CN114861462A - Natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method - Google Patents
Natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method Download PDFInfo
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Abstract
The utility model provides a natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method, which belongs to the field of natural gas pipeline geological disaster prediction prevention and control, and comprises the following steps: acquiring the monitoring data of the accumulated deformation of the natural gas pipeline landslide, and preprocessing the monitoring data sequence and analyzing the trend of the deformation curve; establishing a gray differential equation of a gray theoretical model GM (1,1), obtaining a time response sequence equation of the monitoring data of the natural gas pipeline landslide accumulated deformation, and carrying out residual error detection on the gray model GM (1, 1); outputting a predicted value of a gray model GM (1,1), giving weights to each point of a single prediction model according to the sequence of high and low precision, solving the minimum value reached by the sum of squares of the inverses of errors of predicted data in the nth period, establishing a dynamic gray deformation model based on an Induced Order Weighted Harmonic (IOWHA) operator, predicting the deformation of the natural gas pipeline landslide on the basis of the trend analysis of the accumulated deformation curve of the landslide, and achieving the effect of efficiently and scientifically improving the prediction precision of the natural gas pipeline landslide deformation.
Description
Technical Field
The invention relates to the field of natural gas pipeline geological disaster prediction and prevention, in particular to a natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method.
Background
At present, methods for analyzing and predicting natural gas pipeline landslide accumulated deformation curves and deformation trends are developed from qualitative analysis of each stage of pipeline landslide deformation analysis according to displacement-time curves to quantitative intelligent prediction and analysis adopting mathematical models, and intelligent mathematical models are gradually increased, such as grey prediction models, time sequence prediction models, genetic algorithm prediction models, BP neural network prediction models and the like.
However, most of intelligent mathematical models are mainly single prediction models, certain limitations exist, the calculation process and steps are complicated and complex, and the particularity of natural gas pipeline landslide cannot be reflected, so that the limitation of the single prediction models can be made up, and the prediction precision and the calculation efficiency can be improved by applying the combined prediction method.
Disclosure of Invention
The method aims to solve the problems that in the prior art, a single prediction model is limited, uncertain factors of a grey prediction theory are large and the like, an Induced Order Weighted Harmonic (IOWHA) operator is introduced, a dynamic grey time series natural gas pipeline landslide deformation combined prediction model based on the Induced Order Weighted Harmonic (IOWHA) operator is built, the prediction precision of natural gas pipeline landslide deformation is effectively improved, and technical support is provided for natural gas pipeline landslide deformation prediction and disaster prevention.
In order to achieve the above purpose, the present disclosure is achieved by the following technical solutions: the method for analyzing the natural gas pipeline landslide accumulated deformation curve and predicting the landslide mainly comprises the following steps of:
the method comprises the following steps: acquiring landslide deformation monitoring data of the natural gas pipeline, and preprocessing a monitoring data sequence and analyzing an accumulated deformation curve;
step two: establishing a gray differential equation of a gray theoretical model GM (1,1), obtaining a time response sequence equation of the landslide deformation monitoring data of the natural gas pipeline, and carrying out residual error detection on the gray model GM (1, 1);
step three: outputting a predicted value of a gray model GM (1,1), introducing an Induced Order Weighted Harmonic (IOWHA) operator, and giving a weight to each point of the single prediction model according to the high and low precision sequence;
step four: solving a minimum value reached by the sum of squares of the inverses of errors of the predicted data in the nth period, establishing a dynamic gray deformation model based on an Induced Order Weighted Harmonic (IOWHA) operator, and predicting the deformation of the natural gas pipeline landslide;
preferably, in the first step, a series of random original data obtained by deformation monitoring is acquired by arranging a natural gas pipeline landslide surface layer deformation monitoring sensor;
preferably, the monitored natural gas pipeline landslide deformation data sequence is preprocessed according to formulas (1) and (2), and the preprocessing is specifically implemented according to the following method:
the data original sequence of the landslide deformation monitoring of the natural gas pipeline is assumed as follows:
X (0) ＝{x (0) (1),x (0) (2),...,x (0) (n)} (1)
the formula 1 is generated by accumulation, and the sequence of the monitoring data is preprocessed to obtain:
X (1) ＝{x (1) (1),x (1) (2),...,x (1) (n)} (2)
in the above formula, x (0) is an original numerical value for monitoring landslide deformation of the natural gas pipeline, and x (1) is a data sequence generated after primary accumulation;
preferably, the second step is as follows: firstly, aiming at an ash number sequence generated after pretreatment of natural gas pipeline landslide deformation original data, establishing a grey differential equation of a single grey theoretical model GM (1, 1); then whitening the gray differential equation to obtain a time response sequence equation of the color differential equation; further carrying out one-to-one residual error detection on the grey theoretical model predicted value and the natural gas pipeline landslide deformation field actual monitoring value, and concretely implementing the following steps:
assuming that Z (1) is the gray number sequence generated by X (1) after pretreatment:
in the above formula: k is a natural number 1,2, 3 … …
Establishing a gray differential equation of a single gray theoretical model GM (1,1) according to a gray number sequence after pretreatment of the landslide deformation monitoring data of the natural gas pipeline:
x (0) (k)+az (1) (k)＝b (4)
in the above formula: and a is the development coefficient of the natural gas pipeline landslide deformation monitoring gray prediction model, and b is the acting dependent variable of the natural gas pipeline landslide deformation monitoring gray prediction model.
Whiten the gray differential equation due to
And let x (1) (0)＝x (0) (1) And obtaining a time response sequence equation of a gray differential equation of the natural gas pipeline landslide deformation prediction:
carrying out one-to-one residual error detection on the difference between the grey theoretical model predicted value and the field actual monitoring value of the landslide deformation of the natural gas pipeline, and calculating the original monitoring data x of the landslide deformation of the pipeline (0) (i) And
absolute residual of (d):
preferably, the third step is as follows: by predicting a grey theoretical GM (1,1) model of natural gas pipeline landslide deformation, introducing an Induced Order Weighted Harmonic (IOWHA) operator, and endowing each point of the single grey theoretical GM (1,1) model with a weight according to a precision sequence, the specific implementation is as follows:
at a certain moment, the natural gas pipeline landslide deformation prediction based on the IOWHA operator combined model is as follows:
in the above equation, xit (i ═ 1,2) is the i-th prediction model, Pit represents the prediction accuracy value of the i-th prediction model at time t, and p-index (it) is a subscript of a value having a large prediction accuracy of the single prediction method.
At time t, the method with higher prediction accuracy gives a larger weight coefficient. Suppose that
e a-index(it) ＝1/x t -1/x p-index(it) (8)
In the above formula, a is a noise sequence;
preferably, the fourth step is implemented as follows:
according to the IOWHA combined prediction theory, solving the minimum value of the sum of squares of the inverses of the errors of the prediction data in the nth stage of the landslide deformation of the natural gas pipeline:
in the above formula, x ^ T is the weighted harmonic mean value at time T, and l ═ l1, l2) T is the combined prediction weighting coefficient;
establishing a dynamic grey time series oil and gas pipeline landslide deformation prediction model based on an Induced Order Weighted Harmonic (IOWHA) operator as follows:
and predicting the landslide deformation according to the natural gas pipeline landslide deformation data obtained by monitoring according to the prediction model established by the above formula.
In conclusion, the beneficial technical effects of the invention are as follows:
aiming at the difficulty that the single gray model GM (1,1) prediction method considers the geological complexity of the natural gas pipeline landslide and the high consequence of disaster occurrence at the same time, an Induced Order Weighted Harmonic (IOWHA) operator is introduced, the landslide predicted values of the single method are combined in a weighted average mode, an IOWHA combined prediction model is established, the dynamic prediction of the natural gas pipeline landslide deformation can be realized, and the prediction precision of the natural gas pipeline landslide deformation can be effectively, efficiently and scientifically improved.
Drawings
FIG. 1 is a flow chart of a combined prediction method for landslide deformation of a natural gas pipeline according to an embodiment of the disclosure;
FIG. 2 is a graph comparing single predictive models, combined predictive models, and actual monitored values in an implementation of the present disclosure;
fig. 3 is a diagram of an error process in a specific embodiment of the present disclosure.
Detailed Description
The technical solution in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
A natural gas pipeline landslide deformation combined prediction method comprises the following steps:
the method comprises the following steps: laying a deformation monitoring sensor on a natural gas pipeline landslide, acquiring monitoring original data in a certain period, and preprocessing a monitoring data sequence:
the method comprises the following steps of monitoring and preprocessing an obtained natural gas pipeline landslide deformation data sequence according to formulas (1) and (2), and specifically according to the following method:
the data original sequence of the landslide deformation monitoring of the natural gas pipeline is assumed as follows:
X (0) ＝{x (0) (1),x (0) (2),...,x (0) (n)} (1)
the formula 1 is generated by accumulation, and the sequence of the monitoring data is preprocessed to obtain:
X (1) ＝{x (1) (1),x (1) (2),...,x (1) (n)} (2)
in the above formula, x (0) is an original numerical value for monitoring landslide deformation of the natural gas pipeline, and x (1) is a data sequence generated after one-time accumulation.
Step two: establishing a gray differential equation of a gray theoretical model GM (1,1), obtaining a time response sequence equation of the landslide deformation monitoring data of the natural gas pipeline, and carrying out residual error detection on the gray model GM (1, 1);
aiming at an ash number sequence generated after preprocessing of the natural gas pipeline landslide deformation original data, establishing a grey differential equation of a single grey theoretical model GM (1, 1); then whitening the gray differential equation to obtain a time response sequence equation of the color differential equation; further carrying out one-to-one residual error detection on the grey theoretical model predicted value and the natural gas pipeline landslide deformation field actual monitoring value, and concretely implementing the following steps:
assuming that Z (1) is the gray number sequence generated by X (1) after pretreatment:
in the above formula: k is a natural number 1,2, 3 … …
Establishing a gray differential equation of a single gray theoretical model GM (1,1) according to a gray number sequence after pretreatment of the landslide deformation monitoring data of the natural gas pipeline:
x (0) (k)+az (1) (k)＝b (4)
in the above formula: and a is the development coefficient of the natural gas pipeline landslide deformation monitoring gray prediction model, and b is the acting dependent variable of the natural gas pipeline landslide deformation monitoring gray prediction model.
Whiten the gray differential equation due to
And let x (1) (0)＝x (0) (1) And obtaining a time response sequence equation of a gray differential equation of the natural gas pipeline landslide deformation prediction:
carrying out one-to-one residual error detection on the difference between the grey theoretical model predicted value and the field actual monitoring value of the landslide deformation of the natural gas pipeline, and calculating the original monitoring data x of the landslide deformation of the pipeline (0) (i) And
absolute residual of (d):
step three: outputting a predicted value of a gray model GM (1,1), introducing an Induced Order Weighted Harmonic (IOWHA) operator, and giving a weight to each point of the single prediction model according to the high and low precision sequence;
by predicting a grey theoretical GM (1,1) model of natural gas pipeline landslide deformation, introducing an Induced Order Weighted Harmonic (IOWHA) operator, and endowing each point of the single grey theoretical GM (1,1) model with a weight according to a precision sequence, the specific implementation is as follows:
at a certain moment, the natural gas pipeline landslide deformation prediction based on the IOWHA operator combined model is as follows:
in the above equation, xit (i ═ 1,2) is the i-th prediction model, Pit represents the prediction accuracy value of the i-th prediction model at time t, and p-index (it) is a subscript of a value having a large prediction accuracy of the single prediction method.
At time t, the method with higher prediction accuracy gives a larger weight coefficient. Suppose that
e a-index(it) ＝1/x t -1/x p-index(it) (8)
In the above equation, a is a noise sequence.
Step four: solving a minimum value reached by the sum of squares of the inverses of errors of the predicted data in the nth period, establishing a dynamic gray deformation model based on an Induced Order Weighted Harmonic (IOWHA) operator, and predicting the deformation of the natural gas pipeline landslide;
according to the IOWHA combined prediction theory, solving the minimum value of the sum of squares of the inverses of the errors of the prediction data in the nth stage of the landslide deformation of the natural gas pipeline:
in the above formula, x ^ T is the weighted harmonic mean value at time T, and l ^ (l1, l2) T is the combined prediction weighting coefficient.
Establishing a dynamic grey time series oil and gas pipeline landslide deformation prediction model based on an Induced Order Weighted Harmonic (IOWHA) operator as follows:
and predicting the landslide deformation according to the natural gas pipeline landslide deformation data obtained by monitoring according to the prediction model established by the above formula.
And selecting error evaluation indexes according to the prediction result, and establishing a prediction error system and quantitatively analyzing the prediction accuracy according to the table 1.
TABLE 1 error evaluation Table
The method for predicting landslide deformation combination of the natural gas pipeline provided by the disclosure includes the following steps:
example one
By using the method provided by the disclosure, an example analysis of the combined deformation prediction is carried out on the down-feather landslide of the dragon boat lawn along a certain natural gas pipeline.
The method comprises the steps of rolling down a landslide of the Liujia in Yichang city on a dragon boat terrace in town of Liujia three groups, wherein the slope is a soil side slope (graved soil), the predicted volume is 1000m3, a pipeline is positioned below the slope and laid through the slope, the direction of the pipeline is 170 degrees, the burial depth is 2.0m, a grouted block stone retaining wall is built at the bottom, local cracks are formed, the cracks are in a splayed shape, a fourth system covering layer is covered on the slope and is thicker and 3-8m thick, a fourth system residual slope layer (Q) with a loose and soft surface layer is mainly formed by tawny clay, powdery clay and broken blocks, and then manually accumulated bodies are mainly distributed on the slope surface, the soil is loose and soft, water drainage is not smooth, the landslide is easy to deform due to water saturation, the landslide is easy to form geological disasters when the rainfall is strong, a distributed large-range potential deformation displacement meter is installed on the landslide in 2019, the surface layer deformation of the pipeline landslide is monitored, and the surface deformation of the landslide is selected as an original sample, data of the monitoring data of 1 month between 8 days in 2019 and 6 days in 2019 and 7 months in 7 days in 2019, as shown in table 2, the deformation of 2019, 9, 7 and later is selected for prediction, the predicted value and the prediction error of the landslide deformation are respectively shown in tables 3 and 4, the comparison between the single prediction model, the combined prediction model and the actual monitoring value is shown in fig. 2, and the absolute error process is shown in fig. 3.
TABLE 2 original sample data selected
TABLE 3 measured and predicted values of landslide deformation
TABLE 4 landslide deformation prediction error
As can be seen from table 4, the maximum absolute error of the prediction of the combined prediction model established herein is 0.54, and the maximum absolute error of the prediction of the single gray theoretical GM (1,1) model is 0.72, which is obvious that the prediction error established herein is relatively small and closer to the actual monitored value.
It will be appreciated that modifications may be made by those skilled in the art in light of the above teachings and all such modifications are intended to fall within the scope of the appended claims.
Claims (5)
1. A natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: acquiring the monitoring data of the accumulated deformation of the natural gas pipeline landslide, and preprocessing the monitoring data sequence and analyzing the trend of the deformation curve;
step two: establishing a gray differential equation of a gray theoretical model GM (1,1), obtaining a time response sequence equation of the landslide deformation monitoring data of the natural gas pipeline, and carrying out residual error detection on the gray model GM (1, 1);
step three: outputting a predicted value of a gray model GM (1,1), introducing an Induced Order Weighted Harmonic (IOWHA) operator, and giving a weight to each point of the single prediction model according to the high and low precision sequence;
step four: and solving a minimum value reached by the sum of squares of the inverses of errors of the predicted data in the nth period, establishing a dynamic gray deformation model based on an Induced Order Weighted Harmonic (IOWHA) operator, and predicting the deformation of the natural gas pipeline landslide on the basis of the trend analysis of the accumulated deformation curve of the landslide.
2. The natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method of claim 1, wherein: the first step is to acquire a series of random original data obtained by deformation monitoring from a natural gas pipeline landslide surface layer deformation monitoring sensor, preprocess a monitored natural gas pipeline landslide deformation data sequence and analyze the trend of a deformation curve according to formulas (1) and (2), and specifically implement the following method:
the data original sequence of the landslide deformation monitoring of the natural gas pipeline is assumed as follows:
X (0) ＝{x (0) (1),x (0) (2),...,x (0) (n)} (1)
the formula 1 is generated by accumulation, and the sequence of the monitoring data is preprocessed to obtain:
X (1) ＝{x (1) (1),x (1) (2),...,x (1) (n)} (2)
in the above formula, x (0) Is an original numerical value, x, of the natural gas pipeline landslide deformation monitoring (1) Is a data sequence generated after one accumulation.
3. The natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method of claim 1, wherein: the specific process of the second step is that firstly, aiming at an ash number sequence generated after the pretreatment of the natural gas pipeline landslide deformation original data, a grey differential equation of a single grey theoretical model GM (1,1) is established; then whitening the gray differential equation to obtain a time response sequence equation of the color differential equation; further carrying out one-to-one residual error detection on the grey theoretical model predicted value and the natural gas pipeline landslide deformation field actual monitoring value, and concretely implementing the following steps:
will Z (1) Assume as post-pretreatment X (1) The grey number sequence generated:
in the above formula: k is a natural number 1,2, 3 … …
Establishing a gray differential equation of a single gray theoretical model GM (1,1) according to a gray number sequence after pretreatment of the landslide deformation monitoring data of the natural gas pipeline:
x (0) (k)+az (1) (k)＝b (4)
in the above formula: a is the development coefficient of the natural gas pipeline landslide deformation monitoring gray prediction model, and b is the acting dependent variable of the natural gas pipeline landslide deformation monitoring gray prediction model;
the gray differential equation is then whitened because
And let x (1) (0)＝x (0) (1) And obtaining a time response sequence equation of a gray differential equation of the natural gas pipeline landslide deformation prediction:
carrying out one-to-one residual error detection on the difference between the grey theoretical model predicted value and the field actual monitoring value of the landslide deformation of the natural gas pipeline, and calculating the original monitoring data x of the landslide deformation of the pipeline (0) (i) And
the absolute residual of the error signal is calculated,
4. the natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method of claim 1, wherein: the concrete flow of the third step is that an Induced Order Weighted Harmonic (IOWHA) operator is introduced through grey theory GM (1,1) model prediction of natural gas pipeline landslide deformation, each point of a single grey theory GM (1,1) model is endowed with a weight according to a precision sequence, and the concrete implementation is as follows:
at a certain moment, the natural gas pipeline landslide deformation prediction based on the IOWHA operator combined model is as follows:
in the above formula, x it (i ═ 1,2) is the i th prediction model, P it P-index (it) is a subscript of a value with higher prediction precision of a single prediction method;
at time t, the method with higher prediction accuracy gives a larger weight coefficient. Suppose that
e a-index(it) ＝1/x t -1/x p-index(it) (8)
In the above equation, a is a noise sequence.
5. The natural gas pipeline landslide accumulated deformation curve analysis and landslide prediction method of claim 1, wherein: the specific process of the second step is that according to an IOWHA combined prediction theory, the minimum value of the sum of squares of the inverses of the prediction data errors of the natural gas pipeline landslide deformation nth stage is obtained:
x in the above formula t Is a weighted harmonic mean at time t, where l ═ l 1 ,l 2 ) T Predicting a weighting factor for the combination;
establishing dynamic grey time series oil-gas pipe based on Induced Order Weighted Harmonic (IOWHA) operator
The road landslide deformation prediction model is as follows:
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