CN114263855A - Method for predicting leakage of natural gas transportation pipeline and application thereof - Google Patents
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Abstract
The invention relates to the technical field of pipeline transportation, and provides a prediction method of natural gas transportation pipeline leakage and application thereof, wherein the prediction method comprises the following steps: acquiring an original data set and a monitoring point set of a transport pipeline; processing the original data set to obtain a dimension reduction data set; inputting the dimension reduction data set into a prediction model to obtain a prediction probability set of the monitoring point set; constructing a pipeline model according to the monitoring point set, and filling the prediction probability set into the pipeline model to obtain the leakage probability of a unit rectangle of a grid region in the pipeline model; and judging whether the leakage probability of the unit rectangle of the grid area is smaller than a preset threshold value. By the method for predicting the leakage of the natural gas transportation pipeline and the application thereof, disclosed by the invention, the leakage probability of the leakage point of the transportation pipeline can be predicted, the leakage can be timely and effectively found, and the leakage point can be accurately positioned.
Description
Technical Field
The invention relates to the technical field of pipeline transportation, in particular to a method for predicting leakage of a natural gas transportation pipeline and application thereof.
Background
When transporting natural gas and the like, pipelines are affected by operation time, operation environment and the like, and are inevitably corroded to finally cause leakage accidents, so that the positions of pipeline leakage need to be positioned. The traditional detection method is implemented through a hardware detection method, and the detection method has the problems of poor generalization capability, low positioning accuracy and the like.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to provide a method for predicting leakage of a natural gas transportation pipeline and an application thereof.
To achieve the above and other related objects, the present invention provides a method for predicting leakage of a natural gas transportation pipeline, comprising the steps of:
acquiring an original data set and a monitoring point set of a transport pipeline;
processing the original data set to obtain a dimension reduction data set;
inputting the dimension reduction data set into a prediction model to obtain a prediction probability set of the monitoring point set;
constructing a pipeline model according to the monitoring point set, and filling the prediction probability set into the pipeline model to obtain the leakage probability of a unit rectangle of a grid region in the pipeline model;
judging whether the leakage probability of the unit rectangle of the grid area is smaller than a preset threshold value: if so, determining that the unit rectangle of the grid area has no leakage risk, and if not, determining that the unit rectangle of the grid area has leakage risk.
In an embodiment of the present invention, the step of processing the original data set to obtain a dimension reduction data set includes:
carrying out sample centralization processing on the original data set to obtain centralization data sets of different types of labels;
processing the centralized data set to obtain a corresponding characteristic value set;
selecting the maximum characteristic value in the characteristic value set to obtain a corresponding characteristic vector set;
and processing the feature vector set and the centralized data set to obtain a dimension reduction data set.
In an embodiment of the invention, the centralized data set x'iExpressed as:
wherein x isiFor the ith data in the category label,
m is the total number of data contained in a single category label.
In an embodiment of the present invention, the feature value set is expressed as: q ═ Q Σ Q-1Wherein, in the step (A),
sigma is a diagonal array, the elements on each diagonal are corresponding characteristic values,
q is a matrix of eigenvectors of the covariance matrix a,
the covariance matrix a is expressed as: a ═ X × (X ═ X)T，
X is centralized data X'iThe matrix of (a) is,
t is transposition.
In an embodiment of the present invention, the feature vector set W is expressed as: w ═ W1,w2,w3,…,wd) Wherein w isdFor the eigenvector corresponding to the d-th largest eigenvalue, the dimension reduction dataset Y is represented as: y ═ W × X.
In an embodiment of the present invention, the prediction probability set P is expressed as:
wherein the content of the first and second substances,
n is the total number of category labels,
m is the total number of the monitoring points,
pmnleakage summary for mth monitoring point in nth category labelAnd (4) rate.
In an embodiment of the present invention, the step of constructing a pipeline model according to the monitoring point set, and filling the prediction probability set into the pipeline model to obtain the leakage probability of a unit rectangle of a grid region in the pipeline model includes:
constructing a pipeline model according to the monitoring point set, and carrying out gridding processing on the pipeline model according to the positions of the monitoring points in the monitoring point set to obtain the leakage probability of a single monitoring point;
and obtaining the leakage probability of the unit rectangle of the grid region in the pipeline model according to the leakage probability of the monitoring points.
In one embodiment of the invention, the leakage probability p of the single monitoring pointiExpressed as:
wherein the content of the first and second substances,
i is the ith monitoring point and the ith monitoring point,
c d is in the form of a grid of the area around the i-th monitoring point,
In one embodiment of the present invention, the leakage probability p of the unit rectanglewExpressed as:
wherein the content of the first and second substances,
w is a unit rectangle within the grid area,
k is the number of active monitoring points around the unit rectangle w,
λithe weight of the influence of different monitoring points on the unit rectangle w,
The invention also provides a system for predicting natural gas transportation pipeline leakage, which comprises:
the data acquisition module is used for acquiring an original data set and a monitoring point set of the transportation pipeline;
the data processing module is used for processing the original data set to obtain a dimension reduction data set;
the prediction module is used for processing the dimension reduction data set to obtain a prediction probability set of the monitoring point set;
the model building module is used for building a pipeline model and filling the prediction probability set into the pipeline model to obtain the leakage probability of a unit rectangle of a grid region in the pipeline model; and
and the judging module is used for judging the leakage probability of the unit rectangle of the grid region so as to judge whether the unit rectangle of the grid region has leakage risk.
As described above, the invention provides a method for predicting leakage of a natural gas transportation pipeline and an application thereof, which can find leakage timely and effectively and accurately position leakage points, thereby reducing the overhaul range and facilitating troubleshooting of maintenance personnel. Meanwhile, the intelligent detection is realized, the manpower and material resource investment can be reduced, the leakage tracing cost is reduced, the direct contact of personnel is reduced, and the injury risk of the personnel is reduced. The invention can timely send out early warning, can prevent positions with possible leakage risks in advance, can take some protective measures in time, can reduce the property loss of personnel to the minimum, and effectively protects the life safety of the personnel.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart illustrating a method for predicting leakage in a natural gas transportation pipeline according to the present invention.
Fig. 2 is a flow chart illustrating a sub-step of step S2 in the method for predicting leakage of a natural gas transportation pipeline according to the present invention.
Fig. 3 is a flow chart illustrating a sub-step of step S4 in the method for predicting leakage of a natural gas transportation pipeline according to the present invention.
Fig. 4 is a schematic diagram of a system for predicting natural gas transportation pipeline leakage according to the present invention.
In the figure: 1. a data acquisition module; 2. a data processing module; 3. a prediction module; 4. a model building module; 5. and a judging module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention discloses a method for predicting leakage of a natural gas transportation pipeline, which can predict a possible leakage position on the natural gas transportation pipeline, so as to predict a leakage probability of a leakage point of the transportation pipeline, effectively find the leakage in time, and accurately position the leakage point. The method for predicting the leakage of the natural gas transportation pipeline can comprise the following steps:
and step S1, acquiring an original data set and a monitoring point set of the transportation pipeline.
In this embodiment, the original data set may include a first data set, a second data set, and a third data set. The first data set is obtained by performing simulation on a multi-factor comprehensive actual working environment such as pressure, temperature and the like through software and the like, and establishing a discretized transient flow model comprising a plurality of monitoring points, so that basic data such as pressure, temperature, flow rate, concentration and the like of the monitoring points can be obtained through calculation, each group of data corresponds to a corresponding category label, for example, a pressure data group corresponds to a pressure label, and the basic data and the category labels are integrated to obtain the first data set.
The second data set refers to data acquired in real time through a sensor, for example, by installing a plurality of pressure sensors in the pipeline, the pressure at different positions in the pipeline can be detected in real time, and corresponding pressure data can be obtained; the temperature sensors are arranged in the pipeline, so that the temperatures of different positions in the pipeline can be detected in real time, and corresponding temperature data can be obtained; the flow velocity sensors are arranged in the pipeline, so that the flow velocity of different positions in the pipeline can be detected in real time, and corresponding flow velocity data can be obtained; through a plurality of concentration sensor of installation in the pipeline to can carry out real-time detection to the concentration of different positions in the pipeline, thereby obtain corresponding concentration data, accessible different sensor detects different classification labels, thereby obtain corresponding data.
The third data set refers to the corresponding data set obtained after other companies detect the pipeline in advance. The monitoring point set refers to the installation positions of a plurality of sensors, and corresponding data in the pipeline can be detected through different sensors.
Referring to fig. 2, in step S2, the original data set is processed to obtain a dimension-reduced data set. The step of processing the original data set to obtain a dimension reduction data set comprises:
step S21, carrying out sample centralization processing on the original data set to obtain centralized data sets x 'with different category labels'iExpressed as:
wherein the content of the first and second substances,
xifor the ith data in the category label,
m is the total number of data contained in a single category label,
raw data set Xm*nExpressed as:
wherein the content of the first and second substances,
n is the number of category labels,
amnfor the mth data of the nth class label,
cna column vector for the data contained in the nth class label.
Step S22, processing the centralized data set to obtain a corresponding feature value set, which is expressed as: q ═ Q Σ Q-1Wherein, in the step (A),
q is a matrix of eigenvectors of the covariance matrix a,
sigma is a diagonal array, the elements on each diagonal are corresponding characteristic values,
the covariance matrix a is expressed as: a ═ X × (X ═ X)TWherein, in the step (A),
x is centralized data X'iThe matrix of (a) is,
t is transposition.
Step S23, selecting the maximum eigenvalue in the eigenvalue set to obtain a corresponding eigenvector set W, which is expressed as: w ═ W1,w2,w3,…,wd) Wherein the largest characteristic value represents the element with the largest value on the diagonal of each diagonal matrix Σ, wdAnd the feature vector corresponding to the d-th maximum feature value.
Step S24, processing the feature vector set and the centralized data set to obtain a dimensionality reduction data set Y represented as: y ═ W × X.
In step S21, when the original data set needs to be processed, the original data set can be represented in a matrix form for subsequent processing, where the matrix form X of the original data setm*nExpressed as:
wherein the content of the first and second substances,
n is the number of category labels,
amnfor the mth data of the nth class label,
cna column vector for the data contained in the nth class label. Specifically, each column of data in the matrix form of the raw data set represents data of a category label, for example, a first column of data may represent data of a category label being pressure, a second column of data may represent data of a category label being temperature, a third column of data may represent data of a category label being flow rate, and a fourth column of data may represent data of a category label being concentration. When the original data set is subjected to sample centralization processing, a corresponding average value of each line of data in a matrix form of the original data set can be obtained, and then a difference value between specific data in each line of data and the average value of the line of data is calculated, so that centralization data sets of different lines of data, namely centralization data sets x 'of different types of tags can be obtained'iExpressed as:
wherein the content of the first and second substances,
xifor the ith data in the category label,
m is the total number of data contained in a single category label.
In step S22, when the centralized data set x 'of different category labels is obtained'iLater, to facilitate processing thereof, the centralized data set x 'may therefore be in the form of a matrix'iExpressed as:
to obtain the corresponding feature value set, a covariance matrix a of the centralized data set may be first calculated, which is expressed as: a ═ X × (X ═ X)TWhere T is transposed, then may be pairedThe covariance matrix A is subjected to eigenvalue decomposition to obtain A ═ Q ∑ Q-1And Q is a matrix formed by eigenvectors of the covariance matrix A, sigma is a diagonal matrix, and elements on each diagonal are corresponding eigenvalues, so that a corresponding eigenvalue set can be obtained according to the diagonal matrix sigma.
In step S23, since each diagonal matrix Σ has a plurality of feature values, the largest feature value of the plurality of feature values of a single diagonal matrix Σ may be selected first, and then the remaining diagonal matrices Σ may be processed accordingly, so that the largest feature value of the diagonal matrix Σ may be selected to obtain one largest feature value set, and the feature vector set W corresponding to the largest feature value set may be represented as: w ═ W1,w2,w3,…,wd) Wherein w isdAnd the feature vector corresponding to the d-th maximum feature value.
In step S24, after obtaining the feature vector set corresponding to the maximum feature value set, a dimension reduction data set Y can be obtained according to the centralized data set and the feature vector set, and is represented as: y ═ W × X.
Step S3, inputting the dimensionality reduction data set into a prediction model to obtain a prediction probability set P of the monitoring point set, wherein the prediction probability set P is expressed as:
wherein p ismnAnd (4) the leakage probability of the mth monitoring point in the nth class label.
In this embodiment, the neural network of the prediction model may be trained using the reduced-dimension dataset, and the training parameters of the neural network may be optimized to classify the predicted leakage points. Therefore, different prediction probabilities can be given according to different maximum characteristic values and different category labels to optimize data monitored by the characteristic monitoring points, and a prediction probability set P of the monitoring point set can be obtained and expressed as:
wherein p ismnAnd (4) the leakage probability of the mth monitoring point in the nth class label.
Referring to fig. 3, in step S4, a pipeline model is built according to the monitoring point set, and the prediction probability set is filled into the pipeline model, so as to obtain the leakage probability of a unit rectangle of a grid region in the pipeline model. The step of constructing a pipeline model according to the monitoring point set, and filling the prediction probability set into the pipeline model to obtain the leakage probability of the unit rectangle of the grid region in the pipeline model comprises the following steps:
s41, constructing a pipeline model according to the monitoring point set, and carrying out gridding processing on the pipeline model according to the positions of the monitoring points in the monitoring point set to obtain the leakage probability p of each monitoring pointiExpressed as:
wherein the content of the first and second substances,
i is the ith monitoring point and the ith monitoring point,
c d is in the form of a grid of the area around the i-th monitoring point,
Step S42, obtaining the leakage probability p of the unit rectangle of the grid area in the pipeline model according to the leakage probability of the monitoring pointwExpressed as:
wherein the content of the first and second substances,
w is a unit rectangle within the grid area,
k is the number of active monitoring points around the unit rectangle w,
λithe weight of the influence of different monitoring points on the unit rectangle w,
In step S41, a pipeline model may be constructed for the transportation pipeline according to the position of each monitoring point in the monitoring point set, the pipeline model may be gridded, the surrounding area of each monitoring point may be represented in a grid form, and the surrounding area of each monitoring point may be divided into a plurality of unit rectangles. Therefore, the leakage probability p of a single monitoring point can be obtained according to the prediction probability set of the monitoring point setiExpressed as:
wherein the content of the first and second substances,
i is the ith monitoring point and the ith monitoring point,
c d is in the form of a grid of the area around the i-th monitoring point,
In step S42, the probability p of leakage due to each monitoring pointiAre not more than 1, and therefore, after a plurality of unit rectangles are divided into the peripheral regions of the respective monitoring points, the leak probabilities p of all the unit rectangleswIs also no greater than 1. Therefore, the surrounding area of each monitoring point can be divided into a plurality of unit rectangles, the leakage probability of the single monitoring point is used as a weight value and is filled into the subdivided unit rectangles, the leakage probability of the unit rectangles can be obtained, and when the area of the unit rectangles is small enough, the accurate positioning of the leakage point in the pipeline can be realized. Wherein, the leakage probability p of unit rectangle of grid region in the pipeline modelwExpressed as:
wherein the content of the first and second substances,
w is a unit rectangle within the grid area,
k is the number of active monitoring points around the unit rectangle w,
λithe weight of the influence of different monitoring points on the unit rectangle w,
Step S5, determining whether the leakage probability of the unit rectangle of the grid region is less than a preset threshold: if the unit rectangle in the grid area has no leakage risk, judging that the unit rectangle in the grid area has the leakage risk, and closing the transportation pipeline.
In this embodiment, the specific size of the preset threshold may be set according to actual requirements, and may be set to 0.3, 0.4, 0.5, and so on. When the leakage probability of a certain unit rectangle in the grid area is larger than a preset threshold value, an alarm can be sent to the system at the moment, a leakage point is positioned through a pipeline model of the transportation pipeline and the specific position of the unit rectangle in the grid area to remind workers that the leakage point is possibly at risk, the transportation pipeline is closed, meanwhile, the system can record data recorded by sensors around the leakage point in a period of time and keep the data, so that parameters in the neural network of the prediction model can be optimized in the later period, wherein the specific length of the period of time for recording the data can be set according to actual requirements, for example, all data in the half hour before and after the leakage can be recorded, and certainly, when the leakage occurs, corresponding data can be displayed through charts such as data statistics and the like.
Referring to fig. 4, the present invention further discloses a system for predicting leakage of a natural gas transportation pipeline, where the system for predicting leakage of a natural gas transportation pipeline may include: the device comprises a data acquisition module 1, a data processing module 2, a prediction module 3, a model construction module 4 and a judgment module 5. The data acquisition module 1 can be used for acquiring an original data set and a monitoring point set of a transportation pipeline, the data processing module 2 can be used for processing the original data set so as to obtain a dimension reduction data set, the prediction module 3 can process the dimension reduction data set so as to obtain a prediction probability set of the monitoring point set, the model building module 4 can build a pipeline model according to the monitoring point set and fill the prediction probability set into the pipeline model so as to obtain the leakage probability of unit rectangles of grid regions in the pipeline model, and the judgment module 5 can be used for judging the leakage probability of the unit rectangles of the grid regions so as to judge whether the unit rectangles of the grid regions have leakage risks.
In conclusion, the method for predicting the leakage of the natural gas transportation pipeline and the application thereof can find the leakage timely and effectively and accurately position the leakage point, thereby reducing the overhaul range and facilitating the troubleshooting of maintenance personnel. Meanwhile, the intelligent detection is realized, the manpower and material resource investment can be reduced, the leakage tracing cost is reduced, the direct contact of personnel is reduced, and the injury risk of the personnel is reduced. The invention can timely send out early warning, can prevent positions with possible leakage risks in advance, can take some protective measures in time, can reduce the property loss of personnel to the minimum, and effectively protects the life safety of the personnel.
In the description of the present specification, reference to the description of the terms "present embodiment," "example," "specific example," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The embodiments of the invention disclosed above are intended merely to aid in the explanation of the invention. The examples are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. A method for predicting leakage of a natural gas transportation pipeline is characterized by comprising the following steps:
acquiring an original data set and a monitoring point set of a transport pipeline;
processing the original data set to obtain a dimension reduction data set;
inputting the dimension reduction data set into a prediction model to obtain a prediction probability set of the monitoring point set;
constructing a pipeline model according to the monitoring point set, and filling the prediction probability set into the pipeline model to obtain the leakage probability of a unit rectangle of a grid region in the pipeline model;
judging whether the leakage probability of the unit rectangle of the grid area is smaller than a preset threshold value: if so, determining that the unit rectangle of the grid area has no leakage risk, and if not, determining that the unit rectangle of the grid area has leakage risk.
2. The method for predicting natural gas transportation pipeline leakage according to claim 1, wherein the step of processing the raw data set to obtain a reduced-dimension data set comprises:
carrying out sample centralization processing on the original data set to obtain centralization data sets of different types of labels;
processing the centralized data set to obtain a corresponding characteristic value set;
selecting the maximum characteristic value in the characteristic value set to obtain a corresponding characteristic vector set;
and processing the feature vector set and the centralized data set to obtain a dimension reduction data set.
3. The method of predicting gas transportation pipeline leakage of claim 2, wherein the centralized data set x'iExpressed as:
wherein the content of the first and second substances,
xifor the ith data in the category label,
m is the total number of data contained in a single category label.
4. The method of predicting natural gas transport pipeline leaks of claim 2, wherein the set of eigenvalues is expressed as: q ═ Q Σ Q-1Wherein, in the step (A),
sigma is a diagonal array, the elements on each diagonal are corresponding characteristic values,
q is a matrix of eigenvectors of the covariance matrix a,
the covariance matrix a is expressed as: a ═ X × (X ═ X)T，
X is centralized data X'iThe matrix of (a) is,
t is transposition.
5. The method of predicting natural gas transport pipeline leaks of claim 2, wherein the set of eigenvectors, W, is expressed as: w ═ W1,w2,w3,…,wd) Wherein w isdFor the eigenvector corresponding to the d-th largest eigenvalue, the dimension reduction dataset Y is represented as: y ═ W × X.
6. The method of claim 1The method for predicting natural gas transportation pipeline leakage, wherein the prediction probability set P is expressed as:
wherein the content of the first and second substances,
n is the total number of category labels,
m is the total number of the monitoring points,
pmnand (4) the leakage probability of the mth monitoring point in the nth class label.
7. The method for predicting natural gas transportation pipeline leakage according to claim 1, wherein the step of constructing a pipeline model according to the monitoring point set, and filling the pipeline model with the prediction probability set to obtain the leakage probability of a unit rectangle of a grid region in the pipeline model comprises:
constructing a pipeline model according to the monitoring point set, and carrying out gridding processing on the pipeline model according to the positions of the monitoring points in the monitoring point set to obtain the leakage probability of a single monitoring point;
and obtaining the leakage probability of the unit rectangle of the grid region in the pipeline model according to the leakage probability of the monitoring points.
8. The method of predicting gas transport pipeline leaks according to claim 7, wherein the leak probability p of the single monitoring pointiExpressed as:
wherein the content of the first and second substances,
i is the ith monitoring point and the ith monitoring point,
c d is in the form of a grid of the area around the i-th monitoring point,
9. The method for predicting natural gas transportation pipeline leakage according to claim 7, wherein the unit rectangle has a leakage probability pwExpressed as:
wherein the content of the first and second substances,
w is a unit rectangle within the grid area,
k is the number of active monitoring points around the unit rectangle w,
λithe weight of the influence of different monitoring points on the unit rectangle w,
10. A system for predicting natural gas transportation pipeline leaks, comprising:
the data acquisition module is used for acquiring an original data set and a monitoring point set of the transportation pipeline;
the data processing module is used for processing the original data set to obtain a dimension reduction data set;
the prediction module is used for processing the dimension reduction data set to obtain a prediction probability set of the monitoring point set;
the model building module is used for building a pipeline model and filling the prediction probability set into the pipeline model to obtain the leakage probability of a unit rectangle of a grid region in the pipeline model; and
and the judging module is used for judging the leakage probability of the unit rectangle of the grid region so as to judge whether the unit rectangle of the grid region has leakage risk.
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| CN108647470A (en) * | 2018-05-29 | 2018-10-12 | 杭州电子科技大学 | A kind of localization method at the beginning of based on the leakage loss with depth belief network is clustered |
| CN110107819A (en) * | 2019-05-17 | 2019-08-09 | 河南工业大学 | A kind of petroleum chemicals conveyance conduit leakage monitoring early warning system and method |
| US20210116325A1 (en) * | 2019-10-22 | 2021-04-22 | Saudi Arabian Oil Company | Bisection numerical algorithm coupled with pressure deviation method to determine the size and location of a leak in a pipeline |
| CN111734961A (en) * | 2020-06-24 | 2020-10-02 | 东北石油大学 | Natural gas pipeline leakage detection method |
| CN113124328A (en) * | 2021-03-15 | 2021-07-16 | 湖南匡楚科技有限公司 | Natural gas pipeline leakage detection method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116822952A (en) * | 2023-06-26 | 2023-09-29 | 北京讯腾智慧科技股份有限公司 | Risk assessment method and device for gas pipe network |
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