CN112001104A - Buried pipeline service performance evaluation method, computer readable medium and equipment - Google Patents
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Abstract
The invention relates to a buried pipeline service performance evaluation method, a computer readable medium and equipment. A buried pipeline service performance evaluation method comprises the following steps: s1, constructing a buried pipeline model by combining the influence variables, and acquiring a regression equation of the pipeline stress and the influence variables; s2, constructing a relation curve between the influence variable and the pipeline stress based on the regression equation; and S3, measuring variable measurement data in the actual working condition of the pipeline, and predicting the service life of the pipeline. The method can directly solve the stress of various buried pipelines under the conditions of multiple diseases and complex service by adopting the established pipeline service performance evaluation model, calculate the contribution percentage of variables contained in the equation to the pipeline stress, and predict the residual life of the pipeline.
Description
Technical Field
The invention relates to the field of pipeline performance evaluation, in particular to a buried pipeline service performance evaluation method, a computer readable medium and equipment.
Background
The buried pipeline built in the early stage has long service life, and various diseases such as pipe bottom void, inner wall corrosion, pipe body cracking, leakage and the like coexist under the coupling action of various internal and external factors, so that the possibility of accidents such as pipe explosion, pavement collapse and the like is increased greatly. Therefore, how to evaluate the service status, the remaining life and the repair priority of the old pipelines becomes a serious problem for the municipal administration.
At present, analytical methods and semi-empirical methods are mainly used for solving the pipeline stress. Although these methods are widely used in practice, they are severely limited in scope by a number of assumptions. For example, three-dimensional effects are ignored, pipe-soil interactions cannot be analyzed using Winkler springs to reflect nonlinear characteristics, and the like. The most common method for evaluating the performance of the buried pipeline in the existing pipeline service performance evaluation methods is to determine defect values according to defect types and severity, then calculate the average value and the maximum value of defect coefficients according to the defect points, wherein the value with the larger value is the structural and functional defect coefficient of the pipeline section, and finally carry out pipeline defect grade division according to the section to which the structural and functional defect coefficients of the pipeline section belong. This evaluation method has the following problems: (1) the real condition of the pipeline defect cannot be reflected by adopting a single length average value index; (2) evaluating only according to the apparent detection result without distinguishing the relation between the defects and the pipeline material; (3) the evaluation process lacks scientific quantification and theoretical basis; (4) the remaining life of the pipeline cannot be predicted. Meanwhile, most of the existing other pipeline service condition evaluation methods are based on statistical methods and reliability theories, the evaluation result is highly dependent on the number of samples, and the service state of the pipeline cannot be truly reflected by the evaluation result under the condition that enough capacity samples are difficult to obtain, so that the method cannot be popularized and applied in a large quantity.
The patent document with the application number of CN 201910159966.2 discloses a method for detecting the service life of a defect-containing buried PE gas pipeline, which is used for testing the gas pipeline to obtain the structural size and the operating parameters of the gas pipeline; intercepting a gas pipeline, and preparing a bent compact sample; stretching the sample at a preset frequency f, wherein the stretching stress in the stretching process directly changes back and forth at a maximum preset stress Fmax and a minimum preset stress Fmin; determining the size of the limit defect according to the first failure principle based on the reliability and finite element analysis and calculation; establishing a fatigue crack propagation model, calculating the fatigue cycle number, and determining the residual life of the buried PE gas pipeline containing the defects. The method can accurately estimate the residual life of the buried PE gas pipeline containing the defects. But still fails to efficiently solve the above problems.
Therefore, the existing field of the evaluation of the performance of the buried pipeline has defects and needs to be improved.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide a buried pipeline service performance evaluation method, a computer readable medium and equipment, which can scientifically quantify the performance result of the buried pipeline and can predict the residual life of the pipeline.
In order to achieve the purpose, the invention adopts the following technical scheme:
a buried pipeline service performance evaluation method comprises the following steps:
s1, constructing a buried pipeline model by combining the influence variables, and acquiring a regression equation of the pipeline stress and the influence variables;
s2, constructing a relation curve between the influence variable and the pipeline stress based on the regression equation;
and S3, measuring variable measurement data in the actual working condition of the pipeline, and predicting the service life of the pipeline.
Preferably, in the method for evaluating service performance of a buried pipeline, the step S1 specifically includes:
s11, constructing a buried pipeline model, simultaneously acquiring influence variables of the service performance of the pipeline, setting different influence variable conditions, and respectively acquiring pipeline stress;
s12, carrying out dimensionless processing on the influence variables in the buried pipeline model, determining the relation between the influence variables and pipeline stress, and constructing a regression equation;
s13, solving the coefficients of the regression equation to enable the prediction precision of the regression equation to meet the prediction requirement.
Preferably, in the method for evaluating service performance of a buried pipeline, the step S12 specifically includes:
s121, carrying out dimensionless processing on the influence variables to obtain dimensionless variables, wherein the number of the dimensionless variables is determined through a pi theory;
and S122, expressing the pipeline stress as a function form of a dimensionless variable, and performing fitting analysis based on a multiple nonlinear regression method to obtain a regression equation.
Preferably, in the buried pipeline service performance evaluation method, in step S13, the regression equation coefficients are solved by using a least square method; and the initial value of the regression equation is selected by using a random selection particle swarm algorithm.
Preferably, in the buried pipeline service performance evaluation method, in step S11, the step of constructing the buried pipeline model includes:
s111, respectively establishing a disease-free pipeline primary model and a disease pipeline primary model of a pipeline soil structure according to design conditions, respectively carrying out sensitivity analysis on the model size and the network size of the disease-free pipeline primary model and the disease pipeline primary model, and adjusting corresponding model parameters;
and S112, performing full-scale tests on the primary model of the disease-free pipeline and the primary model of the disease pipeline respectively, and taking the optimal primary model of the disease pipeline obtained through the tests as a buried pipeline model.
Preferably, in the method for evaluating service performance of a buried pipeline, the step S3 specifically includes:
s31, determining the maximum stress of the pipeline;
s32, determining an influence variable extreme value through the relation curve;
s33, carrying out sensitivity analysis on the influence variables according to the regression equation to obtain the contribution values of the influence variables to the pipeline stress;
s34, acquiring variable measurement data and the annual average increment rate of the defect corresponding to the influence variable;
and S35, obtaining the residual service life of the pipeline.
Preferably, in the method for evaluating service performance of a buried pipeline, the step S33 specifically includes:
s331, carrying out disturbance to a certain degree aiming at one-dimensional variables in the influence variables to obtain new input influence variables;
s332, inputting the input variable into the regression equation, and calculating a Spanish-type level correlation coefficient between an output value before disturbance and an output value after disturbance;
s333, standardizing the Spanish level correlation coefficient to obtain the contribution value of each dimension influence variable to the pipeline stress.
Preferably, in the method for evaluating the service performance of the buried pipeline, the calculation formula of the residual life of the pipeline is as follows:
wherein N is the remaining life; c is the annual average increment rate of the defect corresponding to the influencing variable; ch-criThe maximum stress corresponds to an influence variable extreme value; chData is measured for the current variable.
A computer readable medium stores computer software which, when executed by a computer, implements the buried pipeline service performance assessment method.
An electronic device comprising a processor and a memory; the memory stores computer software; the processor is used for executing the computer software to realize the buried pipeline service performance evaluation method.
Compared with the prior art, the buried pipeline service performance evaluation method, the computer readable medium and the equipment provided by the invention have the following beneficial effects:
1. the method can establish various buried pipeline service performance evaluation models under the condition of multiple diseases and complex service, and overcomes the defects of excessive assumed conditions, insufficient theoretical support and the like compared with the existing pipeline evaluation method;
2. the method can directly solve the stress of various buried pipelines under the conditions of multiple diseases and complex service by adopting the established pipeline service performance evaluation model, calculate the contribution percentage of variables contained in an equation to the pipeline stress, and predict the residual life of the pipeline;
3. the method can be applied to the establishment of various pipeline service performance evaluation models of concrete pipelines, nodular cast iron pipelines, PCCP (prestressed concrete cylinder pipe), HDPE (high-density polyethylene) pipes, PE (polyethylene) pipes and the like, can provide a new idea for solving such problems, and can provide theoretical support for the evaluation of various pipeline service performance by using the established pipeline stress evaluation model for municipal departments, water conservancy departments and the like.
Drawings
FIG. 1 is a flow chart of a buried pipeline service performance evaluation method provided by the invention;
FIG. 2 is a specific flowchart of step S1 in the method for evaluating service performance of a buried pipeline provided by the present invention;
fig. 3 is a flow chart for constructing the buried pipeline model provided by the present invention;
FIG. 4 is a flow chart for obtaining a regression equation provided by the present invention;
FIG. 5 is a specific flowchart of step S3 in the method for evaluating service performance of a buried pipeline according to the present invention;
FIG. 6 is a flow chart of the sensitivity analysis provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, the present invention provides a method for evaluating service performance of a buried pipeline, including the steps of:
s1, constructing a buried pipeline model by combining the influence variables, and acquiring a regression equation of the pipeline stress and the influence variables;
in this embodiment, the buried pipeline model constructed in this step is constructed according to influence variables (such as corrosion, void, crack, groundwater, fault zone, soft foundation, etc.) which are considered to have significant influence on the service performance of the pipeline in the study of the problem of the defect occurring in the pipeline. Preferably, the constructed buried pipeline model is a finite element model. Generally, the method for evaluating service performance of a pipeline provided by the present invention may be used for Concrete pipelines, ductile iron pipelines, PCCP (Prestressed Concrete Cylinder Pipe) pipelines, HDPE (High Density Polyethylene) pipelines, PE (Polyethylene) pipelines, etc., and generally, different pipelines have different influence variables, but the method used is the same and is not further described.
Referring to fig. 2, as a preferred scheme, in this embodiment, the step S1 specifically includes:
s11, constructing a buried pipeline model, simultaneously acquiring influence variables of the service performance of the pipeline, setting different influence variable conditions, and respectively acquiring pipeline stress;
specifically, a full-scale test scheme for verifying the model is designed by referring to a designed finite element model; carrying out a full-scale test to obtain test data; establishing a three-dimensional refined finite element model of the buried pipeline based on finite element software, wherein the three-dimensional refined finite element model can accurately reflect the structural characteristics (such as bell and spigot, rubber ring and the like), defects (such as corrosion, void, crack and the like) and complex service conditions (such as underground water, fault zone, soft foundation and the like) of the pipeline, and simultaneously, the maximum pipeline stress of the corresponding buried pipeline at the moment is obtained according to different service conditions; in the embodiment, the concrete pipeline is modeled according to the corresponding design working condition (for example, corrosion defect condition exists),and calculating the pipeline stress of the concrete pipeline at the moment through the finite element model according to the characteristics of the responded concrete material, setting different design working conditions respectively, inputting the different design working conditions into the finite element model, obtaining a plurality of groups of pipeline stresses corresponding to the different design working conditions, and knowing whether the concrete pipeline model is reasonable or not. The influencing variables include: length of corrosion (C)l) Depth of etching (C)h) Etching width (C)w) Length of crack (L)l) Crack depth (L)h) Void length (V)l) Depth of void (V)h) Void width (V)w) Depth of burial (h), lateral soil pressure coefficient (k), pipe foundation strength (E)b) Strength of backfill (E)c) Underground water level (h)w) And a traffic load (P). Of course, when the finite element model is constructed, corresponding basic parameters are also needed as a framework, wherein the basic parameters include pipe diameter (D), wall thickness (t), total length (L) of the pipeline, elastic modulus (Ep) of the pipeline and soil mass weight (γ). In general, the basic parameter settings are not changed during a single evaluation.
Referring to fig. 3, as a preferred scheme, in step S11, in this embodiment, the step of constructing the buried pipeline model includes:
s111, respectively establishing a disease-free pipeline primary model and a disease pipeline primary model of a pipeline soil structure according to design conditions, respectively carrying out sensitivity analysis on the model size and the network size of the disease-free pipeline primary model and the disease pipeline primary model, and adjusting corresponding model parameters;
specifically, the construction process of the buried pipeline model is as follows:
(1) establishing a model: a pipeline soil structure Model is established in a Model module of ABAQUS, and the material, thickness, support angle, inner diameter, wall thickness, effective length and the like of a pipe base can be determined according to the researched pipeline type and relevant specifications. The pipeline diseases, the complex service conditions and the like can be set item by item according to the design of the design working condition.
(2) Endowing the material with the following properties: endowing Material attributes of each part of the model in a Material module of ABAQUS and inputting related parameters, wherein the soil body usually adopts a nonlinear elastic-plastic constitutive model, such as a Moore-coulomb model, a Duncan-Zhang model and the like; the tube-based material is usually in a linear elastic model; the material composition of the pipe and the relevant parameters are selected according to the type of pipe under study.
(3) Mesh generation: to ensure the quality of the model mesh, the mesh size can be formulated
Wherein, CsFor shear wave velocity (m/s), Δ l is the maximum grid cell size (m), f is the excitation frequency (Hz), and ω is the cycle frequency of the excitation (rad/s). In addition, the soil body grids close to the pipeline part are encrypted, the pipeline and the dry soil body can adopt a three-dimensional entity eight-node reduction integral unit (C3D8R), the saturated soil can adopt a three-dimensional entity eight-node stress-hole pressure coupling reduction integral unit (C3D8RP), the rubber sealing ring can adopt a three-dimensional entity eight-node coordination reduction integral unit (C3D8RH), and in addition, hourglass control needs to be carried out on all grids.
(4) Contact attribute setting: the method is characterized in that model contact attributes are set in Interaction modules of ABAQUS, specifically, surface-to-surface contact or point-to-surface contact is set at contact interfaces of pipe-soil, socket-rubber ring and socket-rubber ring, tangential friction contact models can be selected from Penalty, Frititionless, Rough, Lagrange Multiplie and the like, normal friction attributes can be selected from Hard, Exponential, Linear and the like, and a proper friction model can be selected according to the type of a researched pipeline.
(5) Setting boundary conditions: if power calculation is adopted, the model boundary adopts a viscoelastic-plastic artificial power boundary or an infinite element power boundary condition so as to reduce the superposition effect of stress waves at the boundary; if static calculation is adopted, the normal displacement freedom degrees of four side surfaces, the bottom surface and two ends of the pipeline of the calculation model are limited; considering the influence of underground water, the interface of saturated soil and dry soil and saturated soil and pipeline is set as the wetting surface, the pore pressure on the wetting surface is 0, and the boundary condition of pore pressure is p ═ rhow×g×(z0-hi) Set up where pwIs the density of water, g is the acceleration of gravity, z0Is a vertical seat with 0 hole pressure surfaceValue of mark, hiIs the vertical coordinate value of any point below the 0-hole pressure surface.
(6) And (3) ground stress balance: creating a geostress balance analysis Step (Geostatic) in a Step module, wherein the type is 'Fixed', adding gravity in the z direction to the whole model in a Load module, and submitting calculation to obtain a result file geo. And copying a Model (Model 2), keeping all settings unchanged, importing geo. odb in a predefined field in the Load module, resubmitting calculation, realizing ground stress balance, when the displacement of the balanced pipeline is less than 1e-05, indicating that the balance effect is good, and otherwise, repeatedly importing the odb file newly obtained each time until the requirement is met.
(7) Setting an analysis step: model 2 is copied, the geostress balance analysis step is preserved, and other analysis steps required for calculation are created behind it.
(8) Setting a load: in the Load module, the ground stress analysis step still applies gravity, and the rest analysis steps set corresponding Load variables and Load amplitudes according to the design working conditions in S11.
(9) And (4) creating a calculation name in a Job module, and clicking submission (Submit) to calculate to obtain the pipeline stress of the current model.
And S112, performing full-scale tests on the primary model of the disease-free pipeline and the primary model of the disease pipeline respectively, and taking the optimal primary model of the disease pipeline obtained through the tests as a buried pipeline model.
Specifically, sensitivity analysis is carried out on the model size, the grid size, the unit type, the friction attribute and the like, the reliability of the model is verified according to test data, and parameters of the buried pipeline model are adjusted according to the analysis result. All buried pipeline models obtained according to different design working conditions are submitted to calculation to obtain corresponding pipeline stress, the output pipeline stress is determined by pipeline materials, the maximum main stress is generally output for brittle materials such as concrete and the like, and Mises stress is output for elastoplastic materials such as ductile cast iron pipes and the like. It should be noted that the buried pipeline model is obtained by optimizing a primary model of a diseased pipeline, and when corresponding disease data are all empty, the apparent characteristics of the buried pipeline model are similar to those of the primary model of the disease-free pipeline.
S12, carrying out dimensionless processing on the influence variables in the buried pipeline model, determining the relation between the influence variables and pipeline stress, and constructing a regression equation;
referring to fig. 4, as a preferred scheme, in this embodiment, the step S12 specifically includes:
s121, carrying out dimensionless processing on the influence variables to obtain dimensionless variables, wherein the number of the dimensionless variables is determined through a pi theory;
and S122, expressing the pipeline stress as a function form of a dimensionless variable, and performing fitting analysis based on a multiple nonlinear regression method to obtain a regression equation.
Specifically, all the influence variables in the buried pipeline model are subjected to dimensionless processing, the number of the dimensionless variables is determined by pi theory, and the form of the dimensionless variables can be specifically determined according to the physical meaning of the variables, for example, the corresponding form of the dimensionless variables in the concrete pipeline can be:
the pipeline stress is expressed as a function of a dimensionless variable, which may be:
wherein σmaxIs the maximum stress; alpha is alpha1、……α14Evaluating coefficients of the expression equation for the pipeline; beta is a1、……β14Evaluating an index parameter of an expression equation for the pipeline; clIs the length of etching, ChIs depth of corrosion, CwIs the etching width, LlIs the length of the crack, LhDepth of crack, VlFor a void length, VhFor the depth of void, VwIs void width, h is buried depth, k is lateral soil pressure coefficient, EbIs the tube base strength, EcFor the strength of backfill soil, hwIs the underground water level, P is the traffic load; d is pipe diameter, t is wall thickness, L is total length of pipeline, EpThe elastic modulus of the pipeline is shown, and the weight gamma is the soil body weight.
S13, solving the coefficients of the regression equation to enable the prediction precision of the regression equation to meet the prediction requirement. And verifying the prediction precision of the model by using test data or a newly established finite element model, so that the prediction precision of the regression model established by the regression equation meets the prediction requirement.
Preferably, in this embodiment, in the step S13, the regression equation coefficients are solved by using a least square method; and the initial value of the regression equation is selected by using a random selection particle swarm algorithm.
Specifically, the solving step of the regression coefficient of the regression model formed by solving the regression equation is as follows:
reading all influence variables of the regression model;
carrying out non-dimensionalization processing on all the influence variables, and defining the input and the output of the regression model;
and solving the model regression coefficient by using a least square method principle, wherein the initial value can be selected by adopting a random selection method or a particle swarm algorithm.
S2, constructing a relation curve between the influence variable and the pipeline stress based on the regression equation;
and S3, measuring variable measurement data in the actual working condition of the pipeline, and predicting the service life of the pipeline.
Specifically, the step of predicting the remaining life of the pipeline takes a corroded concrete pipeline as an example, the rest variables in the regression model are kept unchanged, the relation between the corrosion depth and the maximum main stress of the concrete pipeline is calculated, the corrosion depth corresponding to the ultimate tensile strength of the concrete pipeline is found, and the remaining life of the corroded concrete pipeline can be predicted by utilizing the annual growth rate of the corrosion depth. Of course, the relationship between the rest variables in the proposed model and the mechanical response of the pipeline can be calculated to predict the remaining life of the pipeline. The method is similar for other types of pipes.
Referring to fig. 5, as a preferred scheme, in this embodiment, the step S3 specifically includes:
s31, determining the maximum stress of the pipeline; the acquisition mode of the maximum stress of the pipeline is obtained by a finite element analysis method of a buried pipeline model according to different pipeline materials,
s32, determining an influence variable extreme value through the relation curve;
s33, carrying out sensitivity analysis on the influence variables according to the regression equation to obtain the contribution values of the influence variables to the pipeline stress;
referring to fig. 6, as a preferred scheme, in this embodiment, the step S33 specifically includes:
s331, carrying out disturbance to a certain degree aiming at one-dimensional variables in the influence variables to obtain new input influence variables; carrying out certain disturbance on one-dimensional vectors (namely influence variables of which contribution values need to be solved) to obtain new input variables; in specific implementation, if the contribution values of all the influencing variables need to be obtained, all the influencing variables need to be disturbed respectively; it should be noted here that the preferred disturbance amplitude is 5% -20%, more preferably 10% of the respective influencing variable;
s332, inputting the input variable into the regression equation, and calculating a Spanish-type level correlation coefficient between an output value before disturbance and an output value after disturbance; calculating output values (namely pipeline stress) corresponding to input variables (namely all influence variables in corresponding design working conditions) according to the regression model;
s333, standardizing the Spanish level correlation coefficient to obtain the contribution value of each dimension influence variable to the pipeline stress. Calculating a spearman grade correlation coefficient between the pipeline stress output value under the condition of the design working condition before disturbance and the pipeline stress output value under the condition of the design working condition after disturbance; and (4) normalizing the spearman grade correlation coefficient to obtain the relative contribution percentage of each control variable to the maximum stress of the pipeline. Here, the corresponding spearman coefficient evaluation and normalization are realized by using spearman coefficient correlation technique, and the present invention is not limited.
S34, acquiring variable measurement data and the annual average increment rate of the defect corresponding to the influence variable;
and S35, obtaining the residual service life of the pipeline.
As a preferable scheme, in this embodiment, the calculation formula of the remaining life of the pipeline is as follows:
wherein N is the remaining life; c is the annual average increment rate of the defect corresponding to the influencing variable; ch-criThe maximum stress corresponds to an influence variable extreme value; chData is measured for the current variable.
In conclusion, the method can establish various buried pipeline service performance evaluation models under the conditions of multiple diseases and complex service, and overcomes the defects of excessive assumed conditions, insufficient theoretical support and the like compared with the conventional pipeline evaluation method. The pipeline service performance evaluation model established by the method provided by the invention can directly solve the stress of various buried pipelines under the conditions of multiple diseases and complex service, calculate the contribution percentage of variables contained in the equation to the pipeline stress, and predict the residual life of the pipeline. The method can be applied to the establishment of various pipeline service performance evaluation models of concrete pipelines, nodular cast iron pipelines, PCCP pipes, HDPE pipes, PE pipes and the like, can provide a new idea for solving the problems, and can provide theoretical support for the evaluation of the service performance of various pipelines by using the established pipeline stress evaluation model for municipal departments, water conservancy departments and the like.
Correspondingly, the invention also provides a computer readable medium, which stores computer software, and when the computer software is executed by a computer, the method for evaluating the service performance of the buried pipeline is realized. Specifically, the readable medium may be a stand-alone medium or an internal component of the electronic device, and the present invention is not limited thereto. When the readable medium is an independent medium, the readable medium can be connected with an upper computer (the upper computer is provided with a processor capable of running the computer software) for use.
The invention also provides an electronic device comprising a processor and a memory; the memory stores computer software; the processor is used for executing the computer software to realize the buried pipeline service performance evaluation method. Specifically, the number of processors is not limited, and one or more of the processors may execute the computer software in the memory, alone or in combination; the memory may be a readable medium provided by the present invention, or may be other devices in the field, and the number of the memory is not limited, and one or more of the memory may be provided, and each memory stores the computer software.
It should be understood that equivalents and modifications of the technical solution and inventive concept thereof may occur to those skilled in the art, and all such modifications and alterations should fall within the scope of the appended claims.
Claims (10)
1. A buried pipeline service performance evaluation method is characterized by comprising the following steps:
s1, constructing a buried pipeline model by combining the influence variables, and acquiring a regression equation of the pipeline stress and the influence variables;
s2, constructing a relation curve between the influence variable and the pipeline stress based on the regression equation;
and S3, measuring variable measurement data in the actual working condition of the pipeline, and predicting the service life of the pipeline.
2. The method for evaluating the service performance of the buried pipeline according to claim 1, wherein the step S1 specifically comprises:
s11, constructing a buried pipeline model, simultaneously acquiring influence variables of the service performance of the pipeline, setting different influence variable conditions, and respectively acquiring pipeline stress;
s12, carrying out dimensionless processing on the influence variables in the buried pipeline model, determining the relation between the influence variables and pipeline stress, and constructing a regression equation;
s13, solving the coefficients of the regression equation to enable the prediction precision of the regression equation to meet the prediction requirement.
3. The method for evaluating the service performance of the buried pipeline according to claim 2, wherein the step S12 specifically comprises:
s121, carrying out dimensionless processing on the influence variables to obtain dimensionless variables, wherein the number of the dimensionless variables is determined through a pi theory;
and S122, expressing the pipeline stress as a function form of a dimensionless variable, and performing fitting analysis based on a multiple nonlinear regression method to obtain a regression equation.
4. The buried pipeline service performance evaluation method of claim 2, wherein in the step S13, the regression equation coefficients are solved by using a least square method; and the initial value of the regression equation is selected by using a random selection particle swarm algorithm.
5. The buried pipeline service performance evaluation method of claim 2, wherein in step S11, the step of constructing the buried pipeline model comprises:
s111, respectively establishing a disease-free pipeline primary model and a disease pipeline primary model of a pipeline soil structure according to design conditions, respectively carrying out sensitivity analysis on the model size and the network size of the disease-free pipeline primary model and the disease pipeline primary model, and adjusting corresponding model parameters;
and S112, performing full-scale tests on the primary model of the disease-free pipeline and the primary model of the disease pipeline respectively, and taking the optimal primary model of the disease pipeline obtained through the tests as a buried pipeline model.
6. The method for evaluating the service performance of the buried pipeline according to claim 1, wherein the step S3 specifically comprises:
s31, determining the maximum stress of the pipeline;
s32, determining an influence variable extreme value through the relation curve;
s33, carrying out sensitivity analysis on the influence variables according to the regression equation to obtain the contribution values of the influence variables to the pipeline stress;
s34, acquiring variable measurement data and the annual average increment rate of the defect corresponding to the influence variable;
and S35, obtaining the residual service life of the pipeline.
7. The method for evaluating the service performance of the buried pipeline according to claim 6, wherein the step S33 specifically comprises:
s331, carrying out disturbance to a certain degree aiming at one-dimensional variables in the influence variables to obtain new input influence variables;
s332, inputting the input variable into the regression equation, and calculating a Spanish-type level correlation coefficient between an output value before disturbance and an output value after disturbance;
s333, standardizing the Spanish level correlation coefficient to obtain the contribution value of each dimension influence variable to the pipeline stress.
8. The buried pipeline service performance evaluation method of claim 6, wherein the formula for calculating the residual life of the pipeline is as follows:
wherein N is the remaining life; c is the annual average increment rate of the defect corresponding to the influencing variable; ch-criThe maximum stress corresponds to an influence variable extreme value; chData is measured for the current variable.
9. A computer readable medium storing computer software, wherein the computer software, when executed by a computer, implements the method of evaluating the service performance of a buried pipeline according to any one of claims 1 to 8.
10. An electronic device comprising a processor and a memory; the memory stores computer software; the processor is used for executing the computer software to realize the buried pipeline service performance evaluation method of any one of claims 1 to 8.
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