Method and device for predicting service life of metal pipeline, electronic equipment and storage medium

Abstract

The present application relates to the technical field of pipeline testing, and particularly relates to a metal pipeline service life prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: firstly, a plurality of soil corrosion factors are obtained; then, the plurality of soil corrosion factors are input into a corrosion rate prediction model to obtain an expected long-term average corrosion rate; then, a pipeline corrosion allowance is determined according to a plurality of parameter characteristics of the pipeline; finally, an expected service life is determined according to the corrosion allowance and the expected long-term average corrosion rate, and / or an allowable working pressure of a predetermined term is determined according to the expected long-term average corrosion rate. According to the embodiment of the present application, the influencing factors are input into the prediction model to obtain the long-term corrosion rate, the corrosion allowance is determined according to the parameters of the pipeline, and the service life of the metal pipeline can be accurately evaluated according to the corrosion rate and the corrosion allowance. Therefore, the pipeline meeting the user's demand can be designed according to the soil properties, and the demand of the customer can be met.

Description

Technical Field

[0001] This invention relates to the field of pipeline testing technology, and in particular to a method, apparatus, electronic device, and storage medium for predicting the service life of metal pipelines. Background Technology

[0002] Currently, publicly available data on the service life of metal pipelines, especially ductile iron pipelines, is limited. Furthermore, buried ductile iron pipelines experience corrosion of their outer coating and pipe walls as their service life increases. While minor corrosion has no impact on pipeline use, severe corrosion has a significant impact, frequently causing pipe perforation or even rupture, resulting in substantial economic losses and social consequences. Therefore, a systematic study of the corrosion resistance of ductile iron pipes under different soil conditions and an assessment of their expected service life are particularly important.

[0003] On the other hand, ductile iron pipe users need to select ductile iron pipe grades that meet the engineering design life requirements based on the specific soil corrosion environment. Therefore, it is urgent and important to study and evaluate the expected service life of ductile iron pipe systems.

[0004] Existing research on pipeline corrosion and service life largely focuses on steel pipes. This research studies steel pipes operating in actual corrosive environments for several years, calculating and assessing their remaining service life based on monitored corrosion rates. Alternatively, it uses finite element analysis software to calculate stress based on the geometry and shape of corrosion pits on the pipe's outer wall, thus determining the remaining service life. However, research on the service life of ductile iron pipes is almost nonexistent. This is primarily because ductile iron pipes and steel pipes have different compositions and corrosion patterns.

[0005] Because many factors influence soil corrosivity, including soil texture, oxygen content, soil resistivity, and Cl-, etc. - SO4 2- Factors such as water content, pH value, and salt content must be considered. Therefore, it is necessary to systematically study the corrosion behavior of ductile iron pipes in soil environments and calculate and evaluate the expected service life of ductile iron pipes under given soil physicochemical property parameters.

[0006] Therefore, it is necessary to develop a method for predicting the service life of metal pipes. Summary of the Invention

[0007] The present invention provides a method, apparatus, electronic device and storage medium for predicting the service life of metal pipelines, which solves the problem that the service life of metal pipelines is not easy to determine in the prior art.

[0008] In a first aspect, embodiments of the present invention provide a method for predicting the service life of metal pipelines, including:

[0009] Multiple soil corrosion factors are obtained, wherein the soil corrosion factors characterize the factors that affect the corrosion rate of the pipeline at the pipeline laying site;

[0010] The multiple soil corrosion factors are input into the corrosion rate prediction model to obtain the expected long-term average corrosion rate.

[0011] The corrosion allowance of the pipeline is determined based on multiple parameter characteristics of the pipeline, wherein the parameter characteristics represent the pipeline parameters that affect the corrosion allowance of the pipeline.

[0012] The expected service life is determined based on the corrosion allowance and the expected long-term average corrosion rate, and / or the allowable operating pressure for a predetermined number of years is determined based on the expected long-term average corrosion rate.

[0013] In one possible implementation, the construction of the corrosion rate prediction model includes:

[0014] An initial model was obtained by constructing an Elman neural network model.

[0015] The corrosion rate prediction model is obtained by training the initial model, and the training process includes:

[0016] Multiple corrosion data points were acquired, including long-term average corrosion rates and multiple soil corrosion factors.

[0017] The multiple corrosion data are split according to a predetermined ratio to obtain a training dataset and a test dataset;

[0018] Training steps: Input multiple corrosion data from the training dataset into the initial model, and adjust the parameters of the initial model based on multiple outputs of the initial model and multiple long-term corrosion rates corresponding to the multiple corrosion data in the training dataset, until the output error of the initial model is lower than the threshold.

[0019] The parameters of the initial model are fixed, and multiple corrosion data from the test dataset are input into the initial model. The test error is determined by multiple outputs of the initial model and multiple long-term corrosion rates corresponding to the multiple corrosion data in the test dataset.

[0020] If the test error is higher than the threshold, the number of nodes in the hidden layer of the initial model is increased, and the process jumps to the training step.

[0021] In one possible implementation, the multiple corrosion data are obtained based on experimental data from typical corrosive soil areas and corrosion data from actually buried pipelines. The method for obtaining the multiple corrosion data from the corrosion data of actually buried pipelines includes:

[0022] Multiple samples were buried in a typical corrosive soil area;

[0023] The multiple samples were excavated in sequence at multiple different time points;

[0024] The multiple samples were analyzed to obtain multiple maximum corrosion pit depths, wherein the multiple maximum corrosion pit depths correspond to multiple different time points, and the maximum corrosion pit depth is the maximum corrosion pit depth on the sample.

[0025] Multiple maximum corrosion rates are determined based on the multiple different time points and the multiple maximum corrosion pit depths, wherein the multiple maximum corrosion rates correspond to the multiple different time points;

[0026] Based on the multiple different time points and the multiple maximum corrosion rates, a corrosion rate prediction function is established.

[0027] The corrosion rate of the actual buried pipeline is obtained by normalizing the corrosion rate prediction function and the actual corrosion rate of the buried pipeline.

[0028] To obtain multiple corrosive factors of the soil in which the pipeline was actually buried;

[0029] The corrosion rate of the actual buried pipeline and multiple corrosion factors of the soil containing the actual buried pipeline are used as the multiple corrosion data.

[0030] In one possible implementation, establishing the corrosion rate prediction function based on the plurality of different time points and the plurality of maximum corrosion rates includes:

[0031] Based on the multiple different time points and the multiple maximum corrosion rates, a corrosion rate prediction function is established, which is:

[0032] V = a·t b

[0033] In the formula, V is the corrosion rate in a typical corrosive soil area, a is a precondition constant, b is a postcondition constant, and t is time.

[0034] The corrosion rate of the actual buried pipeline is obtained by normalizing the corrosion rate based on the corrosion rate prediction function, the first formula, and the actual corrosion rate of the buried pipeline. The first formula is:

[0035]

[0036] In the formula, is the normalized corrosion rate of the actual buried pipeline, t1 is the time node when the actual buried pipeline is sampled, V0 is the corrosion rate of the longest time node corresponding to the highly corrosive soil area, and V1 is the corrosion rate of the actual buried pipeline at time node t1.

[0037] In one possible implementation, determining the corrosion allowance of the pipeline based on multiple parameter characteristics of the pipeline includes:

[0038] The corrosion allowance of the pipeline is determined based on the minimum wall thickness, actual allowable working pressure, safety factor, outer diameter, tensile strength of the pipeline material, and the second formula, wherein the second formula is:

[0039]

[0040] In the formula, h is the corrosion allowance of the pipeline, and e min For the minimum wall thickness of the pipe, PFA 实际 The actual allowable working pressure is given by SF, where SF is the safety factor for the actual allowable working pressure, DE is the outer diameter of the pipe, and R is the outer diameter of the pipe. m This refers to the tensile strength of the pipe material.

[0041] In one possible implementation, determining the expected service life based on the corrosion allowance and the expected long-term average corrosion rate includes:

[0042] The expected service life is determined based on the third formula, the corrosion allowance, and the expected long-term average corrosion rate. The third formula is:

[0043]

[0044] In the formula, L is the expected service life, h is the corrosion allowance of the pipeline, and V3 is the expected long-term average corrosion rate.

[0045] In one possible implementation, determining the permissible operating pressure for a predetermined number of years based on the expected long-term average corrosion rate includes...

[0046] Based on the expected long-term average corrosion rate, the minimum wall thickness of the pipeline, the tensile strength of the pipeline material, the outer diameter of the pipeline, and the fourth formula, the allowable working pressure for the predetermined number of years is determined. The fourth formula is:

[0047]

[0048] In the formula, p is the allowable working pressure for T0 years, e min V3 is the minimum wall thickness of the pipe, V3 is the expected long-term average corrosion rate, and R is the mean wall thickness of the pipe. m DE represents the tensile strength of the pipe material and the outer diameter of the pipe.

[0049] Secondly, embodiments of the present invention provide a metal pipeline service life prediction device, comprising:

[0050] The corrosion factor acquisition module is used to acquire multiple soil corrosion factors, wherein the soil corrosion factors characterize the factors that affect the corrosion rate of the pipeline at the pipeline laying site;

[0051] The long-term average corrosion rate prediction module is used to input the multiple soil corrosion factors into the corrosion rate prediction model to obtain the expected long-term average corrosion rate.

[0052] The corrosion allowance determination module is used to determine the corrosion allowance of the pipeline based on multiple parameter characteristics of the pipeline, wherein the parameter characteristics characterize the pipeline parameters that affect the corrosion allowance of the pipeline.

[0053] as well as,

[0054] The service life prediction module is used to determine the expected service life based on the corrosion allowance and the expected long-term average corrosion rate, and / or to determine the allowable operating pressure for a predetermined number of years based on the expected long-term average corrosion rate.

[0055] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0056] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0057] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0058] This invention discloses a method for predicting the service life of metal pipelines. First, it acquires multiple soil corrosion factors, whereby these factors characterize the factors affecting the corrosion rate of the pipeline due to its location. Then, it inputs these soil corrosion factors into a corrosion rate prediction model to obtain the expected long-term average corrosion rate. Next, based on multiple parameter characteristics of the pipeline, it determines the pipeline's corrosion margin, whereby these parameter characteristics characterize pipeline parameters affecting the corrosion margin. Finally, it determines the expected service life and / or the allowable operating pressure for a predetermined number of years based on the corrosion margin and the expected long-term average corrosion rate. This invention, by inputting influencing factors into a prediction model to obtain the long-term corrosion rate and determining the corrosion margin based on pipeline parameters, can accurately assess the service life of metal pipelines based on the corrosion rate and corrosion margin. Therefore, it allows for the design of pipelines that meet user needs based on soil properties, satisfying customer requirements.

[0059] 1. Once the physical and chemical properties of the soil are determined, the expected service life of ductile iron pipes can be scientifically and reasonably calculated and evaluated to meet user needs.

[0060] 2. Based on the specific design life requirements of the pipeline, the wall thickness level of the ductile iron pipeline can be scientifically and rationally selected, saving metal materials, reducing costs, and creating huge economic benefits; at the same time, it improves material utilization, is green and low-cost, low-carbon and environmentally friendly, and significantly reduces carbon emissions, setting a benchmark for the industry and driving the green and healthy development of the ductile iron pipeline industry.

[0061] 3. Using this method to calculate and evaluate the expected service life of ductile iron pipes and to make scientific material selection can avoid water leakage accidents during the service life due to unreasonable pipe material selection. It can significantly reduce the excavation and maintenance costs of municipal underground water pipes, reduce water waste caused by pipe leakage, and play a good role in protecting the environment. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart of the metal pipeline service life prediction method provided by the embodiments of the present invention;

[0064] Figure 2 This is a basic structural diagram of the Elman network model provided in the embodiments of the present invention;

[0065] Figure 3 This is a functional block diagram of the metal pipe service life prediction device provided in the embodiments of the present invention;

[0066] Figure 4 This is a functional block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0067] In the following description, specific details such as particular system structures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0068] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0069] The embodiments of the present invention will be described in detail below. This example is implemented based on the technical solution of the present invention, and provides detailed implementation methods and specific operation processes. However, the protection scope of the present invention is not limited to the following embodiments.

[0070] Figure 1 A flowchart of a method for predicting the service life of metal pipelines provided in an embodiment of the present invention.

[0071] like Figure 1 As shown, a flowchart illustrating the implementation of the metal pipeline service life prediction method provided by an embodiment of the present invention is illustrated below:

[0072] In step 101, multiple soil corrosion factors are obtained, wherein the soil corrosion factors characterize the factors that affect the corrosion rate of the pipeline at the pipeline laying location.

[0073] In step 102, the multiple soil corrosion factors are input into the corrosion rate prediction model to obtain the expected long-term average corrosion rate.

[0074] In some implementations, the construction of the corrosion rate prediction model includes:

[0075] An initial model was obtained by constructing an Elman neural network model.

[0076] The corrosion rate prediction model is obtained by training the initial model, and the training process includes:

[0077] Multiple corrosion data points were acquired, including long-term average corrosion rates and multiple soil corrosion factors.

[0078] The multiple corrosion data are split according to a predetermined ratio to obtain a training dataset and a test dataset;

[0079] Training steps: Input multiple corrosion data from the training dataset into the initial model, and adjust the parameters of the initial model based on multiple outputs of the initial model and multiple long-term corrosion rates corresponding to the multiple corrosion data in the training dataset, until the output error of the initial model is lower than the threshold.

[0080] The parameters of the initial model are fixed, and multiple corrosion data from the test dataset are input into the initial model. The test error is determined by multiple outputs of the initial model and multiple long-term corrosion rates corresponding to the multiple corrosion data in the test dataset.

[0081] If the test error is higher than the threshold, the number of nodes in the hidden layer of the initial model is increased, and the process jumps to the training step.

[0082] In some embodiments, the multiple corrosion data are obtained based on experimental data from typical corrosive soil areas and corrosion data from actually buried pipelines. The method for obtaining the multiple corrosion data from the corrosion data of actually buried pipelines includes:

[0083] Multiple samples were buried in a typical corrosive soil area;

[0084] The multiple samples were excavated in sequence at multiple different time points;

[0085] The multiple samples were analyzed to obtain multiple maximum corrosion pit depths, wherein the multiple maximum corrosion pit depths correspond to multiple different time points, and the maximum corrosion pit depth is the maximum corrosion pit depth on the sample.

[0086] Multiple maximum corrosion rates are determined based on the multiple different time points and the multiple maximum corrosion pit depths, wherein the multiple maximum corrosion rates correspond to the multiple different time points;

[0087] Based on the multiple different time points and the multiple maximum corrosion rates, a corrosion rate prediction function is established.

[0088] The corrosion rate of the actual buried pipeline is obtained by normalizing the corrosion rate prediction function and the actual corrosion rate of the buried pipeline.

[0089] To obtain multiple corrosive factors of the soil in which the pipeline was actually buried;

[0090] The corrosion rate of the actual buried pipeline and multiple corrosion factors of the soil containing the actual buried pipeline are used as the multiple corrosion data.

[0091] In some implementations, establishing a corrosion rate prediction function based on the plurality of different time points and the plurality of maximum corrosion rates includes:

[0092] Based on the multiple different time points and the multiple maximum corrosion rates, a corrosion rate prediction function is established, which is:

[0093] V = a·t b

[0094] In the formula, V is the corrosion rate in a typical corrosive soil area, a is a precondition constant, b is a postcondition constant, and t is time.

[0095] The corrosion rate of the actual buried pipeline is obtained by normalizing the corrosion rate based on the corrosion rate prediction function, the first formula, and the actual corrosion rate of the buried pipeline. The first formula is:

[0096]

[0097] In the formula, is the normalized corrosion rate of the actual buried pipeline, t1 is the time node when the actual buried pipeline is sampled, V0 is the corrosion rate of the longest time node corresponding to the typical corrosive soil area, and V1 is the corrosion rate of the actual buried pipeline at time node t1.

[0098] For example, soil corrosion factors typically include several physicochemical factors, such as soil resistivity, redox potential, and Cl-. - SO4 2- By inputting factors such as water content, pH value, and salinity into the model as dependent variables, the expected long-term average corrosion rate can be obtained.

[0099] In some applications, prediction models are built based on Elman neural network models, which generally consist of four layers: input layer, hidden layers (intermediate layers), connecting layers, and output layer. For example... Figure 2 As shown, the connections between the input layer, hidden layer, and output layer are similar to those of a feedforward network. The Elman neural network is a typical dynamic neural network. Based on the basic structure of a backpropagation (BP) network, it stores internal states to enable it to map dynamic features, thus allowing the system to adapt to time-varying characteristics. Due to the limited number of training samples, relatively large prediction errors are possible, which can be avoided by increasing the sample size and pre-filtering erroneous data.

[0100] In this embodiment of the invention, a network model net is established using a computer. The hierarchical structure of the net model includes an input layer, a hidden layer, a receiving layer, and an output layer.

[0101] Regarding node settings, first determine the number of influencing factor nodes a0 in the input layer, such as 7 soil corrosion factors (soil resistivity, redox potential, Cl). - SO4 2- (Water content, pH value, salt content), at this time a0 = 7, that is, select a0 corresponding factors from the soil corrosion factors.

[0102] Then determine the number of nodes b0 in the output layer, such as the long-term average corrosion rate of ductile iron pipes. At this time, b0 = 1 (b0 is a defined value, indicating that the output data port is 1, a fixed value).

[0103] Next, the range of hidden layer nodes is determined. The rules for determining the number of hidden layer nodes are as follows:

[0104] S = (a0 + b0) * 0.4 + c

[0105] Where S is the number of hidden layer nodes, a0 is the number of input layer nodes, b0 is the number of output layer nodes, and the constant c = 2 to 9. After calculation, the range of the number of hidden layer nodes is [m1, m2].

[0106] Next, the network is built, trained, and debugged. The number of hidden layer nodes is determined to be m1, and the hidden layer function is...

[0107]

[0108] or

[0109]

[0110] The output layer function is:

[0111] f(x) = kx + d

[0112] Where a′ is a positive real number, and the determination of the values ​​of d and k is to determine the parameters of the model.

[0113] Finally, network training was started after setting the network learning rate to 0.1–0.001, the number of training iterations to 1,000–100,000, and the error to 0.1–0.0001.

[0114] Once the set error is reached within the set number of training iterations, the network training is complete. After testing and verification, the network parameters can be saved. Otherwise, return to the network creation step, increment the number of hidden layer nodes by 1, and recreate the network, hidden layer function, and output layer function until the network converges successfully and is tested and verified. Then, save the network parameters and name the saved network net1.

[0115] For the samples used to train the above prediction model, in some application scenarios, D1, D2, D3, ..., Dm typical corrosive soil areas are selected as experimental sites for burying metal pipes, where m is 4 to 20. In each area, 4 to 15 test pits are dug to bury the samples and prepare the required metal pipe samples.

[0116] The substrate material for the metal tube sample is a metal tube, such as ductile iron or steel. First, a zinc coating is applied to the outside of the metal tube by electric arc spraying. The zinc coating has a unit weight of 130 g / m³. 2 ~200g / m 2 Then, an organic finishing coating is sprayed onto the zinc coating, with a thickness of 70–150 micrometers. (The presence of the coating is not a sufficient or necessary condition, but only one of the factors).

[0117] Then, in the processing workshop, the above-mentioned pipes are processed into the required sample size, with the sample size being a DN100×100mm pipe section. For each type of coating, 3–10 parallel samples are prepared per cycle, and the samples are sealed at the edges. The coated samples are dried for one week to ensure complete drying before packaging. The packaged samples are then transported to the aforementioned typical soil testing stations. The samples are unpacked and placed horizontally at the bottom of the test pit according to their type, secured with soil, and compacted. The spacing between samples and the edge of the test pit should be greater than 150mm, ensuring that the soil around the sample is dense and without gaps. The excavated soil is then backfilled in the original order.

[0118] Next, excavation and testing were carried out sequentially according to the predetermined excavation cycle T1, T2, T3, ..., Tn. The excavation cycle n can be set to 3 to 12 cycles, following the principle of denser excavation followed by sparser excavation (the interval gradually increases), and can be 0.082 years, 0.164 years, 0.247 years, and 7 years. Alternatively, it can be 0.082 years, 0.164 years, 0.247 years, 0.493 years, 1 year, and 7 years. After excavating the above samples in each cycle, the soil on the samples was carefully removed until completely clean. If the soil was strongly adhered and difficult to wipe off, a soft plastic brush with a suitable amount of water could be used to gently wash the soil off. The samples were then placed in a drying oven to dry for 24 hours. After drying, the samples were taken from the area with the largest pitting corrosion, and the maximum pitting depth data of the area with the largest pitting corrosion was detected using a SEM scanning electron microscope.

[0119] Next, the maximum corrosion pit depth data of samples from different periods T1, T2, T3, ..., Tn are converted into the maximum corrosion rate data V1, V2, V3, ..., Vn for the corresponding periods;

[0120] Using the maximum corrosion rate data at locations D1, D2, D3, ..., Dm with different corrosion periods T1, T2, T3, ..., Tn, m corrosion rate values ​​of the form: V = a·t b The corrosion rate is expected to be related by a function, where V is the corrosion rate, t is time, and a and b are constants.

[0121] The soil environment with the highest corrosion rate at each location under the longest period Tn is defined as the most severe corrosion environment, and its corrosion rate model is designated as the reference model. For example, the corrosion rate at location D3 is the highest under Tn conditions, so the corrosion rate model at location D3 is designated as the reference model, and its corrosion rate under the longest period Tn conditions is denoted as V0.

[0122] The corrosion rates V1 obtained from excavation and testing of pipelines with different ages in various locations, such as location A in year t1, are normalized to the corrosion rate data under the longest period Tn condition. The normalization method is as follows:

[0123]

[0124] V a The normalized corrosion rate of the actual buried pipeline is given by t1, which is the time point when the actual buried pipeline was sampled. V0 is the corrosion rate of the longest time point in the highly corrosive soil area, and V1 is the corrosion rate of the actual buried pipeline at time point t1.

[0125] The normalized corrosion rate data and corresponding soil physicochemical properties are summarized, and the corrosion data is split into training data and test data. For example, the ratio of training data to test data is 3 to 8:1.

[0126] At this point, the training and testing data have been completed.

[0127] In step 103, the corrosion allowance of the pipeline is determined based on multiple parameter characteristics of the pipeline, wherein the parameter characteristics represent the pipeline parameters that affect the corrosion allowance of the pipeline.

[0128] In some implementations, step 103 includes:

[0129] The corrosion allowance of the pipeline is determined based on the minimum wall thickness, actual allowable working pressure, safety factor, outer diameter, tensile strength of the pipeline material, and the second formula, wherein the second formula is:

[0130]

[0131] In the formula, h is the corrosion allowance of the pipeline, and e min For the minimum wall thickness of the pipe, PFA 实际 The actual allowable working pressure is given by SF, where SF is the safety factor for the actual allowable working pressure, DE is the outer diameter of the pipe, and R is the outer diameter of the pipe.m This refers to the tensile strength of the pipe material.

[0132] In step 104, the expected service life is determined based on the corrosion allowance and the expected long-term average corrosion rate, and / or the allowable operating pressure for a predetermined number of years is determined based on the expected long-term average corrosion rate.

[0133] In some implementations, step 104 includes:

[0134] The expected service life is determined based on the third formula, the corrosion allowance, and the expected long-term average corrosion rate. The third formula is:

[0135]

[0136] In the formula, L is the expected service life, h is the corrosion allowance of the pipeline, and V3 is the expected long-term average corrosion rate.

[0137] In some implementations, step 104 includes:

[0138] Based on the expected long-term average corrosion rate, the minimum wall thickness of the pipeline, the tensile strength of the pipeline material, the outer diameter of the pipeline, and the fourth formula, the allowable working pressure for the predetermined number of years is determined. The fourth formula is:

[0139]

[0140] In the formula, p is the allowable working pressure for T0 years, e min V3 is the minimum wall thickness of the pipe, V3 is the expected long-term average corrosion rate, and R is the mean wall thickness of the pipe. m DE represents the tensile strength of the pipe material and the outer diameter of the pipe.

[0141] For example, the corrosion allowance h of a ductile iron pipe is calculated using the following formula:

[0142]

[0143] In the formula, h is the corrosion allowance of the pipeline, and e min For the minimum wall thickness of the pipe, PFA 实际 The actual allowable working pressure is given by SF, where SF is the safety factor for the actual allowable working pressure, DE is the outer diameter of the pipe, and R is the outer diameter of the pipe. m This refers to the tensile strength of the pipe material.

[0144] The expected service life is:

[0145]

[0146] In the formula, L is the expected service life, h is the corrosion allowance of the pipeline, and V3 is the expected long-term average corrosion rate.

[0147] The permissible workload for the predetermined number of years is:

[0148]

[0149] In the formula, p is the allowable working pressure for T0 years, e min V3 is the minimum wall thickness of the pipe, V3 is the expected long-term average corrosion rate, and R is the mean wall thickness of the pipe. m DE represents the tensile strength of the pipe material and the outer diameter of the pipe.

[0150] In addition, it can not only calculate the expected service life of the pipeline, but also calculate and assess the remaining service life of the pipeline that has been used for w years. The calculation formula is: Remaining service life of pipeline = Lw.

[0151] The following describes the specific implementation process for several application scenarios:

[0152] Example 1:

[0153] First, four typical corrosive soil regions in my country, A, B, C, and D, were selected as experimental sites for the burial of ductile iron pipes. The maximum pitting depth data of the samples were collected by excavation according to excavation cycles of 1 month, 2 months, 3 months, and 84 months.

[0154] SEM analysis revealed that location D exhibited the highest corrosion rate among sites A, B, C, and D, with a maximum value of 141.17 μm / a, denoted as V0. Therefore, a corrosion rate prediction model was established using location D as the model for the most severe corrosion. Specific data are shown in Table 1.

[0155] Table 1 Corrosion rate of soil samples buried at location D

[0156]

[0157] The fitted model is as follows:

[0158] V = 207.289t -0.229

[0159] The corrosion rates of the excavated pipeline at locations E (15 years), F (16.2 years), G (16.2 years), and H (6 years) were 19.80, 37.60, 33.13, and 1200.00 μm / a, respectively. This was calculated using V1*V0 / (a*(t1)). b After normalization of the formula, the corrosion rates at locations E, F, G, and H over 7 years are 25.10, 48.45, 42.69, and 1231.81 μm / a, respectively.

[0160] The training data sequence is shown in Table 2.

[0161] Table 2 Training Data Sequence List

[0162]

[0163] The test data series is shown in Table 3.

[0164] Table 3 Test Data Sequence List

[0165]

[0166] A network model, net, is built using a computer. The model layers include an input layer, a hidden layer, and an output layer.

[0167] The number of influencing factor nodes in the input layer is determined to be 6, namely 6 soil corrosion factors (soil resistivity, Cl-). - SO4 2- (Water content, pH value, salinity), at this time a0 = 6;

[0168] Determine the number of nodes in the output layer, b0, which is the long-term average corrosion rate of the ductile iron pipe. At this point, b0 = 1.

[0169] Determining the number of hidden layer nodes is another crucial step in neural network design. The complexity of this problem means that a good analytical formula has yet to be found. This example uses the formula:

[0170] S = (a0 + b0) * 0.4 + c

[0171] The number of hidden layer nodes was determined to be between 3 and 12. Experiments were then conducted with the number of hidden layer nodes ranging from 3 to 12. By comparing the network training results, the optimal combination of network training error and number of training iterations was selected, as shown in Table 4.

[0172] Table 4. Iterative training results of the neural prediction network with different numbers of hidden layer nodes.

[0173]

[0174] As shown in Table 1, the average number of iterations decreases significantly with the increase of the number of hidden layer nodes. However, with further increases in the number of nodes, the average number of iterations does not improve significantly. Considering that more hidden nodes result in poorer generalization ability, i.e., poorer ability to identify new samples, and taking into account the number of successful convergences, the maximum number of iterations, and the minimum number of iterations, the number of hidden layer nodes in the neural network is chosen to be S = 4.

[0175] After multiple debugging and testing sessions, the hidden function was determined to be...

[0176]

[0177] The output layer function is determined to be: f(x) = 4x + 6

[0178] After setting the network learning rate to 0.05, the number of training iterations to 10,000, and the error to 0.01, network training began.

[0179] The network training is complete when the error reaches 0.01 within 10,000 training iterations. After testing and verification, the network parameters are saved as net2.

[0180] Input the soil physicochemical properties under the given soil conditions at location u into the established network net2, such as soil resistivity of 44 Ω·cm and Cl. - The content is 1.6780%, SO4 2- With a content of 0.1561%, a water content of 23.95%, a pH value of 8.67, and a salt content of 2.9660%, the expected corrosion rate of the ductile iron pipe calculated using net2 is 15.29 μm / a.

[0181] Using the second formula, the corrosion allowance for a DN100C-class pipeline under an allowable working pressure of 10 bar is calculated to be 2.58 mm.

[0182] Therefore, the expected service life of the ductile iron pipe is L = 2.58 / 0.01529 = 167.4 years;

[0183] Calculate the allowable working pressure p of a DN100 Class C pipeline after 100 years; calculate using formula four.

[0184]

[0185] The final output indicates that the expected service life of the DN100C-class pipeline is 167.4 years, and the allowable working pressure p of the pipeline after 100 years is 35.3 bar.

[0186] Example 2:

[0187] First, four typical corrosive soil areas in my country, namely D1, D2, D3, and D4, were selected as experimental sites for the installation of ductile iron pipes. The maximum pitting depth data of the excavation test samples were collected according to excavation cycles of 0.082 years, 0.164 years, 0.247 years, 0.493 years, 1 year, and 7 years.

[0188] SEM analysis revealed that location D4 exhibited the highest corrosion rate among locations D1, D2, D3, and D4, with a maximum value of 141.17 μm / a, denoted as V0. Therefore, a corrosion rate prediction model was established using location D4 as the model for the most severe corrosion. Specific data are as follows.

[0189] Table 5D4 shows the corrosion rate of soil-buried bare ductile iron pipe (with pitting) samples at location D4.

[0190]

[0191] The fitting results are as follows:

[0192] V = 205.731t -0.231

[0193] The corrosion rates of the excavated pipeline at locations D5 (15 years), D6 (16.2 years), D7 (16.2 years), and D8 (6 years) were 19.80, 37.60, 33.13, and 1200.00 μm / a, respectively. This was calculated using V1*V0 / (a*(t1)). b After formula normalization, the corrosion rates of locations D5, D6, D7 and D8 over 7 years are 25.40, 49.09, 43.26 and 1245.60 μm / a, respectively.

[0194] Then, the data from other excavated pipelines were summarized, organized, and analyzed. A total of 25 locations were used as training data, and 5 locations were used as test data.

[0195] A network model .net is built using a computer. The model layers include an input layer, a hidden layer, a continuation layer, and an output layer.

[0196] The number of influencing factor nodes in the input layer is determined to be 7, namely 7 soil corrosion factors (soil resistivity, redox potential, Cl). - SO4 2- (Water content, pH value, salinity), at this time a0 = 7;

[0197] Determine the number of nodes in the output layer, b0, which is the long-term average corrosion rate of the ductile iron pipe. At this point, b0 = 1.

[0198] According to the formula:

[0199] S = (a0 + b0) * 0.4 + c

[0200] The number of hidden layer nodes was determined to be between 4 and 15. Experiments were then conducted with each of the 4 to 15 hidden layer nodes. By comparing the network training results, the optimal combination of network training error and the number of training iterations was selected, resulting in the selection of the 12 hidden layer neurons corresponding to this optimal number.

[0201] After multiple debugging and testing sessions, the hidden function was determined to be...

[0202]

[0203] In the formula, e is the natural constant, and a′ is a positive real number.

[0204] The output layer function is determined as follows:

[0205] f(x) = 3.5x + 7

[0206] After setting the network learning rate to 0.03, the number of network training iterations to 20,000, and the error to 0.001, network training began.

[0207] The network training is complete when the error reaches 0.001 within 20,000 training iterations. After testing and verification, the network parameters are saved as net3.

[0208] Input the soil physicochemical properties under given location v' conditions into the established network, such as soil resistivity of 59660 Ω·cm, redox potential of 558.0 mV, and Cl... - The content is 0.0014%, SO4 2- With a content of 0.0010%, a water content of 11.3%, a pH value of 6.9, and a salt content of 0.02%, the expected corrosion rate of the ductile iron pipe calculated using net3 is 27.73 μm / a.

[0209] Using the second formula, the corrosion allowance for a DN600 K9 grade pipeline under an allowable working pressure of 16 bar is calculated to be 4.39 mm.

[0210] Therefore, the expected service life of the ductile iron pipe is L = 4.39 / 0.02773 = 158.3 years;

[0211] Calculate the allowable working pressure of a DN600 K9 grade pipeline after 60 years. Using the fourth formula, the allowable working pressure of the DN600 K9 grade pipeline after 60 years is 2.81 MPa, or 28.1 bar.

[0212] Example 3

[0213] The following are the soil physicochemical properties parameters provided by X Water Company, a customer, for the soil environment in XX area: soil resistivity is 16980 Ω·cm, redox potential is 156.8 mV, Cl... - The content is 0.325%, SO4 2- The content is 0.782%, the water content is 8.6%, the pH value is 8.3, the salt content is 0.916%, and the allowable working pressure of the water being transported is 10 bar. A DN100 ductile iron pipe is selected, with an expected service life of 100 years. Please select the appropriate pipe material for the customer.

[0214] A corrosion rate prediction model, net4, was established using the method of this invention, and the expected corrosion rate of the ductile iron pipe was calculated to be 21.87 μm / a.

[0215] Using the second formula, the corrosion allowance for a DN100 Class C pipe under an allowable working pressure of 10 bar is calculated to be 2.58 mm; therefore, the expected service life of the ductile iron pipe is L = 2.58 / 0.02187 = 117.9 years.

[0216] The above calculations and evaluations show that DN100 Class C pipes can meet the usage requirements without the need for thicker K9 Class pipes. This saves metal resources, reduces energy and carbon emissions, and meets the user's requirements.

[0217] This invention discloses a method for predicting the service life of metal pipelines. First, it acquires multiple soil corrosion factors, whereby these factors characterize the factors affecting the pipeline's corrosion rate due to the pipeline's laying location. Then, it inputs these multiple soil corrosion factors into a corrosion rate prediction model to obtain the expected long-term average corrosion rate. Next, based on multiple parameter characteristics of the pipeline, it determines the pipeline's corrosion margin, whereby these parameter characteristics characterize pipeline parameters affecting the corrosion margin. Finally, it determines the expected service life and / or the allowable operating pressure for a predetermined number of years based on the corrosion margin and the expected long-term average corrosion rate. This invention, by inputting influencing factors into a prediction model to obtain the long-term corrosion rate and determining the corrosion margin based on pipeline parameters, can accurately assess the service life of metal pipelines based on the corrosion rate and corrosion margin. Therefore, it allows for the design of pipelines that meet user needs based on soil properties, satisfying customer requirements.

[0218] 1. Once the physical and chemical properties of the soil are determined, the expected service life of ductile iron pipes can be scientifically and reasonably calculated and evaluated to meet user needs.

[0219] 2. Based on the specific design life requirements of the pipeline, the wall thickness level of the ductile iron pipeline can be scientifically and rationally selected, saving metal materials, reducing costs, and creating huge economic benefits; at the same time, it improves material utilization, is green and low-cost, low-carbon and environmentally friendly, and significantly reduces carbon emissions, setting a benchmark for the industry and driving the green and healthy development of the ductile iron pipeline industry.

[0220] 3. Using this method to calculate and evaluate the expected service life of ductile iron pipes and to make scientific material selection can avoid water leakage accidents during the service life due to unreasonable pipe material selection. It can significantly reduce the excavation and maintenance costs of municipal underground water pipes, reduce water waste caused by pipe leakage, and play a good role in protecting the environment.

[0221] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0222] The following are embodiments of the apparatus of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0223] Figure 3 This is a functional block diagram of the metal pipeline service life prediction device provided in the embodiments of the present invention, with reference to... Figure 3 The metal pipeline service life prediction device 3 includes: a first state acquisition module 301, a line loss acquisition module 302, a second state acquisition module 303, an anomaly determination module 304, and an output module 303.

[0224] The first state acquisition module 301 is used to acquire the state of the first metering device, which is a power metering device for the bus.

[0225] The line loss acquisition module 302 is used to acquire the current line loss when the first metering device is in good condition. The line loss is the deviation between the sum of the metering data of each feeder line and the metering data of the bus. The feeder line is the feeder line connected to the bus.

[0226] The second state acquisition module 303 is used to determine the state of the second metering device set based on the current line loss. The second metering device is the power metering device of the feeder line connected to the bus. The second metering device set includes each of the second metering devices.

[0227] Anomaly determination module 304 is configured to determine the second metering device that caused the anomaly when the state of the second metering device set is abnormal; and,

[0228] The output module 303 is used to send maintenance information of the second metering device that caused the abnormality to the maintenance personnel.

[0229] Figure 4 This is a functional block diagram of the electronic device provided in an embodiment of the present invention. For example... Figure 4 As shown, the electronic device 4 of this embodiment includes a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can run on the processor 400. When the processor 400 executes the computer program 402, it implements the steps of the various metal pipe service life prediction methods and embodiments described above, for example... Figure 1 Steps 101 to 105 are shown.

[0230] For example, the computer program 402 may be divided into one or more modules / units, which are stored in the memory 401 and executed by the processor 400 to complete the present invention.

[0231] The electronic device 4 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0232] The processor 400 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0233] The memory 401 can be an internal storage unit of the electronic device 4, such as a hard disk or memory. The memory 401 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 401 can include both internal and external storage units of the electronic device 4. The memory 401 is used to store the computer program and other programs and data required by the electronic device. The memory 401 can also be used to temporarily store data that has been output or will be output.

[0234] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0235] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0236] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0237] In the embodiments provided by this invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0238] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0239] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0240] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various methods and apparatus embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0241] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for predicting the service life of a metal pipe, characterized by, The method comprises the following steps: obtaining a plurality of soil corrosion factors, wherein the soil corrosion factors represent factors affecting the corrosion rate of the pipeline in the pipeline laying site; inputting the plurality of soil corrosion factors into a corrosion rate prediction model to obtain an expected long-term average corrosion rate; determining a corrosion allowance of the pipeline according to a plurality of parameter characteristics of the pipeline, wherein the parameter characteristics represent pipeline parameters affecting the corrosion allowance of the pipeline; determining an expected service life according to the corrosion allowance and the expected long-term average corrosion rate, and / or determining an allowable working pressure for a predetermined period of time according to the expected long-term average corrosion rate; wherein the construction of the corrosion rate prediction model comprises: constructing an initial model based on an Elman neural network model; training the initial model with a plurality of corrosion data to obtain the corrosion rate prediction model; wherein the plurality of corrosion data is obtained based on experimental data in a typical corrosion soil area and corrosion data of an actually buried pipeline, and the method for obtaining the plurality of corrosion data from the corrosion data of the actually buried pipeline comprises: burying a plurality of samples in the typical corrosion soil area; excavating the plurality of samples at a plurality of different time nodes in sequence; analyzing the plurality of samples respectively to obtain a plurality of maximum corrosion pit depths, wherein the plurality of maximum corrosion pit depths correspond to the plurality of different time nodes, and the maximum corrosion pit depth is the maximum corrosion pit depth on the sample; determining a plurality of maximum corrosion rates according to the plurality of different time nodes and the plurality of maximum corrosion pit depths, wherein the plurality of maximum corrosion rates correspond to the plurality of different time nodes; establishing a corrosion rate expectation function according to the plurality of different time nodes and the plurality of maximum corrosion rates, wherein the corrosion rate expectation function is: wherein is the corrosion rate for a typical corrosive soil region, is a pre-constant, is a post-constant, is time; normalizing the corrosion rate of the actually buried pipeline according to the corrosion rate expectation function, a first formula, and the corrosion rate of the actually buried pipeline, wherein the first formula is: wherein, is the normalized actual corrosion rate of the buried pipe, is the time node when the actual buried pipe is sampled, is the corrosion rate of the most long time node corresponding to the strong corrosion soil area, is the corrosion rate of the actual buried pipe at the time node. obtaining a plurality of corrosion factors of the soil of the actually buried pipeline; using the normalized corrosion rate of the actually buried pipeline and the plurality of corrosion factors of the soil of the actually buried pipeline as the plurality of corrosion data.

2. The metal piping service life prediction method according to claim 1, characterized by, The construction of the corrosion rate prediction model comprises: the training of the initial model with the plurality of corrosion data to obtain the corrosion rate prediction model comprises: obtaining a plurality of corrosion data, wherein the corrosion data comprises a long-term average corrosion rate and a plurality of soil corrosion factors; splitting the plurality of corrosion data according to a predetermined proportion to obtain a training data set and a test data set; training step: inputting the plurality of corrosion data in the training data set into the initial model, adjusting the parameters of the initial model through a plurality of outputs of the initial model and a plurality of long-term corrosion rates corresponding to the plurality of corrosion data in the training data set until the output error of the initial model is lower than a first threshold value; fixing parameters of the initial model, inputting a plurality of corrosion data in the test data set into the initial model, determining a test error through a plurality of outputs of the initial model and long-term corrosion rates corresponding to the plurality of corrosion data in the test data set; if the test error is higher than a second threshold value, increasing a number of nodes of a hidden layer in the initial model, and jumping to the training step.

3. The metal piping service life prediction method according to claim 1, characterized by, determining the corrosion allowance of the pipeline according to the parameter characteristics of the pipeline, comprising: determining the corrosion allowance of the pipeline according to the minimum wall thickness, the actual allowable working pressure, the safety factor, the outer diameter, the tensile strength of the pipeline material and a second formula, wherein the second formula is: wherein is the corrosion allowance of the pipe, is the minimum wall thickness of the pipe, is the actual allowable working pressure, is the safety factor of the actual allowable working pressure, is the outside diameter of the pipe, is the tensile strength of the pipe material.

4. The metal pipe service life prediction method according to any one of claims 1 to 3, characterized by, determining the expected service life according to the corrosion allowance and the expected long-term average corrosion rate, comprising: determining the expected service life according to a third formula, the corrosion allowance and the expected long-term average corrosion rate, wherein the third formula is: wherein is the expected service life, is the corrosion allowance of the pipe, is the average corrosion rate expected over the long term.

5. The metal piping service life prediction method according to any one of claims 1 to 3, characterized by, determining the allowable working pressure of the predetermined period according to the expected long-term average corrosion rate, comprising determining the allowable working pressure of the predetermined period according to the expected long-term average corrosion rate, the minimum wall thickness of the pipeline, the tensile strength of the pipeline material, the outer diameter of the pipeline and a fourth formula, wherein the fourth formula is: wherein is the minimum wall thickness of the pipe, is the allowable working pressure for the period of time, is the minimum wall thickness of the pipe, is the expected long-term average corrosion rate, is the tensile strength of the pipe material, is the outside diameter of the pipe.

6. A metal piping service life prediction device characterized by comprising: The metal pipeline service life prediction device for realizing the metal pipeline service life prediction method according to any one of claims 1-5, comprising: a corrosion factor acquisition module configured to acquire a plurality of soil corrosion factors, wherein the soil corrosion factors represent factors affecting the corrosion rate of the pipeline in the soil where the pipeline is laid; a long-term average corrosion rate prediction module configured to input the plurality of soil corrosion factors into a corrosion rate prediction model to obtain an expected long-term average corrosion rate; a corrosion allowance determination module configured to determine a corrosion allowance of the pipeline according to a plurality of parameter characteristics of the pipeline, wherein the parameter characteristics represent pipeline parameters affecting the corrosion allowance of the pipeline; and a service life prediction module configured to determine an expected service life according to the corrosion allowance and the expected long-term average corrosion rate, and / or to determine an allowable working pressure of a predetermined period according to the expected long-term average corrosion rate.

7. An electronic device comprising a memory and a processor, said memory having stored therein a computer program operable on said processor, characterized in that, The processor executes the computer program to realize the steps of the method according to any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. The computer program is executed by the processor to realize the steps of the method according to any one of claims 1-5.

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