CN103206245B - Intelligent mining device for predicting and identifying coal seams vibration and method for achieving same - Google Patents
Intelligent mining device for predicting and identifying coal seams vibration and method for achieving same Download PDFInfo
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- CN103206245B CN103206245B CN201310090840.7A CN201310090840A CN103206245B CN 103206245 B CN103206245 B CN 103206245B CN 201310090840 A CN201310090840 A CN 201310090840A CN 103206245 B CN103206245 B CN 103206245B
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Abstract
The invention discloses an intelligent mining device for predicting and identifying coal seams vibration and a method for achieving same and belongs to the technical field of hazards information early warning in mining coal seams vibration. The intelligent mining device comprises a vibration signal acquisition module, a vibrating sensor driven model, an intelligent integrated vibrating sensor, a man-machine interaction integrated display terminal module, and further includes an intelligent vibration signal detecting processor, an intelligent vibration signal predicting and indentifying processor and an intelligent data processing software module. By means of vibration signal detection technology, integrated intelligent sensor technology, high-order signal acquisition, filtering and processing technology, high-integrate digital signal processing technology, software model identification technology and the like, coal seams vibration information can be regarded as sources of the hazards information so as to accomplish the process from detection, process, prediction to identification.
Description
Technical field
The invention belongs to mine coal seam vibration disaster information warning field, particularly a kind of Intelligent mining coal seam vibration prediction identifying apparatus and method.
Background technology
At present, the prediction of down-hole disaster information and identification are mostly detect methane gas, also there is many Predicting Gas device for identifying, and the gas-detecting device based on gas, can only a category information be detected, on the block mold of prediction identification, be incomplete; And at present a can using the coal seam vibration signal before methane gas produces as judging the immediate data of disaster information and the device detected these data.
Summary of the invention
For the deficiencies in the prior art, the present invention proposes a kind of Intelligent mining coal seam vibration prediction identifying apparatus and method, to reach in real time to prediction and the identification of underground coal mine vibration disaster information, improves the object of safe prediction early warning.
A kind of Intelligent mining coal seam vibration prediction identifying apparatus, the mutual integrative display terminal module of involving vibrations signal acquisition module, vibrating sensor driver module, intelligent integrated vibrating sensor and man-machine interface, also comprises intelligent vibration signal detection processor, intelligent vibration signal early warning identification processor and data intelligence processing software module; Wherein:
Intelligent vibration signal detection processor: be for detecting underground vibrating signal and signal being carried out to the circuit arrangement of filtering;
Intelligent vibration signal early warning identification processor: be for carrying out analog-to-digital conversion to the underground vibrating signal detected, and call the wave form varies trend prediction mode of data intelligence processing software module internal reservoir or data jump prediction mode carries out the device of identification process to down-hole actual vibration data;
Data intelligence processing software module: be the device for storing wave form varies trend prediction mode and data jump prediction mode.
Adopt Intelligent mining coal seam vibration prediction identifying apparatus to carry out the method for identification, comprise the steps:
Step 1, employing vibrating sensor drive circuit Vibration on Start-up sensor;
Step 2, employing vibration signals collecting module gather underground vibrating disaster information data;
Step 3, employing intelligent vibration signal detection processor circuit detect underground vibrating signal and carry out signal filtering;
Step 4, employing intelligent vibration signal early warning identification processor carry out analog-to-digital conversion to the vibration signal detected, and according to wave form varies trend prediction mode or data jump prediction mode, prediction identification is carried out to down-hole actual vibration data, and draw prediction identification result;
Step 5, employing man-machine interface mutual integrative display terminal module display prediction identification result.
Wave form varies trend prediction described in step 4, is calculated as follows:
Wave form varies trend prediction, adopts the amplitude of wavelet maxima hopping algorithms to waveform to extract and calculate,
Formula is as follows:
-wQ′sin(w(J+Q′)+f
i′)=Qcos(w(J+Q)+f
i)-wsin(wJ+f
i) （1）
Wherein, the amplitude of wave form after Q ' expression prediction and calculation; W represents the time; J is predictive factor, and this predictive factor obtains through great many of experiments, the correlation of waveform after representing the front waveform of prediction and predicting, its span is 1.00 ~ 3.00; f
i' be waveform highs and lows span after prediction, f
i' span be 2.00 ~ 2.30; Q represents the amplitude of wave form before prediction and calculation; f
irepresent the front waveform highs and lows span of prediction, f
ispan be 2.00 ~ 2.30;
If when the difference after prediction between waveforms amplitude and the front waveforms amplitude of prediction is in (0,0.50) scope, man-machine interface mutual integrative display terminal module display friction, all are normal;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [0.50,1.00) in scope time, man-machine interface mutual integrative display terminal module display slight vibration, please notes safety;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [1.00,1.45) in scope time, man-machine interface mutual integrative display terminal module shows slight continuous shaking, please withdraw this deathtrap;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [1.45,2.00) in scope time, man-machine interface mutual integrative display terminal module shows larger intensity continuous shaking, asks emergency escape;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [2.00 ,+∞) in scope time, the mutual integrative display terminal module of man-machine interface shows judder, asks emergency escape.
Data jump prediction described in step 4, is calculated as follows:
Data jump is predicted, adopts data difference integral algorithm extract data and calculate,
Determine that waveform hop value formula is as follows:
T
j=N
(BER)×R
j+D
j（2）
Wherein, T
jrepresent the rear waveform hop value of prediction; N
(BER)for the relative influence factor, this relative influence factor obtains through great many of experiments, the saltus step situation correlation of waveform after representing the front waveform of prediction and predicting, its span is 1.00-4.00; R
jfor waveform saltus step maximum value; D
jfor waveform saltus step minimum value;
If when after prediction, waveform hop value is in (0,0.50) scope, man-machine interface mutual integrative display terminal module display friction, all are normal;
If after prediction waveform hop value [0.50,1.00) in scope time, man-machine interface mutual integrative display terminal module display slight vibration, please notes safety;
If after prediction waveform hop value [1.00,1.45) in scope time, man-machine interface mutual integrative display terminal module shows slight continuous shaking, please withdraw this deathtrap;
If after prediction waveform hop value [1.45,2.00) in scope time, man-machine interface mutual integrative display terminal module shows larger intensity continuous shaking, asks emergency escape;
If after prediction waveform hop value [2.00 ,+∞) in scope time, the mutual integrative display terminal module of man-machine interface shows judder, asks emergency escape.
Advantage of the present invention:
Intelligent mining coal seam of the present invention vibration prediction identifying apparatus and method, by means such as Vibration Signal Detection, Integrated Intelligent Sensors technology, high order signals collecting filtering treatment technology, high integrated Digital Signal treatment technology, software model identification techniques, using coal seam vibration information as disaster information source, complete from process being detected again to the process of early warning identification.
Accompanying drawing explanation
Fig. 1 is the apparatus structure block diagram of an embodiment of the present invention;
Fig. 2 is the intelligent vibration signal detection processor circuit schematic diagram of an embodiment of the present invention;
Fig. 3 is the intelligent vibration signal early warning identification processor circuit theory diagrams of an embodiment of the present invention;
Fig. 4 is the vibrating sensor circuit theory diagrams of an embodiment of the present invention;
Fig. 5 is the vibration signals collecting modular circuit schematic diagram of an embodiment of the present invention;
Fig. 6 is the flow chart that the Intelligent mining coal seam vibration prediction of an embodiment of the present invention distinguishes method.
Detailed description of the invention
Below in conjunction with accompanying drawing, an embodiment of the present invention is described further.
A kind of Intelligent mining coal seam vibration prediction identifying apparatus, as shown in Figure 1, the mutual integrative display terminal module of involving vibrations signal acquisition module, vibrating sensor driver module, intelligent integrated vibrating sensor and man-machine interface, is characterized in that: also comprise intelligent vibration signal detection processor, intelligent vibration signal early warning identification processor and data intelligence processing software module; Wherein: intelligent vibration signal detection processor is for detecting underground vibrating signal and signal being carried out to the circuit arrangement of filtering; Intelligent vibration signal early warning identification processor is for carrying out analog-to-digital conversion to the underground vibrating signal detected, and calls the wave form varies trend prediction mode of data intelligence processing software module internal reservoir or data jump prediction mode carries out the device of identification process to down-hole actual vibration data; Data intelligence processing software module is the device for storing wave form varies trend prediction mode and data jump prediction mode.
As shown in Figures 2 and 3, in the embodiment of the present invention, intelligent vibration signal detection processor adopts S3C6400 model, intelligent vibration signal early warning identification processor adopts STM32F103 model, wherein, the N1 of intelligent vibration signal detection processor S3C6400, N2 pin connects 29 of intelligent vibration signal early warning identification processor STM32F103, 30 pins, as shown in Figure 4, the drive singal input pin of vibrating sensor driver module circuit is connected with 39 pins of intelligent vibration signal early warning identification processor STM32F103, the drive singal output pin 2 of vibrating sensor driver module circuit is connected with the input pin 1 of intelligent integrated vibrating sensor, the output pin 2 of intelligent integrated vibrating sensor connects with the vibration signals collecting pin of vibration signals collecting modular circuit, as shown in Figure 5, the vibration signal output pin of vibration signals collecting modular circuit is connected with the C1 pin of intelligent vibration signal detection processor.
Adopt Intelligent mining coal seam vibration prediction identifying apparatus to carry out the method for identification, flow chart as shown in Figure 6, comprises the steps:
Step 1, employing vibrating sensor drive circuit Vibration on Start-up sensor;
Step 2, employing vibration signals collecting module gather underground vibrating disaster information data;
Step 3, employing intelligent vibration signal detection processor circuit detect underground vibrating signal and carry out signal filtering;
Step 4, employing intelligent vibration signal early warning identification processor carry out analog-to-digital conversion to the vibration signal detected, and according to wave form varies trend prediction mode or data jump prediction mode, prediction identification is carried out to down-hole actual vibration data, and draw prediction identification result;
Step 5, employing man-machine interface mutual integrative display terminal module display prediction identification result.
Wave form varies trend prediction described in step 4, is calculated as follows:
Wave form varies trend prediction, adopts the amplitude of wavelet maxima hopping algorithms to waveform to extract and calculate,
Formula is as follows:
-wQ′sin(w(J+Q′)+f
i′)=Qcos(w(J+Q)+f
i)-wsin(wJ+f
i) （1）
Wherein, the amplitude of wave form after Q ' expression prediction and calculation; W represents the time; J is predictive factor, and this predictive factor obtains through great many of experiments, the correlation of waveform after representing the front waveform of prediction and predicting, its span is 1.00 ~ 3.00; f
i' be waveform highs and lows span after prediction, f
i' span be 2.00 ~ 2.30; Q represents the amplitude of wave form before prediction and calculation; f
irepresent the front waveform highs and lows span of prediction, f
ispan be 2.00 ~ 2.30;
If when the difference after prediction between waveforms amplitude and the front waveforms amplitude of prediction is in (0,0.50) scope, man-machine interface mutual integrative display terminal module display friction, all are normal;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [0.50,1.00) in scope time, man-machine interface mutual integrative display terminal module display slight vibration, please notes safety;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [1.00,1.45) in scope time, man-machine interface mutual integrative display terminal module shows slight continuous shaking, please withdraw this deathtrap;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [1.45,2.00) in scope time, man-machine interface mutual integrative display terminal module shows larger intensity continuous shaking, asks emergency escape;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [2.00 ,+∞) in scope time, the mutual integrative display terminal module of man-machine interface shows judder, asks emergency escape;
In the embodiment of the present invention, data measured is as shown in table 1,
Table 1
In this form, waveform saltus step is all in safe range, and above list data will computing record preservation in intelligent vibration signal early warning identification processor, will predict the outcome by starting man-machine interface mutual integrative display terminal module display prediction identification result simultaneously.
Data jump prediction described in step 4, is calculated as follows:
Data jump is predicted, adopts data difference integral algorithm extract data and calculate,
Determine that waveform hop value formula is as follows:
T
j=N
(BER)×R
j+D
j（2）
Wherein, T
jrepresent the rear waveform hop value of prediction; N
(BER)for the relative influence factor, this relative influence factor obtains through great many of experiments, the saltus step situation correlation of waveform after representing the front waveform of prediction and predicting, its span is 1.00-4.00; R
jfor waveform saltus step maximum value; D
jfor waveform saltus step minimum value;
If when after prediction, waveform hop value is in (0,0.50) scope, man-machine interface mutual integrative display terminal module display friction, all are normal;
If after prediction waveform hop value [0.50,1.00) in scope time, man-machine interface mutual integrative display terminal module display slight vibration, please notes safety;
If after prediction waveform hop value [1.00,1.45) in scope time, man-machine interface mutual integrative display terminal module shows slight continuous shaking, please withdraw this deathtrap;
If after prediction waveform hop value [1.45,2.00) in scope time, man-machine interface mutual integrative display terminal module shows larger intensity continuous shaking, asks emergency escape;
If after prediction waveform hop value [2.00 ,+∞) in scope time, the mutual integrative display terminal module of man-machine interface shows judder, asks emergency escape.
In the embodiment of the present invention, data measured is as shown in table 2,
Table 2
| N (BER) | R j（mv） | D j（mv） | T j（mv） |
| 2.00 | 2.21 | 2.18 | 2.17 |
| 1.00 | 2.40 | 2.43 | 2.40 |
| 1.50 | 2.12 | 2.11 | 2.12 |
| 1.25 | 2.14 | 2.05 | 2.13 |
| 2.25 | 2.17 | 2.13 | 2.21 |
| 3.00 | 2.21 | 2.18 | 2.22 |
| 2.20 | 2.25 | 2.19 | 2.24 |
| 3.03 | 2.12 | 2.20 | 2.25 |
| 3.12 | 2.02 | 2.17 | 2.10 |
| 3.22 | 2.10 | 2.14 | 2.13 |
| 3.50 | 2.09 | 2.16 | 2.11 |
Above list data will computing record preservation in intelligent vibration signal early warning identification processor, will predict the outcome by starting man-machine interface mutual integrative display terminal module display prediction identification result simultaneously.
Claims (1)
1. an Intelligent mining coal seam vibration prediction discrimination method, the method adopts Intelligent mining coal seam vibration prediction identifying apparatus, the mutual integrative display terminal module of involving vibrations signal acquisition module, vibrating sensor driver module, intelligent integrated vibrating sensor and man-machine interface, also comprises intelligent vibration signal detection processor, intelligent vibration signal early warning identification processor and data intelligence processing software module; Wherein:
Intelligent vibration signal detection processor: be for detecting underground vibrating signal and signal being carried out to the circuit arrangement of filtering;
Intelligent vibration signal early warning identification processor: be for carrying out analog-to-digital conversion to the underground vibrating signal detected, and call the wave form varies trend prediction mode of data intelligence processing software module internal reservoir or data jump prediction mode carries out the device of identification process to down-hole actual vibration data;
Data intelligence processing software module: be the device for storing wave form varies trend prediction mode and data jump prediction mode; It is characterized in that: method comprises the steps:
Step 1, employing vibrating sensor drive circuit Vibration on Start-up sensor;
Step 2, employing vibration signals collecting module gather underground vibrating disaster information data;
Step 3, employing intelligent vibration signal detection processor circuit detect underground vibrating signal and carry out signal filtering;
Step 4, employing intelligent vibration signal early warning identification processor carry out analog-to-digital conversion to the vibration signal detected, and according to wave form varies trend prediction mode or data jump prediction mode, prediction identification is carried out to down-hole actual vibration data, and draw prediction identification result;
Described wave form varies trend prediction, is calculated as follows:
Wave form varies trend prediction, adopts the amplitude of wavelet maxima hopping algorithms to waveform to extract and calculate,
Formula is as follows:
-wQ′sin(w(J+Q′)+f
i′)＝Qcos(w(J+Q)+f
i)-wsin(wJ+f
i) (1)
Wherein, the amplitude of wave form after Q ' expression prediction and calculation; W represents the time; J is predictive factor, and this predictive factor obtains through great many of experiments, the correlation of waveform after representing the front waveform of prediction and predicting, its span is 1.00 ~ 3.00; f
i' be waveform highs and lows span after prediction, f
i' span be 2.00 ~ 2.30; Q represents the amplitude of wave form before prediction and calculation; f
irepresent the front waveform highs and lows span of prediction, f
ispan be 2.00 ~ 2.30;
If when the difference after prediction between waveforms amplitude and the front waveforms amplitude of prediction is in (0,0.50) scope, man-machine interface mutual integrative display terminal module display friction, all are normal;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [0.50,1.00) in scope time, man-machine interface mutual integrative display terminal module display slight vibration, please notes safety;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [1.00,1.45) in scope time, man-machine interface mutual integrative display terminal module shows slight continuous shaking, please withdraw deathtrap;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [1.45,2.00) in scope time, man-machine interface mutual integrative display terminal module shows larger intensity continuous shaking, asks emergency escape;
If the difference after prediction before waveforms amplitude and prediction between waveforms amplitude [2.00 ,+∞) in scope time, the mutual integrative display terminal module of man-machine interface shows judder, asks emergency escape;
Described data jump prediction, is calculated as follows:
Data jump is predicted, adopts data difference integral algorithm extract data and calculate,
Determine that waveform hop value formula is as follows:
T
j＝N
(BER)×R
j+D
j(2)
Wherein, T
jrepresent the rear waveform hop value of prediction; N
(BER)for the relative influence factor, this relative influence factor obtains through great many of experiments, the saltus step situation correlation of waveform after representing the front waveform of prediction and predicting, its span is 1.00-4.00; R
jfor waveform saltus step maximum value; D
jfor waveform saltus step minimum value;
If when after prediction, waveform hop value is in (0,0.50) scope, man-machine interface mutual integrative display terminal module display friction, all are normal;
If after prediction waveform hop value [0.50,1.00) in scope time, man-machine interface mutual integrative display terminal module display slight vibration, please notes safety;
If after prediction waveform hop value [1.00,1.45) in scope time, man-machine interface mutual integrative display terminal module shows slight continuous shaking, please withdraw deathtrap;
If after prediction waveform hop value [1.45,2.00) in scope time, man-machine interface mutual integrative display terminal module shows larger intensity continuous shaking, asks emergency escape;
If after prediction waveform hop value [2.00 ,+∞) in scope time, the mutual integrative display terminal module of man-machine interface shows judder, asks emergency escape;
Step 5, employing man-machine interface mutual integrative display terminal module display prediction identification result.
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| CN103487250B (en) * | 2013-10-08 | 2015-08-05 | 中国矿业大学(北京) | Based on the coal mine equipment anticipatory maintenance method of two-dimensional projections |
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| JPH04244933A (en) * | 1991-01-31 | 1992-09-01 | Fuji Heavy Ind Ltd | Device for detecting knocking of engine |
| JP3073681B2 (en) * | 1995-12-27 | 2000-08-07 | 株式会社光研 | Multi-point simultaneous measurement system |
| JP2006118869A (en) * | 2004-10-19 | 2006-05-11 | Nsk Ltd | Defect detector for rolling bearing, and defect detection method for the rolling bearing |
| JP2008170275A (en) * | 2007-01-11 | 2008-07-24 | Toshiba Corp | Wireless vibration measuring system and method therefor |
| CN101762830B (en) * | 2009-09-29 | 2013-01-02 | 中国矿业大学 | Distributed coal mine rock burst monitoring method |
| CN101718212B (en) * | 2009-10-09 | 2013-05-22 | 西安西科测控设备有限责任公司 | Device for tracking and early warning outburst danger of mine coal and gas in real time |
| CN101787897B (en) * | 2009-12-30 | 2013-05-22 | 西安西科测控设备有限责任公司 | System and method for predicting coal and gas outburst risk of mine in real time |
| CN201794617U (en) * | 2009-12-30 | 2011-04-13 | 西安西科测控设备有限责任公司 | System for predicting outburst risk of coal and gas in mine in real time |
| CN102635405B (en) * | 2012-04-17 | 2014-01-22 | 辽宁工程技术大学 | Real-time microseismic monitoring system and monitoring method for coal mines |
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