CN105488307B - Slope monitoring and early warning system evaluation method based on the Big Dipper - Google Patents
Slope monitoring and early warning system evaluation method based on the Big Dipper Download PDFInfo
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- CN105488307B CN105488307B CN201610023372.5A CN201610023372A CN105488307B CN 105488307 B CN105488307 B CN 105488307B CN 201610023372 A CN201610023372 A CN 201610023372A CN 105488307 B CN105488307 B CN 105488307B
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Abstract
The slope monitoring and early warning system evaluation method based on the Big Dipper that the invention discloses a kind of, it is related to engineering monitoring technical field;Its evaluation method is:Step 1:The monitoring data such as the displacement in side slope continuous time section are obtained by Big Dipper measurement module, and it is pre-processed, comply with system structure demand;Step 2:Graphical display, parameter setting, text output, data update, preservation, modification are carried out by data management module;Step 3: according to obtained Big Dipper displacement monitoring data are continuously measured, grey differential prediction model is established;Step 4: Slope Stability Evaluation early warning;Energy side slope monitoring of the invention is estimated and is predicted, to judge that the tendency in side slope future provides foundation and reference, the limitation of Classical forecast model is overcome to a certain extent, help to reduce the randomness of time series and improves precision of prediction, and grey forecasting model has required sample few in terms of monitoring and forecasting, requires sample not stringent.
Description
Technical field:
The slope monitoring and early warning system evaluation method based on the Big Dipper that the present invention relates to a kind of belonging to engineering monitoring technology neck
Domain.
Background technology:
Currently, Slope Monitoring method has macroscopical geology observation method, artificial observation method, sets station observation method, instrument observation
Method.Current monitoring method is varied, but monitoring data format differs widely, and can not achieve the shared of data;Monitoring calculation
There are many model, but the precision of result of calculation is not mostly high, can not side slope disaster accurately predicted.
Invention content:
In view of the above-mentioned problems, the technical problem to be solved in the present invention is to provide a kind of slope monitoring early warning system based on the Big Dipper
System evaluation method.
A kind of slope monitoring and early warning system evaluation method based on the Big Dipper of the present invention, its evaluation method are:
Step 1:Obtain the monitoring data such as displacement in side slope continuous time section by Big Dipper measurement module, and to its into
Row pretreatment complies with system structure demand;
Step 2:By data management module carry out graphical display, parameter setting, text output, data update, preservation,
Modification;
Step 3: according to obtained Big Dipper displacement monitoring data are continuously measured, grey differential prediction model is established:
(3.1), the foundation of GM (1.1) model:
(3.1.1), the new sequence of Accumulating generation:Original series X(0)={ X(0)(1),X(0)(2),…,X(0)(n) } there is n sight
Value is examined, second data of original series are added in first data of original series by first data of original series,
And it is as second data for generating row, and the third data of original series are added in second data for generating row,
With as the third data for generating row, pass through the new sequence X of Accumulating generation by this rule(1)={ X(1)(1),X(1)(2),…,X(1)
(n) }, then the corresponding differential equation of GM (1.1) model is:
Wherein:A is known as developing grey number;μ is known as the interior grey number of raw control;
(3.1.2), structural matrix B and vector Y:
Vectorial Y=(X(0)(2),X(0)(3),…,X(0)(n));
Solve the differential equation, you can obtain prediction model:
(3.1.4), forecasting sequence regressive is restored:Regressive reduction is done to it, you can obtain the gray prediction of original data series
Model is:
In formula:The size of a basic control system developing states, that is, reflect the developing state of prediction, wherein:
1., as-a<When 0.3, GM (1.1) model can be used for medium- and long-term forecasting;
2., when 0.3<-a<When 0.5, GM (1.1) model can be used for short-term forecast, and medium- and long-term forecasting uses with caution;
3., when 0.5<-a<When 1, GM (1.1) improved model, including GM (1.1) Residual Error Modified Model should be used;
4., as-a>When 1, GM (1,1) model should not be used;
(3.2), model testing:Model testing has residual test, degree of association inspection and posterior difference examination;
(3.2.1), residual test:
It is calculated by prediction modelAnd it willInverse accumulated generatingThen original series X is calculated(0)(i)
WithAbsolute error sequence and relative error sequence;After establishing model, accuracy test is carried out to model;
(3.2.2), the degree of association are examined:
Correlation analysis is the method for each correlate degree in analysis system, needs first to calculate pass before calculating correlation
Contact number;
Incidence coefficient is defined as:
In formula:For k-th of point X(0)WithAbsolute error;
For two-stage lowest difference;
For two-stage maximum difference;
ρ is known as resolution ratio, and 0<ρ<1, if ρ is smaller, difference is bigger between incidence coefficient, and separating capacity is stronger;It is general take ρ=
0.5;Differ to unit, the different sequence of initial value should be initialized before calculating related coefficient, i.e., owned the sequence first
Data difference divided by first data;
(3.2.3), posterior difference examination:
(a) calculates original series standard deviation:
(b) calculates the standard deviation of absolute error sequence:
(c) calculates posteriority difference ratio:
Step 4: Slope Stability Evaluation early warning:
(4.1), when grey forecasting model where applicable, the result of monitoring data is analyzed using grey forecasting model,
Fuzzy long-term description is made to the changing rule of measurement point displacement, judges the change in displacement trend of side slope, and sets displacement change
The secure threshold of change then starts alert program more than secure threshold;
(4.2), when grey forecasting model should not use, the current stability of fuzzy comprehensive evaluation method side slope can be passed through
Do qualitative evaluation;Select opinion rating for:Stablize, is relatively stable, more unstable and unstable;Evaluation points are:Cohesive strength interior is rubbed
Wipe angle, slope angle, slope height, maximum displacement speed.
Preferably, the specific evaluation method in (4.2) of the step four:
The calculating of (4.2.1), degree of membership:
The degree of membership that each evaluation points x Slope Stabilities influence is calculated using trapezoidal function, calculation formula is as follows:
In formula, Y1,Y2,Y3,Y4Respectively evaluation points are for the degree of membership of 4 danger classes, S1,S2,S3,S4It is corresponding
In the classification thresholds of evaluation points.According to the input value and membership function of evaluation points, it can be found out corresponding to each grade
Degree of membership, and then obtain fuzzy relationship matrix r;
The calculating of (4.2.2), weight:Power method calculates the weight of each factor, as the following formula:
Wi=Ci/Si
Wherein:WiFor the weight of each evaluation points;CiFor the input value of i-th kind of factor;SiFor i-th kind of factor standard at different levels
Average value, Si=1/4 (Si1+Si2+Si3+Si4).Calculated weighted value WiNormalized is done, i.e.,:
It can finally obtain weight matrix A=(Wi1,Wi2,Wi3,Wi4);Where evaluation result is by the determination of maximum membership degree rule, i.e.,
The degree of membership of one grade is maximum, then degree of danger belongs to the grade, starts the alarm system of appropriate level according to grade.
Beneficial effects of the present invention are:By quickly establishing GM (1.1) prediction model, side slope monitoring carries out estimation and pre-
It surveys, to judge that the tendency in side slope future provides foundation and reference, overcomes the limitation of Classical forecast model to a certain extent,
Help to reduce the randomness of time series and improve precision of prediction, and grey forecasting model has institute in terms of monitoring and forecasting
It needs sample few, requires sample not stringent feature.
Specific implementation mode:
Present embodiment uses following technical scheme:Its evaluation method is:
Step 1:Obtain the monitoring data such as displacement in side slope continuous time section by Big Dipper measurement module, and to its into
Row pretreatment complies with system structure demand;
Step 2:By data management module carry out graphical display, parameter setting, text output, data update, preservation,
Modification;
Step 3: according to obtained Big Dipper displacement monitoring data are continuously measured, grey differential prediction model is established:
(3.1), the foundation of GM (1.1) model:
(3.1.1), the new sequence of Accumulating generation:Original series X(0)={ X(0)(1),X(0)(2),…,X(0)(n) } there is n sight
Value is examined, second data of original series are added in first data of original series by first data of original series,
And it is as second data for generating row, and the third data of original series are added in second data for generating row,
With as the third data for generating row, pass through the new sequence X of Accumulating generation by this rule(1)={ X(1)(1),X(1)(2),…,X(1)
(n) }, then the corresponding differential equation of GM (1.1) model is:
Wherein:A is known as developing grey number;μ is known as the interior grey number of raw control;
(3.1.2), structural matrix B and vector Y:
Vectorial Y=(X(0)(2),X(0)(3),…,X(0)(n));
Solve the differential equation, you can obtain prediction model:
(3.1.4), forecasting sequence regressive is restored:Regressive reduction is done to it, you can obtain the gray prediction of original data series
Model is:
In formula:The size of a basic control system developing states, that is, reflect the developing state of prediction, wherein:
1., as-a<When 0.3, GM (1.1) model can be used for medium- and long-term forecasting;
2., when 0.3<-a<When 0.5, GM (1.1) model can be used for short-term forecast, and medium- and long-term forecasting uses with caution;
3., when 0.5<-a<When 1, GM (1.1) improved model, including GM (1.1) Residual Error Modified Model should be used;
4., as-a>When 1, GM (1,1) model should not be used, it is contemplated that other prediction techniques;
(3.2), model testing:Model testing has residual test, degree of association inspection and posterior difference examination;
(3.2.1), residual test:
It is calculated by prediction modelAnd it willInverse accumulated generatingThen original series X is calculated(0)(i) withAbsolute error sequence and relative error sequence;
Wherein Δ(0)(i) it is absolute error, φ (i) is relative error;
After establishing model, it is necessary to carry out accuracy test to model, test stone is shown in Table 1.
1 accuracy test grade of table is with reference to table
(3.2.2), the degree of association are examined:
Correlation analysis is the method for each correlate degree in analysis system, needs first to calculate pass before calculating correlation
Contact number;
Incidence coefficient is defined as:
In formula:For k-th of point X(0)WithAbsolute error;
For two-stage lowest difference;
For two-stage maximum difference;
ρ is known as resolution ratio, and 0<ρ<1, if ρ is smaller, difference is bigger between incidence coefficient, and separating capacity is stronger;It is general take ρ=
0.5;Differ to unit, the different sequence of initial value should be initialized before calculating related coefficient, i.e., owned the sequence first
Data difference divided by first data.
The degree of associationRule of thumb when ρ=0.5, it is just satisfied that the degree of association is more than 0.6.
(3.2.3), posterior difference examination:
(a) calculates original series standard deviation:
(b) calculates the standard deviation of absolute error sequence:
(c) calculates posteriority difference ratio:
2 forecast test grade of table is with reference to table
Step 4: Slope Stability Evaluation early warning:
(4.1), when grey forecasting model where applicable, the result of monitoring data is analyzed using grey forecasting model,
Fuzzy long-term description is made to the changing rule of measurement point displacement, judges the change in displacement trend of side slope, and sets displacement change
The secure threshold of change then starts alert program more than secure threshold;
(4.2), when grey forecasting model should not use, the current stability of fuzzy comprehensive evaluation method side slope can be passed through
Do qualitative evaluation;Select opinion rating for:Stablize, is relatively stable, more unstable and unstable;Evaluation points are:Cohesive strength interior is rubbed
Wipe angle, slope angle, slope height, maximum displacement speed.
Specific evaluation method:
The calculating of (4.2.1), degree of membership:
The degree of membership that each evaluation points x Slope Stabilities influence is calculated using trapezoidal function, calculation formula is as follows:
In formula, Y1,Y2,Y3,Y4Respectively evaluation points are for the degree of membership of 4 danger classes, S1,S2,S3,S4It is corresponding
In the classification thresholds of evaluation points.According to the input value and membership function of evaluation points, it can be found out corresponding to each grade
Degree of membership, and then obtain fuzzy relationship matrix r.
The calculating of (4.2.2), weight:Power method calculates the weight of each factor, as the following formula:
Wi=Ci/Si
Wherein:WiFor the weight of each evaluation points;CiFor the input value of i-th kind of factor;SiFor i-th kind of factor standard at different levels
Average value, Si=1/4 (Si1+Si2+Si3+Si4).Calculated weighted value WiNormalized is done, i.e.,:
It can finally obtain weight matrix A=(Wi1,Wi2,Wi3,Wi4);Where evaluation result is by the determination of maximum membership degree rule, i.e.,
The degree of membership of one grade is maximum, then degree of danger belongs to the grade, starts the alarm system of appropriate level according to grade.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (1)
1. the slope monitoring and early warning system evaluation method based on the Big Dipper, it is characterised in that:Its evaluation method is:
Step 1:The displacement monitoring data in side slope continuous time section are obtained by Big Dipper measurement module, and it is located in advance
Reason complies with system structure demand;
Step 2:Graphical display, parameter setting, text output, data update, preservation, modification are carried out by data management module;
Step 3: according to obtained Big Dipper displacement monitoring data are continuously measured, grey differential prediction model is established:
(3.1), the foundation of GM (1.1) model:
(3.1.1), the new sequence of Accumulating generation:Original series X(0)={ X(0)(1),X(0)(2),K,X(0)(n) } there is n observed value,
By first data of original series, second data of original series are added in first data of original series, and
As second data for generating row, the third data of original series are added in second data for generating row, and are made
The third data made a living in column pass through the new sequence X of Accumulating generation by this rule(1)={ X(1)(1),X(1)(2),K,X(1)(n) },
Then the corresponding differential equation of GM (1.1) model is:
Wherein:A is known as developing grey number;μ is known as the interior grey number of raw control;
(3.1.2), structural matrix B and vector Y:
Matrix
Vectorial Y=(X(0)(2),X(0)(3),K,X(0)(n));
(3.1.3), the solution differential equation, obtains prediction model:IfFor parameter vector to be estimated,Then the differential equation is represented byIt can be obtained using least square method:
Solve the differential equation, you can obtain prediction model:
(3.1.4), forecasting sequence regressive is restored:Regressive reduction is done to it, you can obtain the grey forecasting model of original data series
For:
In formula:The size of a basic control system developing states, that is, reflect the developing state of prediction, wherein:
1., as-a<When 0.3, GM (1.1) model can be used for medium- and long-term forecasting;
2., when 0.3<-a<When 0.5, GM (1.1) model can be used for short-term forecast, and medium- and long-term forecasting uses with caution;
3., when 0.5<-a<When 1, GM (1.1) improved model, including GM (1.1) Residual Error Modified Model should be used;
4., as-a>When 1, GM (1.1) model should not be used;
(3.2), model testing:Model testing has residual test, degree of association inspection and posterior difference examination;
(3.2.1), residual test:
It is calculated by prediction modelAnd it willInverse accumulated generatingThen original series X is calculated(0)(i) withAbsolute error sequence and relative error sequence;After establishing model, accuracy test is carried out to model;
(3.2.2), the degree of association are examined:
Correlation analysis is the method for each correlate degree in analysis system, needs first to calculate association system before calculating correlation
Number;
Incidence coefficient is defined as:
In formula:For k-th of point X(0)WithAbsolute error;
For two-stage lowest difference;
For two-stage maximum difference;
ρ is known as resolution ratio, and 0<ρ<1, if ρ is smaller, difference is bigger between incidence coefficient, and separating capacity is stronger;Take ρ=0.5;To unit
Differ, the different sequence of initial value should be initialized first before calculating related coefficient, i.e., removes all data of the sequence respectively
With first data;
The degree of association
(3.2.3), posterior difference examination:
(a) calculates original series standard deviation:
(b) calculates the standard deviation of absolute error sequence:
(c) calculates posteriority difference ratio:
Step 4: Slope Stability Evaluation early warning:
(4.1), when grey forecasting model where applicable, the result of monitoring data is analyzed using grey forecasting model, to surveying
The changing rule of amount point displacement makes fuzzy long-term description, judges the change in displacement trend of side slope, and set change in displacement
Secure threshold then starts alert program more than secure threshold;
(4.2), when grey forecasting model should not use, can be determined by the current stability of fuzzy comprehensive evaluation method side slope
Property evaluation;Select opinion rating for:Stablize, is relatively stable, more unstable and unstable;Evaluation points are:Cohesive strength, interior friction
Angle, slope angle, slope height, maximum displacement speed;
Specific evaluation method in (4.2) of the step four:
The calculating of (4.2.1), degree of membership:
The degree of membership that each evaluation points x Slope Stabilities influence is calculated using trapezoidal function, calculation formula is as follows:
In formula, Y1,Y2,Y3,Y4Respectively evaluation points are for the degree of membership of 4 danger classes, S1,S2,S3,S4It is commented to correspond to
The classification thresholds of the valence factor;According to the input value and membership function of evaluation points, its being subordinate to corresponding to each grade can be found out
Degree, and then obtain fuzzy relationship matrix r;
The calculating of (4.2.2), weight:Power method calculates the weight of each factor, as the following formula:
Wi=Ci/Si
Wherein:WiFor the weight of each evaluation points;CiFor the input value of i-th kind of factor;SiFor the flat of i-th kind of factor standard at different levels
Mean value, Si=1/4 (Si1+Si2+Si3+Si4);Calculated weighted value WiNormalized is done, i.e.,:
It can finally obtain weight matrix A=(Wi1,Wi2,Wi3,Wi4);Evaluation result is by the determination of maximum membership degree rule, i.e. which etc.
The degree of membership of grade is maximum, then degree of danger belongs to the grade, starts the alarm system of appropriate level according to grade.
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CN107229768B (en) * | 2017-04-12 | 2019-04-02 | 中国地质大学(武汉) | Slopereliability parameter acquiring method and device based on fuzzy classification technology |
CN107562545A (en) * | 2017-09-11 | 2018-01-09 | 南京奥之云信息技术有限公司 | A kind of container dispatching method based on Docker technologies |
CN108776715B (en) * | 2018-04-16 | 2022-04-08 | 浙江大学 | Static design safety coefficient value taking method for large-scale surface mine slope evaluation period |
CN108519045A (en) * | 2018-05-14 | 2018-09-11 | 桂林电子科技大学 | A kind of Big Dipper precision deformation monitoring and early warning system |
CN111415492B (en) * | 2020-04-29 | 2021-03-16 | 中国水利水电科学研究院 | Slope landslide early warning method and system based on fuzzy comprehensive evaluation algorithm |
CN115116202B (en) * | 2022-08-29 | 2022-11-15 | 西南交通大学 | Landslide disaster early warning method, device, equipment and readable storage medium |
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