CN112032568A - Buried gas pipeline leakage risk prediction algorithm and prediction method - Google Patents

Buried gas pipeline leakage risk prediction algorithm and prediction method Download PDF

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CN112032568A
CN112032568A CN202010815236.6A CN202010815236A CN112032568A CN 112032568 A CN112032568 A CN 112032568A CN 202010815236 A CN202010815236 A CN 202010815236A CN 112032568 A CN112032568 A CN 112032568A
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gas pipeline
buried gas
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何文韬
乔宏哲
陶国正
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Changzhou Vocational Institute of Mechatronic Technology
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Abstract

The invention belongs to the technical field of gas pipeline maintenance, and particularly relates to a buried gas pipeline leakage risk prediction algorithm and a prediction method, wherein the buried gas pipeline leakage risk prediction algorithm comprises the following steps: collecting data; establishing a corresponding vector according to the corresponding data; constructing a buried gas pipeline leakage risk model according to the corresponding vector; according to the buried gas pipeline leakage risk degree model and the parameters of the corresponding positions on the buried gas pipeline, acquiring the buried gas pipeline leakage risk degree of the corresponding positions in real time; generating a preventive maintenance strategy according to the leakage risk of the buried gas pipeline at the corresponding position; the invention can overcome the defects of strong subjectivity and great randomness of the buried gas pipeline leakage danger judgment and early warning manually according to the monitoring data, is favorable for carrying out advanced protection and predictive maintenance on buried gas pipeline leakage accidents, has better economical efficiency for the predictive maintenance, and simultaneously reduces the labor cost for buried gas pipeline maintenance.

Description

Buried gas pipeline leakage risk prediction algorithm and prediction method
Technical Field
The invention belongs to the technical field of gas pipeline maintenance, and particularly relates to a buried gas pipeline leakage risk prediction algorithm and a prediction method.
Background
The leakage of the buried gas pipeline cannot be intuitively and obviously found, and particularly, the leakage is not easy to find when trace leakage occurs. When leakage occurs, a series of secondary disasters such as explosion and the like are likely to occur, so that serious personal injuries and property losses are caused. The buried pipeline belongs to concealed engineering, has the characteristics of 24-hour uninterrupted operation and the like, can only ensure the surface safety in daily inspection, has little mastery of internal conditions, causes insufficient information mastery and is difficult to manage. The ultrasonic real-time monitoring gas pipeline data can only be used for overhead pipelines, and cannot obtain enough information for buried pipelines.
At present, common detection methods for buried gas pipelines comprise manual inspection and intelligent inspection based on the internet of things technology, and the inspection methods are used for detecting the leakage condition of the buried gas pipelines and belong to post maintenance. The latter maintenance mode uses buried gas pipelines until leakage occurs, and then repair, if severe leakage occurs, may cause huge loss.
State maintenance based on state monitoring is currently developed, and experts perform reasoning and judgment by adopting mastered knowledge and experience about buried pipelines according to information provided by various measured values obtained by state monitoring, so that maintenance suggestions of the buried pipelines are provided. The method uses a large amount of manpower intervention such as experts, and has the disadvantages of large workload, high labor cost and poor economy; meanwhile, the problems of strong subjectivity and great randomness exist.
Therefore, it is necessary to develop a new prediction algorithm and prediction method for the leakage risk of the buried gas pipeline to solve the above problems.
Disclosure of Invention
The invention aims to provide a prediction algorithm and a prediction method for buried gas pipeline leakage risk.
In order to solve the technical problem, the invention provides a buried gas pipeline leakage risk prediction algorithm, which comprises the following steps: collecting data; establishing a corresponding vector according to the corresponding data; constructing a buried gas pipeline leakage risk model according to the corresponding vector; according to the buried gas pipeline leakage risk degree model and the parameters of the corresponding positions on the buried gas pipeline, acquiring the buried gas pipeline leakage risk degree of the corresponding positions in real time; and generating a preventive maintenance strategy according to the leakage risk of the buried gas pipeline at the corresponding position.
Further, the method for acquiring data comprises the following steps: collecting stress values and PH values, and calculating stress change rates and PH value change rates according to the corresponding stress values and the PH values, wherein the stress change rates are as follows:
Figure BDA0002632446160000021
the change rate of the pH value is as follows:
Figure BDA0002632446160000022
wherein, F0jThe average value of stress measurement F corresponding to the jth node when the buried gas pipeline is just installed or the pipeline state is confirmed to be normaliFor the ith recorded stress value, FRiStress rate of change, PH, recorded for the ith0jThe average value of the PH value measurement corresponding to the jth node when the buried gas pipeline is just installed or the pipeline state is confirmed to be normal is determined, and the PH value isiFor the pH value of the ith record, PHRiThe PH change rate recorded for the ith.
Further, the method for establishing the corresponding vector according to the corresponding data comprises the following steps: for characteristic stress change rate FRiRate of change of pH value PHRiAnd quantifying, wherein the quantification interval is 0.1, and the service time of the buried gas pipeline is rounded according to the year.
Further, the method for establishing the corresponding vector according to the corresponding data further comprises: establishing a data vector: x ═ x(1)，x(2)，x(3)) (ii) a Establishing a coefficient vector: w ═ w (w)(1)，w(2)，w(3)) (ii) a Constructing an optimization model, i.e.
Figure BDA0002632446160000023
S.tyi(w.xi+b)≥1-ξi；ξiMore than or equal to 0i ═ 1, 2.· N; wherein x is(1)For quantified stress changesRate, x(2)To quantify the rate of change of pH, x(3)For the quantized gas pipeline service time, the component of the vector w is the coefficient of the corresponding component of the vector x, and C is a penalty coefficient; x is the number ofiIs the ith training data vector; y isiIs xiWhen y is a class markiWhen the value is-1, leakage occurs in the buried gas pipeline, and when the value is yiWhen the value is 1, the buried gas pipeline is in a normal state; n is the number of training data; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is an offset; the solution of the optimization model is then: w is a*；
Figure BDA0002632446160000031
Wherein, w*Classifying the normal vector of the hyperplane for the optimal;
Figure BDA0002632446160000032
is the ith element of the solution to the dual problem in the lagrange multiplier vector.
Further, the method for constructing the buried gas pipeline leakage risk degree model according to the corresponding vector comprises the following steps: acquiring the mean value and the variance of two data categories of the leakage of the buried gas pipeline and the normal state of the buried gas pipeline, namely acquiring the data obtained after the projection of the stress value and the PH value on the normal vector, namely
Figure BDA0002632446160000033
Figure BDA0002632446160000034
Wherein N isAThe number of samples in the category y is 1; n is a radical ofBThe number of samples in the category y-1; mu.sBData for occurrence of leakage of buried gas pipeline is shown at w*Mean value of the data obtained after vector axis projection; mu.sANormal data for the buried gas pipeline state is w*Mean value of the data obtained after vector axis projection;Anormal data for the buried gas pipeline state is w*Standard deviation of data obtained after vector axis projection;Bdata for occurrence of leakage of buried gas pipeline is shown at w*After vector axis projectionStandard deviation of the resulting data;
Figure BDA0002632446160000035
further, the method for acquiring the leakage risk of the buried gas pipeline at the corresponding position in real time according to the buried gas pipeline leakage risk model and the parameters of the corresponding position on the buried gas pipeline comprises the following steps: let xAFor the sum of sample data for all y ═ 1, ZAIs xAAt w*Projection of vector axis, let current data be xcI.e. Zc ═ w*xc(ii) a When wxc≤μA+AThen, the risk degree V of the buried gas pipeline is 0; when wxc≥μB-BThen, the danger degree V of the buried gas pipeline is 1; when mu isA+A≤wxc≤μB-BAnd the risk degree V of the buried gas pipeline is as follows:
Figure BDA0002632446160000041
wherein, H (Z)C|ZA) At a given ZAUnder the condition of ZCThe conditional entropy of (a); h (mu)B|ZA) At a given ZAUnder the condition ofBThe conditional entropy of (a); the smaller the risk degree V of the buried gas pipeline is, the smaller the leakage probability of the buried gas pipeline is, and the larger the risk degree V of the buried gas pipeline is, the larger the leakage probability of the buried gas pipeline is.
Further, the method for generating the preventive maintenance strategy according to the leakage risk of the buried gas pipeline at the corresponding position comprises the following steps: controlling the inspection time interval according to the danger degree of the buried gas pipeline, namely T ═ VT1+(1-V)T0(ii) a T is the inspection time interval which should be adopted by the current buried gas pipeline under the current risk degree V, T0The reference inspection time interval T when the current buried gas pipeline has the risk degree V of 01The time interval is patrolled and examined for the benchmark when being 1 for degree of danger V to burying the gas pipeline at present.
On the other hand, the invention provides a method for predicting the leakage risk of a buried gas pipeline, which comprises the following steps: collecting data and sending the data to a cloud server and/or a processor module; and the cloud server and/or the processor module judges the risk degree of the buried gas pipeline according to the data.
Further, the cloud server and/or the processor module are suitable for judging the risk level of the buried gas pipeline by adopting the buried gas pipeline leakage risk level prediction algorithm.
The invention has the advantages that the defects of strong subjectivity and great randomness of the buried gas pipeline leakage danger judgment and early warning performed manually according to the monitoring data can be overcome, the buried gas pipeline leakage accident can be protected in advance and maintained predictively, the predictive maintenance has better economical efficiency compared with the later maintenance, and the labor cost of the buried gas pipeline maintenance is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a buried gas pipeline leakage risk prediction algorithm of the present invention;
fig. 2 is a flow chart of the buried gas pipeline leakage risk prediction method of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. 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.
Example 1
Fig. 1 is a flow chart of a buried gas pipeline leakage risk prediction algorithm of the present invention.
In this embodiment, as shown in fig. 1, the present embodiment provides a buried gas pipeline leakage risk prediction algorithm, which includes: collecting data; establishing a corresponding vector according to the corresponding data; constructing a buried gas pipeline leakage risk model according to the corresponding vector; according to the buried gas pipeline leakage risk degree model and the parameters of the corresponding positions on the buried gas pipeline, acquiring the buried gas pipeline leakage risk degree of the corresponding positions in real time; and generating a preventive maintenance strategy according to the leakage risk of the buried gas pipeline at the corresponding position.
In the embodiment, the defects that the buried gas pipeline leakage danger judgment and early warning are performed manually according to monitoring data and the advantages of strong subjectivity and great randomness are overcome, the buried gas pipeline leakage accident is protected in advance and predictive maintenance is facilitated, the predictive maintenance has better economical efficiency compared with the post-event maintenance, and meanwhile, the labor cost of the buried gas pipeline maintenance is reduced.
In this embodiment, the method for acquiring data includes: collecting stress values and PH values, and calculating stress change rates and PH value change rates according to the corresponding stress values and the PH values, wherein the stress change rates are as follows:
Figure BDA0002632446160000061
the change rate of the pH value is as follows:
Figure BDA0002632446160000062
wherein, F0jThe average value of stress measurement F corresponding to the jth node when the buried gas pipeline is just installed or the pipeline state is confirmed to be normaliFor the ith recordStress value of, FRiStress rate of change, PH, recorded for the ith0jThe average value of the PH value measurement corresponding to the jth node when the buried gas pipeline is just installed or the pipeline state is confirmed to be normal is determined, and the PH value isiFor the pH value of the ith record, PHRiThe PH change rate recorded for the ith.
In this embodiment, the method for establishing the corresponding vector according to the corresponding data includes: for characteristic stress change rate FRiRate of change of pH value PHRiAnd quantifying, wherein the quantification interval is 0.1, and the service time of the buried gas pipeline is rounded according to the year.
In this embodiment, the method for establishing the corresponding vector according to the corresponding data further includes: establishing a data vector: x ═ x(1)，x(2)，x(3)) (ii) a Establishing a coefficient vector: w ═ w (w)(1)，w(2)，w(3)) (ii) a Constructing an optimization model, i.e.
Figure BDA0002632446160000063
S.tyi(w.xi+b)≥1-ξi；ξiMore than or equal to 0i ═ 1, 2.· N; wherein x is(1)To quantify the rate of change of stress, x(2)To quantify the rate of change of pH, x(3)For the quantized gas pipeline service time, the component of the vector w is the coefficient of the corresponding component of the vector x, and C is a penalty coefficient; x is the number ofiIs the ith training data vector; y isiIs xiWhen y is a class markiWhen the value is-1, leakage occurs in the buried gas pipeline, and when the value is yiWhen the value is 1, the buried gas pipeline is in a normal state; n is the number of training data; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is an offset; the solution of the optimization model is then: w is a*；
Figure BDA0002632446160000064
Wherein, w*Classifying the normal vector of the hyperplane for the optimal;
Figure BDA0002632446160000065
for the middle pair of Lagrange multiplier vectorsThe ith element of the solution of the even problem.
In this embodiment, the method for constructing the buried gas pipeline leakage risk model according to the corresponding vector includes: acquiring the mean value and the variance of two data categories of the leakage of the buried gas pipeline and the normal state of the buried gas pipeline, namely acquiring the data obtained after the projection of the stress value and the PH value on the normal vector, namely
Figure BDA0002632446160000071
Figure BDA0002632446160000072
Wherein N isAThe number of samples in the category y is 1; n is a radical ofBThe number of samples in the category y-1; mu.sBData for occurrence of leakage of buried gas pipeline is shown at w*Mean value of the data obtained after vector axis projection; mu.sANormal data for the buried gas pipeline state is w*Mean value of the data obtained after vector axis projection;Anormal data for the buried gas pipeline state is w*Standard deviation of data obtained after vector axis projection;Bdata for occurrence of leakage of buried gas pipeline is shown at w*Standard deviation of data obtained after vector axis projection;
Figure BDA0002632446160000073
in this embodiment, the method for obtaining the leakage risk of the buried gas pipeline at the corresponding position in real time according to the buried gas pipeline leakage risk model and the parameters of the corresponding position on the buried gas pipeline includes: let xAFor the sum of sample data for all y ═ 1, ZAIs xAAt w*Projection of vector axis, let current data be xcI.e. Zc ═ w*xc(ii) a When wxc≤μA+AThen, the risk degree V of the buried gas pipeline is 0; when wxc≥μB-BThen, the danger degree V of the buried gas pipeline is 1; when mu isA+A≤wxc≤μB-BAnd the risk degree V of the buried gas pipeline is as follows:
Figure BDA0002632446160000074
wherein, H (Z)C|ZA) At a given ZAUnder the condition of ZCThe conditional entropy of (a); h (mu)B|ZA) At a given ZAUnder the condition ofBThe conditional entropy of (a); the smaller the risk degree V of the buried gas pipeline is, the smaller the leakage probability of the buried gas pipeline is, and the larger the risk degree V of the buried gas pipeline is, the larger the leakage probability of the buried gas pipeline is.
In this embodiment, the risk V of the buried gas pipeline is a number ranging from 0 to 1, and closer to 0 indicates smaller risk, and closer to 1 indicates larger risk, so that the user can perform preventive maintenance according to the risk.
In this embodiment, the method for generating a preventive maintenance strategy according to the risk of leakage of the buried gas pipeline at the corresponding position includes: controlling the inspection time interval according to the danger degree of the buried gas pipeline, namely T ═ VT1+(1-V)T0(ii) a T is the inspection time interval which should be adopted by the current buried gas pipeline under the current risk degree V, T0The reference inspection time interval T when the current buried gas pipeline has the risk degree V of 01The time interval is patrolled and examined for the benchmark when being 1 for degree of danger V to burying the gas pipeline at present.
In this embodiment, when the risk V > γ, an early warning is started to perform a comprehensive inspection. Gamma is an early warning threshold value, the range is 0.3-0.4, a user can independently select the threshold value setting in the range, the value range of the early warning threshold value is reasonable, and the compromise is well made between the false report avoidance and the report omission avoidance.
Example 2
Fig. 2 is a flow chart of the buried gas pipeline leakage risk prediction method of the present invention.
On the basis of embodiment 1, as shown in fig. 2, this embodiment provides a method for predicting the risk of leakage of a buried gas pipeline, which includes: collecting data and sending the data to a cloud server and/or a processor module; and the cloud server and/or the processor module judges the risk degree of the buried gas pipeline according to the data.
In this embodiment, the cloud server and/or the processor module is adapted to determine the risk level of the buried gas pipeline using the buried gas pipeline leakage risk level prediction algorithm as provided in embodiment 1.
In conclusion, the invention can overcome the defects of strong subjectivity and great randomness of the buried gas pipeline leakage danger judgment and early warning manually according to the monitoring data, is favorable for carrying out early protection and predictive maintenance on buried gas pipeline leakage accidents, has better economical efficiency compared with the later maintenance, and simultaneously reduces the labor cost of buried gas pipeline maintenance.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The system embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (9)

1. The utility model provides a buried gas pipeline leakage danger degree prediction algorithm which characterized in that includes:
collecting data;
establishing a corresponding vector according to the corresponding data;
constructing a buried gas pipeline leakage risk model according to the corresponding vector;
according to the buried gas pipeline leakage risk degree model and the parameters of the corresponding positions on the buried gas pipeline, acquiring the buried gas pipeline leakage risk degree of the corresponding positions in real time;
and generating a preventive maintenance strategy according to the leakage risk of the buried gas pipeline at the corresponding position.
2. The buried gas pipeline leakage risk prediction algorithm of claim 1,
the method for acquiring data comprises the following steps:
collecting stress value and PH value, and calculating stress change rate and PH value change rate according to the stress value and the PH value, i.e. obtaining
The stress change rate is:
Figure FDA0002632446150000011
the change rate of the pH value is as follows:
Figure FDA0002632446150000012
wherein, F0jThe average value of stress measurement F corresponding to the jth node when the buried gas pipeline is just installed or the pipeline state is confirmed to be normaliFor the ith recorded stress value, FRiStress rate of change, PH, recorded for the ith0jThe average value of the PH value measurement corresponding to the jth node when the buried gas pipeline is just installed or the pipeline state is confirmed to be normal is determined, and the PH value isiFor the pH value of the ith record, PHRiThe PH change rate recorded for the ith.
3. The buried gas pipeline leakage risk prediction algorithm of claim 2,
the method for establishing the corresponding vector according to the corresponding data comprises the following steps:
for characteristic stress change rate FRiRate of change of pH value PHRiAnd quantifying, wherein the quantification interval is 0.1, and the service time of the buried gas pipeline is rounded according to the year.
4. A buried gas pipeline leakage risk prediction algorithm as claimed in claim 3,
the method for establishing the corresponding vector according to the corresponding data further comprises the following steps:
establishing a data vector: x ═ x(1)，x(2)，x(3))；
Establishing a coefficient vector: w ═ w (w)(1)，w(2)，w(3))；
Constructing an optimization model, i.e.
Figure FDA0002632446150000021
S.t yi(w.xi+b)≥1-ξi；
ξi≥0 i＝1,2,......,N；
Wherein x is(1)To quantify the rate of change of stress, x(2)To quantify the rate of change of pH, x(3)For the quantized gas pipeline service time, the component of the vector w is the coefficient of the corresponding component of the vector x, and C is a penalty coefficient; x is the number ofiIs the ith training data vector; y isiIs xiWhen y is a class markiWhen the value is-1, leakage occurs in the buried gas pipeline, and when the value is yiWhen the value is 1, the buried gas pipeline is in a normal state; n is the number of training data; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is an offset;
the solution of the optimization model is then: w is a*；
Figure FDA0002632446150000022
Wherein, w*Classifying the normal vector of the hyperplane for the optimal;
Figure FDA0002632446150000024
is the ith element of the solution to the dual problem in the lagrange multiplier vector.
5. A buried gas pipeline leakage risk prediction algorithm as claimed in claim 4,
the method for constructing the buried gas pipeline leakage risk degree model according to the corresponding vector comprises the following steps:
acquiring the mean value and the variance of two data categories of the leakage of the buried gas pipeline and the normal state of the buried gas pipeline, namely acquiring the data obtained after the projection of the stress value and the PH value on the normal vector, namely
Figure FDA0002632446150000023
Figure FDA0002632446150000031
Figure FDA0002632446150000032
Figure FDA0002632446150000033
Wherein N isAThe number of samples in the category y is 1; n is a radical ofBThe number of samples in the category y-1; mu.sBData for occurrence of leakage of buried gas pipeline is shown at w*Mean value of the data obtained after vector axis projection; mu.sANormal data for the buried gas pipeline state is w*Mean value of the data obtained after vector axis projection;Anormal data for the buried gas pipeline state is w*Standard deviation of data obtained after vector axis projection;Bdata for occurrence of leakage of buried gas pipeline is shown at w*Standard deviation of data obtained after vector axis projection;
Figure FDA0002632446150000034
6. a buried gas pipeline leakage risk prediction algorithm as claimed in claim 5,
the method for acquiring the buried gas pipeline leakage risk at the corresponding position in real time according to the buried gas pipeline leakage risk model and the parameters of the corresponding position on the buried gas pipeline comprises the following steps:
let xAFor the sum of sample data for all y ═ 1, ZAIs xAAt w*Projection of vector axis, let current data be xcI.e. by
Zc＝w*xc；
When wxc≤μA+AThen, the risk degree V of the buried gas pipeline is 0;
when wxc≥μB-BThen, the danger degree V of the buried gas pipeline is 1;
when mu isA+A≤wxc≤μB-BAnd the risk degree V of the buried gas pipeline is as follows:
Figure FDA0002632446150000035
wherein, H (Z)C|ZA) At a given ZAUnder the condition of ZCThe conditional entropy of (a);
H(μB|ZA) At a given ZAUnder the condition ofBThe conditional entropy of (a);
the smaller the risk degree V of the buried gas pipeline is, the smaller the leakage probability of the buried gas pipeline is, and the larger the risk degree V of the buried gas pipeline is, the larger the leakage probability of the buried gas pipeline is.
7. The buried gas pipeline leakage risk prediction algorithm of claim 6,
the method for generating the preventive maintenance strategy according to the leakage risk of the buried gas pipeline at the corresponding position comprises the following steps:
controlling inspection time intervals according to the risk of buried gas pipelines, i.e.
T＝VT1+(1-V)T0；
T is the time of inspection that the current buried gas pipeline should adopt under the current danger degree VInter space, T0The reference inspection time interval T when the current buried gas pipeline has the risk degree V of 01The time interval is patrolled and examined for the benchmark when being 1 for degree of danger V to burying the gas pipeline at present.
8. A prediction method for buried gas pipeline leakage risk degree is characterized by comprising the following steps:
collecting data and sending the data to a cloud server and/or a processor module;
and the cloud server and/or the processor module judges the risk degree of the buried gas pipeline according to the data.
9. A buried gas pipeline leakage risk prediction method as defined in claim 8,
the cloud server and/or the processor module is adapted to determine the risk of a buried gas pipeline using a buried gas pipeline leakage risk prediction algorithm as claimed in any one of claims 1 to 7.
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Application publication date: 20201204