CN109341778A - A kind of information-based intelligence control system and control method of security monitoring Tailings Dam - Google Patents
A kind of information-based intelligence control system and control method of security monitoring Tailings Dam Download PDFInfo
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Abstract
The invention belongs to Tailings Dam monitoring technology fields, the information-based intelligence control system and control method of a kind of security monitoring Tailings Dam are disclosed, the information-based intelligence control system of the security monitoring Tailings Dam includes: video acquisition module, vibration monitoring module, pressure monitoring module, water level monitoring module, main control module, dam body model construction module, risk evaluation module, alarm modules, data memory module, display module.The present invention by dam body model construction module can Controlling model test in saturation shape and distribution characteristics, the dam body model of experiment and the similitude of practical dam body are improved, to improve the accuracy of experiment；Meanwhile Safety of Tailings Dam situation can not only be evaluated according to historical data by risk evaluation module, also increase spatial information, further refine Tailings Dam appraisement system, be convenient for Tailings Dam real-time early warning, risk investigation controls disaster in rudiment.
Description
Technical field
The invention belongs to the informationization intelligence controls of Tailings Dam monitoring technology field more particularly to a kind of security monitoring Tailings Dam
System and control method processed.
Background technique
Tailings Dam, which refers to build a dam, intercepts what the mouth of a valley or exclosure were constituted, carries out mineral selection to store up metal or non-metal mine
Tailing or the place of other industrial residues is not discharged afterwards.Tailings Dam is the artificial mud-rock flow danger source with high potential energy, is deposited
In dam break danger, once accident, be easy to cause severe and great casualty.The red mud reservoir that melting waste slag is formed, power generation waste residue are formed useless
Slag library should be also managed by Tailings Dam.Tailing refers to the ore that metal or non-metal mine produce, and has selected through dressing plant
" waste residue " discharged after the concentrate of value.These tailings are since quantity is big, containing the useful or harmful components that cannot temporarily handle,
Arbitrarily discharge, it will cause resource loss, large area, which is annihilated, farmland or silts river up, pollutes environment.However, existing to Tailings Dam
Dam body can only accomplish the control to ground water level when monitoring, cannot directly carry out to seepage water saturation location and shape accurate
Control；Timely early warning cannot be carried out to Safety of Tailings Dam simultaneously, caused a serious accident.
In conclusion problem of the existing technology is:
(1) it can only accomplish the control to ground water level when the existing monitoring to tailings warehouse dam body, it cannot be directly to seepage flow water logging
Profit line position and shape are accurately controlled；Timely early warning cannot be carried out to Safety of Tailings Dam simultaneously, caused a serious accident.
(2) shock sensor real-time monitoring Tailings Dam vibration data information, traditional algorithm of use to signals and associated noises into
Row decomposes, and cannot carry out effective digital filtering to inductive signal with extracted valid data, and then must cannot reduce to greatest extent
Error before and after signal denoising.
(3) when pressure sensor real-time monitoring tailings warehouse dam body compression data information, pressure sensor is easy the temperature received
Influence, generate measurement error measurement error is compensated using traditional algorithm, cannot get preferable compensation effect.
(4) waterlevel data information in magnetostrictive liquid level sensor real-time monitoring Tailings Dam, in the big environment of difference variation
Middle temperature drift phenomenon is serious, and generating many factors of temperature drift and the degree of temperature drift is in non-linear relation, using current algorithm, no
Temperature, which can be eliminated, influences magnetostrictive liquid level sensor.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of information-based intelligent controls of security monitoring Tailings Dam
System and control method.
The invention is realized in this way a kind of information-based intelligent control method of security monitoring Tailings Dam, the safety prison
Control Tailings Dam information-based intelligent control method include:
The first step acquires Tailings Dam live video data information, real-time monitoring Tailings Dam vibration data information, real-time monitoring
Waterlevel data information in tailings warehouse dam body compression data information and real-time monitoring Tailings Dam；
Second step constructs dam body threedimensional model using above-mentioned data information, carries out tailing to the data information detected
Library risk is assessed；
Third step, when assessment result is dangerous, alarm is timely alarmed；
4th step stores acquisition video, vibration, pressure, waterlevel data information and assessment result using memory；
5th step is tied by the Tailings Dam video of display screen display acquisition, vibration, pressure, waterlevel data information and assessment
Fruit.
Further, the information-based intelligent control method of the security monitoring Tailings Dam passes through shock sensor real-time monitoring tail
Mine library vibration data information carries out wavelet decomposition to signals and associated noises, the specific steps are as follows:
Signal f (t) containing white Gaussian noise are as follows:
F (t)=s (t)+g (t)；
S (t) is useful signal in formula, and g (t) is white Gaussian noise independent and with distribution, obeys N (0, σ2) distribution；
Step 1 carries out discrete sampling to noise-containing signal, obtains discrete signal f (n), wavelet conversion coefficient ωf
(j, k) are as follows:
K is shift factor in formula, and j is decomposition scale number；
Step 2 selects db5 wavelet basis function to carry out wavelet decomposition to signals and associated noises to be treated, obtains under j scale
J approximation component and j details coefficients, calculate the orthogonal wavelet decomposition formula of noisy acoustical signal f (n) are as follows:
C in formulaj-1For approximation wavelet coefficients, n is discrete sampling points, dj-1For detail wavelet coefficients, h*And g*Be one group just
The mirror filter group of friendship；
Step 3 carries out Multiscale Wavelet Decomposition to signals and associated noises, retains whole low-frequency wavelet coefficients, for high frequency system
Number sets a threshold value first, then will be less than the high-frequency wavelet coefficient amplitude whole zero setting under each scale of threshold value, finally adopt
Shrink process is carried out to remaining all coefficients higher than threshold value with different threshold function tables, obtains the estimation small echo under each scale
Coefficient；
Step 4, since wavelet reconstruction is the inverse process of wavelet decomposition, the signal estimated value after reconstruct denoising passes through estimation
Wavelet coefficient obtains after carrying out inverse wavelet transform processing, the wavelet coefficient of reconstruct are as follows:
Further, pass through pressure sensor real-time monitoring tailings warehouse dam body compression data information, the temperature of pressure sensor
Drift has preferable compensation effect, using the RBF neural network algorithm based on ant colony clustering algorithm, comprising the following specific steps
Step 1 clusters input sample using ant colony clustering algorithm, and obtained cluster centre is as RBF nerve net
The central value of network Hidden unit；
Step 2, with the weight of Pseudoinverse algorithm adjustment hidden layer to output layer；
Step 3, the output for calculating each Hidden unit are gone forward side by side professional etiquette generalized, judge that each Hidden unit exports network
Contribution；
Step 4 simplifies network structure with the method for reduction under conditions of meeting error.
Another object of the present invention is to provide a kind of information-based intelligent control sides for implementing the security monitoring Tailings Dam
The information-based intelligence control system of the security monitoring Tailings Dam of method, the information-based intelligence control system of the security monitoring Tailings Dam
Include:
Video acquisition module is connect with main control module, for acquiring Tailings Dam live video data in real time by image pick-up device
Information；
Vibration monitoring module, connect with main control module, for passing through shock sensor real-time monitoring Tailings Dam vibration data
Information；
Pressure monitoring module is connect with main control module, for being pressurized by pressure sensor real-time monitoring tailings warehouse dam body
Data information；
Water level monitoring module, connect with main control module, for passing through water level number in liquid level sensor real-time monitoring Tailings Dam
It is believed that breath；
Main control module, with video acquisition module, vibration monitoring module, pressure monitoring module, water level monitoring module, master control mould
Block, dam body model construction module, risk evaluation module, alarm modules, data memory module, display module connection, for passing through
Single-chip microcontroller controls modules and works normally；
Dam body model construction module, connect with main control module, for constructing dam body threedimensional model by modeling software；
Risk evaluation module is connect with main control module, for by assessing software according to monitoring data to Tailings Dam risk
Property is assessed；
Alarm modules are connect with main control module, for passing through alarm according to the progress of monitoring risk data and alarm；
Data memory module is connect with main control module, for storing acquisition video, vibration, pressure, water level by memory
Data information；
Display module is connect with main control module, for by display control system interface and acquisition Tailings Dam video,
Vibration, pressure, waterlevel data information.
Another object of the present invention is to provide a kind of information-based intelligent control sides using the security monitoring Tailings Dam
The Tailings Dam management terminal of method.
Another object of the present invention is to provide a kind of information-based intelligent control sides using the security monitoring Tailings Dam
The computer of method.
Advantages of the present invention and good effect are as follows: the present invention by dam body model construction module can Controlling model test in
The shape and distribution characteristics of saturation improve the dam body model of experiment and the similitude of practical dam body, to improve the standard of experiment
True property；It is divided into several big sections according to saturation of the shape of dam seepage line to dam body, segmentation is studied, according to practical dam body
Saturation physical location and shape approximation divided, complex-shaped saturation can be simulated, improve the accurate of test
Property；Four layers of horizontal velatively waterproof layer are laid with impermeable material in dam body when establishing dam body model, impermeable stratum is adopted
With the horizontal theoretical impermeable stratum for being laid with substitution null hypothesis, influence of the impermeable stratum to dam slope stability is reduced, while can be with
Guarantee that the following dam body of saturation is to moisten, does not influence the intensity of dam body model；Meanwhile it can not only root by risk evaluation module
Safety of Tailings Dam situation is evaluated according to historical data, also increase spatial information, further refines Tailings Dam appraisement system, be convenient for tailing
Library real-time early warning, risk investigation, disaster is controlled in rudiment.
Vibration monitoring module is by shock sensor real-time monitoring Tailings Dam vibration data information in the present invention, in order to sense
Induction signal carries out effective digital filtering and carries out wavelet decomposition to signals and associated noises using small echo with extracted valid data, and then most
Limits must reduce the error before and after signal denoising.
When pressure monitoring module passes through pressure sensor real-time monitoring tailings warehouse dam body compression data information in the present invention, pressure
Force snesor be easy by temperature influenced, generate measurement error, it is small in order to reduce error, improve precision, to pressure sensor
Temperature drift have preferable compensation effect, using the RBF neural network algorithm based on ant colony clustering algorithm.
Water level monitoring module in the present invention passes through waterlevel data information in liquid level sensor real-time monitoring Tailings Dam, wherein
Liquid level sensor use magnetostrictive liquid level sensor, temperature drift phenomenon is serious in the big environment of difference variation, and generate temperature
The many factors of drift and the degree of temperature drift are in non-linear relation, influence, adopt to eliminate temperature to magnetostrictive liquid level sensor
With Adaptive Neural-fuzzy Inference algorithm.
Detailed description of the invention
Fig. 1 is the information-based intelligent control method flow chart of security monitoring Tailings Dam provided in an embodiment of the present invention.
Fig. 2 is the information-based Structure of intelligent control system schematic diagram of security monitoring Tailings Dam provided in an embodiment of the present invention；
In figure: 1, video acquisition module；2, vibration monitoring module；3, pressure monitoring module；4, water level monitoring module；5, main
Control module；6, dam body model construction module；7, risk evaluation module；8, alarm modules；9, data memory module；10, mould is shown
Block.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
Structure of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, the information-based intelligent control method of security monitoring Tailings Dam provided in an embodiment of the present invention include with
Lower step:
S101: acquisition Tailings Dam live video data information, real-time monitoring Tailings Dam vibration data information, real-time monitoring tail
Waterlevel data information in mine library dam body compression data information and real-time monitoring Tailings Dam；
S102: utilizing above-mentioned data information, constructs dam body threedimensional model, carries out Tailings Dam to the data information detected
Risk is assessed；
S103: when assessment result is dangerous, alarm is timely alarmed；
S104: memory storage acquisition video, vibration, pressure, waterlevel data information and assessment result are utilized；
S105: pass through Tailings Dam video, vibration, pressure, waterlevel data information and the assessment result of display screen display acquisition.
As shown in Fig. 2, the information-based intelligence control system of security monitoring Tailings Dam provided in an embodiment of the present invention includes: view
Frequency acquisition module 1, vibration monitoring module 2, pressure monitoring module 3, water level monitoring module 4, main control module 5, dam body model construction
Module 6, risk evaluation module 7, alarm modules 8, data memory module 9, display module 10.
Video acquisition module 1 is connect with main control module 5, for acquiring Tailings Dam live video number in real time by image pick-up device
It is believed that breath；
Vibration monitoring module 2 is connect with main control module 5, for shaking number by shock sensor real-time monitoring Tailings Dam
It is believed that breath；
Pressure monitoring module 3 is connect with main control module 5, for by pressure sensor real-time monitoring tailings warehouse dam body by
Press data information；
Water level monitoring module 4 is connect with main control module 5, for passing through water level in liquid level sensor real-time monitoring Tailings Dam
Data information；
Main control module 5, with video acquisition module 1, vibration monitoring module 2, pressure monitoring module 3, water level monitoring module 4,
Main control module 5, dam body model construction module 6, risk evaluation module 7, alarm modules 8, data memory module 9, display module 10
Connection is worked normally for controlling modules by single-chip microcontroller；
Dam body model construction module 6 is connect with main control module 5, for constructing dam body threedimensional model by modeling software；
Risk evaluation module 7 is connect with main control module 5, for by assessing software according to monitoring data to Tailings Dam wind
It is dangerous to be assessed；
Alarm modules 8 are connect with main control module 5, for passing through alarm according to the progress of monitoring risk data and alarm；
Data memory module 9 is connect with main control module 5, for storing acquisition video, vibration, pressure, water by memory
Position data information；
Display module 10 is connect with main control module 5, for the Tailings Dam view by display control system interface and acquisition
Frequently, vibration, pressure, waterlevel data information.
The vibration monitoring module 2 is by shock sensor real-time monitoring Tailings Dam vibration data information, in order to induction
Signal carries out effective digital filtering with extracted valid data, needs to carry out wavelet decomposition to signals and associated noises, and then to greatest extent
The error before and after signal denoising must be reduced, specific step is as follows for wavelet function feedback:
Signal f (t) containing white Gaussian noise is
f(t)=s(t)+g(t);
S (t) is useful signal in formula, and g (t) is white Gaussian noise independent and with distribution, obeys N (0, σ2) distribution；
Step 1 carries out discrete sampling to noise-containing signal, obtains discrete signal f (n), wavelet conversion coefficient ωf
(j, k) is
K is shift factor in formula, and j is decomposition scale number；
Step 2 selects suitable db5 wavelet basis function to carry out wavelet decomposition to signals and associated noises to be treated
, j approximation component and j details coefficients under available j scale calculate the orthogonal small of noisy acoustical signal f (n)
Wave Decomposition formula is
C in formulaj-1For approximation wavelet coefficients, n is discrete sampling points, dj-1For detail wavelet coefficients, h*And g*Be one group just
The mirror filter group of friendship；
Step 3 carries out Multiscale Wavelet Decomposition to signals and associated noises, retains whole low-frequency wavelet coefficients, for high frequency system
Number sets a threshold value first, then will be less than the high-frequency wavelet coefficient amplitude whole zero setting under each scale of threshold value, finally adopt
Shrink process is carried out to remaining all coefficients higher than threshold value with different threshold function tables, the estimation under each scale can be obtained
Wavelet coefficient.
Step 4, since wavelet reconstruction is the inverse process of wavelet decomposition, so the signal estimated value after reconstruct denoising can be with
It is obtained after carrying out inverse wavelet transform processing by estimation wavelet coefficient, the wavelet coefficient of reconstruct are as follows:
When the pressure monitoring module passes through pressure sensor real-time monitoring tailings warehouse dam body compression data information, pressure is passed
Sensor be easy by temperature influenced, generate measurement error, it is small in order to reduce error, improve precision, to the temperature of pressure sensor
Degree drift has preferable compensation effect, walks using the RBF neural network algorithm based on ant colony clustering algorithm, including in detail below
It is rapid:
Step 1 clusters input sample using ant colony clustering algorithm, and obtained cluster centre is as RBF nerve net
The central value of network Hidden unit；
Step 2, with the weight of Pseudoinverse algorithm adjustment hidden layer to output layer；
Step 3, the output for calculating each Hidden unit are gone forward side by side professional etiquette generalized, judge that each Hidden unit exports network
Contribution；
Step 4 simplifies network structure with the method for reduction under conditions of meeting error.
The water level monitoring module 4 passes through waterlevel data information in liquid level sensor real-time monitoring Tailings Dam, liquid therein
Level sensor uses magnetostrictive liquid level sensor, and temperature drift phenomenon is serious in the big environment of difference variation, and generates temperature drift
The degree of many factors and temperature drift is in non-linear relation, is influenced to eliminate temperature to magnetostrictive liquid level sensor, using certainly
Neuro-fuzzy inference algorithm is adapted to, specifically:
Adaptive Neuro-fuzzy Inference is generally divided into 5 layer networks, including two input layers, two rules layers with one
Output layer.
1st layer is blurred the variable of input, and node output is
X in formula1、x2The respectively input of node；The respectively output of node.
The output of node, that is, fuzzy variable Ai、BjSubordinating degree function value, indicate node input x1、x2It is under the jurisdiction of respectively
Ai、BjDegree, application subordinating degree function use Gauss type function are as follows:
C in formulaI, 1、cJ, 1For subordinating degree function center, parameter σ is inscribed before belonging toI, 1、σJ, 1For the width of subordinating degree function, belong to
In preceding topic parameter；
The 2nd layer of variable by input is multiplied, and the output of obtained each node indicates the intensity of a rule；
The 3rd layer of normalization for rule intensity:
The output of 4th layer of calculating each fuzzy rule, the node in this layer are adaptive node, output are as follows:
P in formulai, qi, riFor consequent parameter；
5th layer is the total output for calculating all input signals, is stationary nodes, is indicated are as follows:
By to structural analysis it is found that before the system topic parameter to timing, Adaptive Neuro-fuzzy Inference network
Output can be obtained by consequent parameter linear combination
Coming topic parameter and consequent parameter before constantly correcting using hybrid learning algorithm is Adaptive Neural-fuzzy Inference system
The core concept of system.Parameter is inscribed before need being first assigned to an initial value when use, and is calculated by using Recursive Least Squares Estimation
Then method is reversely propagated forward systematic error using gradient descent method it is concluded that parameter, i.e., from the 5th layer to the 1st Es-region propagations,
To inscribe parameter before amendment.
6 construction method of dam body model construction module provided by the invention is as follows:
A, dam body model is determined according to the shape for the saturation for simulating practical dam body, position before establishing dam body model
The shape of saturation, position, divide several big sections according to shape on the saturation of dam body model；
B, big section is equally divided into three parts, using the separation of this three parts as four crucial controls on saturation
Point, wherein the highest critical control point in position is to infiltrate a little, and the model buried depth of its excess-three critical control point is respectively set as
H22、H33、H44, the horizontal distance of the slope foot of four critical control point to dam body model is respectively set as L1+3Ld、L1+2Ld、L1+
Ld、L1, the vertical range by four critical control point to dam body model bottoms is H respectively1、H2、H3、H4；
C, on the basis of step b, in dam body model, one and ground angle are assumed on the inside of big section described in step b
The theoretical impermeable stratum intersected for α, with ground, it is assumed that the critical control point and theory of the extreme lower position in four critical control point
The horizontal distance of impermeable stratum and ground intersection point is L0,
Relationship between theoretical impermeable stratum and saturation meets seepage flow fundamental equationWherein i is
The gradient of theoretical impermeable stratum, h are actual vertical between the certain point in realistic model on saturation and theory impermeable stratum
Distance；
The vertical distance of four critical control point in model to theoretical impermeable stratum (3) is set as h1、h2、h3And h4, and
And h1、h2、h3And h4Meet following relationship,
By the h in above formula1、h2、h3、h4Seepage flow fundamental equation is substituted into respectivelyAnd solve
It arrives:
By being solved to above formula, α and L are calculated0, so that it is determined that the position of theoretical impermeable stratum；
D, the position of velatively waterproof layer is calculated
Velatively waterproof layer refers to the horizontal impermeable material being laid in dam body model, and each critical control point is corresponding
One layer of velatively waterproof layer, the 1st layer of velatively waterproof layer are L apart from ground level0Tan α, the 1st layer of velatively waterproof layer outside
The distance of edge to slope foot is L1；2nd layer of velatively waterproof layer is (L apart from ground level0+Ld) tan α, the 2nd layer is relatively impermeable
The distance of water layer outer ledge to slope foot is L1+Ld；3rd layer of velatively waterproof layer is (L apart from ground level0+2Ld) tan α, the
The distance of 3 layers of velatively waterproof layer outer ledge to slope foot is L1+2Ld；4th layer of velatively waterproof layer is (L apart from ground level0
+3Ld) tan α, the distance of the 4th layer of velatively waterproof layer outer ledge to slope foot is L1+3Ld；
Step e, step b~d is repeated, the position of other big section of velatively waterproof layers is calculated；
Step f, dam body model is established, is laid with velatively waterproof layer in the corresponding position of dam body model.
7 appraisal procedure of risk evaluation module provided by the invention is as follows:
(1) Tailings Dam evaluation index hierarchical mode is initially set up；
(2) assessment marking then is carried out to each layer of index, importance of the judge index relative to upper one layer of index utilizes
Nine grades of Scale Methods establish index jdgement matrix；
(3) then using root method calculate jdgement matrix Maximum characteristic root and its corresponding characteristic vector W, normalizing
After change, relative importance weight of the as a certain level index for upper level index of correlation；
(4) again using index respective sensor data collected as the input of ambiguity function, output and index weights
Be multiplied, by bottom space index, successively up, until highest one layer i.e. evaluation goal Tailings Dam.
Tailings Dam evaluation index hierarchical mode is established in step (1) provided by the invention refers to that handle will solve the problems, such as layering
PROBLEM DECOMPOSITION is different compositing factors, according between factor according to the property of target itself and target to be achieved by seriation
Interact relationship and membership by its hierarchical cluster, form one and pass rank, orderly hierarchy Model, the structure
Model is divided into P, U, R, and K level Four, P grades are target Tailings Dam；U grades are the second level, choose underground displacement, and reservoir level etc. influences tailing
Six principal elements of library safety；R grades are chosen three different spatials for influencing underground displacement and underground inclination angle respectively
Factor；K grades are the fourth stage, will represent the sensor of different spatial as third level factor.
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (6)
1. a kind of information-based intelligent control method of security monitoring Tailings Dam, which is characterized in that the security monitoring Tailings Dam
Information-based intelligent control method includes:
The first step acquires Tailings Dam live video data information, real-time monitoring Tailings Dam vibration data information, real-time monitoring tailing
Waterlevel data information in library dam body compression data information and real-time monitoring Tailings Dam；
Second step constructs dam body threedimensional model using above-mentioned data information, carries out Tailings Dam wind to the data information detected
It is dangerous to be assessed；
Third step, when assessment result is dangerous, alarm is timely alarmed；
4th step stores acquisition video, vibration, pressure, waterlevel data information and assessment result using memory；
5th step passes through Tailings Dam video, vibration, pressure, waterlevel data information and the assessment result of display screen display acquisition.
2. the information-based intelligent control method of security monitoring Tailings Dam as described in claim 1, which is characterized in that the safety
The information-based intelligent control method of Tailings Dam is monitored by shock sensor real-time monitoring Tailings Dam vibration data information, to noisy
Signal carries out wavelet decomposition, the specific steps are as follows:
Signal f (t) containing white Gaussian noise are as follows:
F (t)=s (t)+g (t)；
S (t) is useful signal in formula, and g (t) is white Gaussian noise independent and with distribution, obeys N (0, σ2) distribution；
Step 1 carries out discrete sampling to noise-containing signal, obtains discrete signal f (n), wavelet conversion coefficient ωf(j, k)
Are as follows:
K is shift factor in formula, and j is decomposition scale number；
Step 2 selects db5 wavelet basis function to carry out wavelet decomposition to signals and associated noises to be treated, obtains j under j scale
Approximation component and j details coefficients calculate the orthogonal wavelet decomposition formula of noisy acoustical signal f (n) are as follows:
C in formulaj-1For approximation wavelet coefficients, n is discrete sampling points, dj-1For detail wavelet coefficients, h*And g*It is one group orthogonal
Mirror filter group；
Step 3 carries out Multiscale Wavelet Decomposition to signals and associated noises, retains whole low-frequency wavelet coefficients, for high frequency coefficient,
A threshold value is set first, then be will be less than the high-frequency wavelet coefficient amplitude whole zero setting under each scale of threshold value, is finally used
Different threshold function tables carries out shrink process to remaining all coefficients higher than threshold value, obtains the estimation wavelet systems under each scale
Number；
Step 4, since wavelet reconstruction is the inverse process of wavelet decomposition, the signal estimated value after reconstruct denoising passes through estimation small echo
Coefficient obtains after carrying out inverse wavelet transform processing, the wavelet coefficient of reconstruct are as follows:
3. the information-based intelligent control method of security monitoring Tailings Dam as described in claim 1, which is characterized in that pass through pressure
Sensor real-time monitoring tailings warehouse dam body compression data information, the temperature drift of pressure sensor have preferable compensation effect, adopt
With the RBF neural network algorithm based on ant colony clustering algorithm, comprising the following specific steps
Step 1 clusters input sample using ant colony clustering algorithm, and obtained cluster centre is hidden as RBF neural
The central value of layer unit；
Step 2, with the weight of Pseudoinverse algorithm adjustment hidden layer to output layer；
Step 3, the output for calculating each Hidden unit are gone forward side by side professional etiquette generalized, judge the tribute that each Hidden unit exports network
Offer size；
Step 4 simplifies network structure with the method for reduction under conditions of meeting error.
4. a kind of security monitoring Tailings Dam for the information-based intelligent control method for implementing security monitoring Tailings Dam described in claim 1
Information-based intelligence control system, which is characterized in that the information-based intelligence control system of the security monitoring Tailings Dam includes:
Video acquisition module is connect with main control module, for acquiring Tailings Dam live video data information in real time by image pick-up device；
Vibration monitoring module, connect with main control module, for passing through shock sensor real-time monitoring Tailings Dam vibration data information；
Pressure monitoring module is connect with main control module, for passing through pressure sensor real-time monitoring tailings warehouse dam body compression data
Information；
Water level monitoring module, connect with main control module, for passing through waterlevel data letter in liquid level sensor real-time monitoring Tailings Dam
Breath；
Main control module, with video acquisition module, vibration monitoring module, pressure monitoring module, water level monitoring module, main control module,
Dam body model construction module, risk evaluation module, alarm modules, data memory module, display module connection, for passing through monolithic
Machine controls modules and works normally；
Dam body model construction module, connect with main control module, for constructing dam body threedimensional model by modeling software；
Risk evaluation module is connect with main control module, for by assessment software according to monitoring data to Tailings Dam risk into
Row assessment；
Alarm modules are connect with main control module, for passing through alarm according to the progress of monitoring risk data and alarm；
Data memory module is connect with main control module, for storing acquisition video, vibration, pressure, waterlevel data by memory
Information；
Display module is connect with main control module, for the Tailings Dam video by display control system interface and acquisition, shake
Dynamic, pressure, waterlevel data information.
5. a kind of tail of the information-based intelligent control method using security monitoring Tailings Dam described in claims 1 to 3 any one
Mine library management terminal.
6. a kind of meter of the information-based intelligent control method using security monitoring Tailings Dam described in claims 1 to 3 any one
Calculation machine.
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