines, including: explosive
Specific consumption, limit vibration velocity, effective throwing rate and the coefficient of volumetric expansion, throw opencut quick-fried effect assessment grade as comment layer,
Respectively with very well (), good (), general () and poor () represent.
5. an opencut throws quick-fried effect analysis method, it is characterised in that the weight calculation of each influence factor, including:
Relation between each fundamental in analysis and evaluation system, sets up the recursive hierarchy structure of system；
Comparing each element of same level two-by-two about the importance of a certain criterion in last layer time, structure compares two-by-two
Judgment matrix；
3) consistency check of judgment matrix；Judgment matrix is carried out consistency check, the most reasonable to determine weight distribution；
Calculated by comparison element for the relative weighting of this criterion, i.e. Mode of Level Simple Sequence by judgment matrix；
Calculate each layer key element synthesis (always) weight to system purpose (general objective), and each alternative is carried out total hierarchial sorting
And consistency check.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111339486A (en) * | 2020-02-28 | 2020-06-26 | 青岛理工大学 | Deep foundation pit blasting vibration velocity risk level big data evaluation method |
| CN113343759A (en) * | 2021-04-28 | 2021-09-03 | 鞍钢矿业爆破有限公司 | Method for evaluating damage effect of open-pit blasting flying stones by using unmanned aerial vehicle |
| CN114815663A (en) * | 2022-05-13 | 2022-07-29 | 天津大学 | Spacecraft attitude simulation control method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006098356A (en) * | 2004-09-30 | 2006-04-13 | Fuji Electric Holdings Co Ltd | Movable mechanism vibration analysis device, and input method of movable mechanism vibration analysis parameter |
| CN103093099A (en) * | 2013-01-22 | 2013-05-08 | 辽宁工程技术大学 | Opencast coal mine safety evaluation method |
-
2016
- 2016-01-22 CN CN201610042954.8A patent/CN105718668A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006098356A (en) * | 2004-09-30 | 2006-04-13 | Fuji Electric Holdings Co Ltd | Movable mechanism vibration analysis device, and input method of movable mechanism vibration analysis parameter |
| CN103093099A (en) * | 2013-01-22 | 2013-05-08 | 辽宁工程技术大学 | Opencast coal mine safety evaluation method |
Non-Patent Citations (2)
| Title |
|---|
| 刘东飞: ""基于ＡＨＰ与云模型的软件过程绩效评价方法"", 《计算机工程与设计》 * |
| 蒲传金 等: ""爆破效果综合评价的模糊层次分析法模型"", 《矿业快报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111339486A (en) * | 2020-02-28 | 2020-06-26 | 青岛理工大学 | Deep foundation pit blasting vibration velocity risk level big data evaluation method |
| CN113343759A (en) * | 2021-04-28 | 2021-09-03 | 鞍钢矿业爆破有限公司 | Method for evaluating damage effect of open-pit blasting flying stones by using unmanned aerial vehicle |
| CN114815663A (en) * | 2022-05-13 | 2022-07-29 | 天津大学 | Spacecraft attitude simulation control method and system |
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Application publication date: 20160629 |