Method and system for synchronous monitoring of multi-point data of buried pipeline corrosion

Abstract

The present application discloses a method and system for synchronously monitoring the corrosion of buried pipelines at multiple points, which relates to the technical field of pipeline corrosion monitoring and is used to improve the overall safety factor of buried pipelines in complex environments. The method of the present application includes: collecting soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested; performing a corrosive analysis on the soil parameters to obtain a first corrosion risk index for each numbered pipe section; calculating the stray current density of each numbered pipe section based on the electrochemical parameters; comparing the stray current density of each numbered pipe section with a preset threshold gradient, and determining a second corrosion risk index for each numbered pipe section based on the comparison result; performing a weighted calculation on the first corrosion risk index and the second corrosion risk index to generate a corrosion risk level distribution for each numbered pipe section; determining the numbered pipe section whose corresponding corrosion risk level meets the preset level as a risky pipe section, and adjusting the cathodic protection parameters of the risky pipe section according to the parameter adjustment mechanism.

Description

Technical Field

[0001] The present application relates to the technical field of pipeline corrosion monitoring, and in particular to a method and system for synchronously monitoring multi-point data of buried pipeline corrosion. Background Art

[0002] Buried pipelines, as crucial infrastructure for transporting fluids like oil, natural gas, and water, are widely used in various sectors, including energy and municipal administration. However, due to their long-term underground presence, buried pipelines are susceptible to corrosion from various factors, including soil moisture, oxygen, and stray currents. Severe corrosion can lead to medium leakage, causing environmental pollution, energy waste, and even accidents, resulting in significant economic losses and social harm.

[0003] The essence of buried pipeline corrosion is the electrochemical inhomogeneity of the metal surface, which forms countless tiny corrosion cells. In the corrosion cell, the metal in the anode loses electrons, undergoing an oxidation reaction and corroding, while the cathode undergoes a reduction reaction. Therefore, existing buried pipelines typically use cathodic protection technology to improve their corrosion resistance. This technology transforms the entire metal surface of the pipeline into a cathode to reduce corrosion caused by oxidation reactions. However, when the environment along the buried pipeline is complex and changeable, different environmental conditions will have different corrosion effects on the buried pipeline. Existing cathodic protection technology cannot adjust to the different corrosion effects in a timely manner, resulting in the safety factor of the buried pipeline being unable to be guaranteed. Summary of the Invention

[0004] The present application provides a method and system for synchronously monitoring multi-point data of the corrosion condition of a buried pipeline, which can improve the overall safety factor of the buried pipeline in a complex environment.

[0005] In a first aspect, the present application provides a method for synchronously monitoring multi-point data of buried pipeline corrosion, comprising:

[0006] Collect soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, wherein the electrochemical parameters include current potential value, natural potential value and limit resistance value;

[0007] Performing a corrosive analysis on the soil parameters to obtain a first corrosion risk index for each numbered pipe section;

[0008] Calculating the stray current density of each numbered pipe section according to the electrochemical parameters;

[0009] Comparing the stray current density of each numbered pipe section with a preset threshold gradient, and determining a second corrosion risk index of each numbered pipe section according to the comparison result;

[0010] Performing weighted calculation on the first corrosion risk index and the second corrosion risk index to generate a corrosion risk level distribution for each numbered pipe section;

[0011] The numbered pipe sections whose corresponding corrosion risk levels meet the preset levels are determined as risky pipe sections, and the cathodic protection parameters of the risky pipe sections are adjusted according to the parameter adjustment mechanism.

[0012] Optionally, calculating the stray current density of each numbered pipe segment according to the electrochemical parameter includes:

[0013] Calculating the difference between the current potential value and the natural potential value in the electrochemical parameter to obtain a potential offset value;

[0014] Calculating a ratio between the potential offset value and the limit resistance value;

[0015] Determine the correction coefficient based on the environmental parameters and the anti-corrosion layer operation data of each numbered pipe section;

[0016] The ratio is multiplied by the correction coefficient to obtain the stray current density of each numbered pipe section.

[0017] Optionally, after adjusting the cathodic protection parameters of the risky pipe section according to the parameter adjustment mechanism, the method further includes:

[0018] Acquiring a historical data set within a preset time period, the historical data set including soil parameters, electrochemical parameters, and cathodic protection parameters of the risky pipe section collected at fixed time intervals;

[0019] Arranging the historical data set in chronological order to obtain a training sample set, and using the historical corrosion rate based on the fixed time interval as a label set;

[0020] Training a pre-trained model based on a random forest algorithm according to the training sample set and the label set, and determining that the pre-trained model that has been trained is a corrosion rate prediction model;

[0021] Predicting a corrosion rate prediction value of each of the risk pipe sections in the next time period according to the corrosion rate prediction model;

[0022] When the corrosion rate prediction value is greater than a safety threshold, an early warning message is generated.

[0023] Optionally, determining the second corrosion risk index of each numbered pipe section according to the comparison result includes:

[0024] The preset threshold gradient includes a first threshold, a second threshold and a third threshold that increase in sequence;

[0025] When the stray current density is less than or equal to the first threshold, the second corrosion risk index of the corresponding numbered pipe section is the first preset index;

[0026] When the stray current density is greater than the first threshold and less than or equal to the second threshold, the second corrosion risk index of the corresponding numbered pipe section is a second preset index;

[0027] When the stray current density is greater than the second threshold value and less than or equal to the third threshold value, the second corrosion risk index of the corresponding numbered pipe section is the third preset index;

[0028] When the stray current density is greater than the third threshold, the second corrosion risk index of the corresponding numbered pipe section is a fourth preset index.

[0029] Optionally, performing weighted calculation on the first corrosion risk index and the second corrosion risk index to generate the corrosion risk level distribution of each numbered pipe section includes:

[0030] assigning different weights to the first corrosion risk index and the second corrosion risk index respectively according to a preset weight ratio;

[0031] Summing the weighted first corrosion risk index and the second corrosion risk index to obtain a target corrosion risk index for each numbered pipe section;

[0032] The target corrosion risk index is compared with a preset index range, and the corrosion risk level of each numbered pipe section is determined according to the comparison result.

[0033] Optionally, adjusting the cathodic protection parameters of the risky pipe section according to a parameter adjustment mechanism includes:

[0034] Obtaining geographical environment information of the area where each risk pipe section is located;

[0035] determining a parameter adjustment mechanism based on the geographic environment information;

[0036] The parameter adjustment mechanism is used to adjust the cathodic protection parameters of the risk pipe section.

[0037] Optionally, before collecting soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, the method further includes:

[0038] Divide the pipeline into different sections according to the geographical features along the buried pipeline to be tested;

[0039] All pipe sections within the pipe section area are numbered according to the numbering rules to obtain numbered pipe sections.

[0040] A second aspect of the present application provides a system for synchronously monitoring multi-point data of buried pipeline corrosion, comprising:

[0041] The acquisition unit is used to collect soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, wherein the electrochemical parameters include current potential value, natural potential value and limit resistance value;

[0042] an analysis unit, configured to perform a corrosive analysis on the soil parameters to obtain a first corrosion risk index for each numbered pipe segment;

[0043] a calculation unit, configured to calculate the stray current density of each numbered pipe section according to the electrochemical parameters;

[0044] a comparing unit, configured to compare the stray current density of each numbered pipe section with a preset threshold gradient, and determine a second corrosion risk index of each numbered pipe section according to the comparison result;

[0045] a weighting unit, configured to perform weighted calculation on the first corrosion risk index and the second corrosion risk index to generate a corrosion risk level distribution for each numbered pipe section;

[0046] The adjustment unit is used to determine the numbered pipe sections whose corresponding corrosion risk levels meet the preset levels as risk pipe sections, and adjust the cathodic protection parameters of the risk pipe sections according to the parameter adjustment mechanism.

[0047] Optionally, the computing unit is specifically configured to:

[0048] Calculating the difference between the current potential value and the natural potential value in the electrochemical parameter to obtain a potential offset value;

[0049] Calculating a ratio between the potential offset value and the limit resistance value;

[0050] Determine the correction coefficient based on the environmental parameters and the anti-corrosion layer operation data of each numbered pipe section;

[0051] The ratio is multiplied by the correction coefficient to obtain the stray current density of each numbered pipe section.

[0052] Optionally, the system further includes:

[0053] an acquisition unit, configured to acquire a historical data set within a preset time period, wherein the historical data set includes soil parameters, electrochemical parameters, and cathodic protection parameters of the risky pipe section collected at fixed time intervals;

[0054] an arranging unit, configured to arrange the historical data set in chronological order to obtain a training sample set, and use the historical corrosion rate based on the fixed time interval as a label set;

[0055] A training unit, configured to train a pre-trained model based on a random forest algorithm according to the training sample set and the label set, and determine that the pre-trained model after training is a corrosion rate prediction model;

[0056] A prediction unit, configured to predict a corrosion rate prediction value of each of the risk pipe sections in a next time period according to the corrosion rate prediction model;

[0057] A generating unit is used to generate early warning information when the corrosion rate prediction value is greater than a safety threshold.

[0058] It can be seen from the above technical solutions that this application has the following effects:

[0059] First, soil parameters and electrochemical parameters of each numbered section of the buried pipeline to be tested are collected. The electrochemical parameters include the current potential value, the natural potential value, and the limit resistance value. Then, the soil parameters are corrosively analyzed to obtain the first corrosion risk index of each numbered section. The stray current density of each numbered section is then calculated based on the electrochemical parameters. The stray current density of each numbered section is then compared with the preset threshold gradient, and the second corrosion risk index of each numbered section is determined based on the comparison result. The first corrosion risk index and the second corrosion risk index are further weighted to generate a corrosion risk level distribution for each numbered section. Finally, the numbered section whose corresponding corrosion risk level meets the preset level is determined as the risk section, and the cathodic protection parameters of the risk section are adjusted according to the parameter adjustment mechanism. In this way, the effects of the two main influencing factors of soil environment and stray current on the corrosion of the buried pipeline can be comprehensively considered to fully reflect the corrosion status of each numbered section of the pipeline. Then, the cathodic protection parameters can be accurately adjusted according to the corrosion risk level of each numbered section, thereby improving the corrosion protection level of each numbered section and further improving the overall safety factor of the buried pipeline in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a schematic diagram of an embodiment of a method for synchronously monitoring multi-point data of buried pipeline corrosion in this application;

[0061] Figure 2-1 、 Figure 2-2 and Figure 2-3 This is a schematic diagram of another embodiment of a method for synchronously monitoring multi-point data of buried pipeline corrosion in the present application;

[0062] Figure 3 This is a schematic diagram of an embodiment of a system for synchronously monitoring multi-point data of buried pipeline corrosion in this application. DETAILED DESCRIPTION

[0063] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0064] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0065] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0066] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0067] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0068] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0069] Existing buried pipelines typically utilize cathodic protection technology to enhance their corrosion resistance. This technology transforms the entire metal surface of the pipeline into a cathode, reducing corrosion caused by oxidation reactions. However, the complex and ever-changing environment along buried pipelines can lead to varying corrosion effects. Existing cathodic protection technology is unable to adapt to these varying corrosion effects, resulting in a lack of guaranteed safety for buried pipelines.

[0070] Based on this, the present application discloses a method and system for synchronously monitoring multi-point data of buried pipeline corrosion, which can improve the overall safety factor of buried pipelines in complex environments.

[0071] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0072] The method for synchronous multi-point data monitoring of buried pipeline corrosion described in this application is applied to a system, terminal, server or other device with logic analysis and processing capabilities for execution and implementation. Figure 1 As shown, an embodiment of the method for synchronously monitoring multi-point data of buried pipeline corrosion in the present application includes:

[0073] 101. Collect soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, wherein the electrochemical parameters include current potential value, natural potential value, and limit resistance value;

[0074] In this embodiment, the buried pipeline to be tested is first segmented and numbered. The numbering scheme can be determined based on factors such as the pipeline's length, material, and installation environment. Monitoring points are set up in the soil above the pipeline corresponding to each numbered segment, and soil parameter sensors are installed at each monitoring point. These soil parameter sensors collect real-time data on soil moisture, pH, salinity, and other parameters. Soil moisture affects soil conductivity, which in turn affects the corrosion rate of the pipeline. For example, when soil moisture exceeds 60%, the corrosion rate significantly accelerates. The pH value reflects the acidity or alkalinity of the soil. For example, acidic soil with a pH less than 5.5 and alkaline soil with a pH greater than 8.5 are both highly corrosive to pipelines. Salt content affects the electrochemical corrosion process of the pipeline; higher salt content increases the corrosion rate. The current potential value in the electrochemical parameter refers to the surface potential of the pipeline during cathodic protection. The natural potential value in the electrochemical parameter refers to the stable potential of the pipeline in the soil when it is not disturbed by external current. It can reflect the corrosion tendency of the pipeline. The greater the natural potential value deviates from the equilibrium potential, the more susceptible the pipeline is to corrosion. The limiting resistance value in the electrochemical parameters refers to the rate of change of current corresponding to a slight change in the potential of the pipeline during the corrosion process, reflecting the resistance of the pipeline corrosion reaction. It should be noted that a reference electrode can be used to measure the surface potential value of each numbered pipe section in the buried pipeline to be tested under cathodic protection to collect the current potential value. A reference electrode can be used to measure the surface potential value of each numbered pipe section in the buried pipeline to be tested that is not under cathodic protection and is not disturbed by external current to collect the natural potential value. The measurement can be carried out by constant potential method or constant current method, that is, applying a small potential disturbance to the pipeline, recording the corresponding current change, and calculating the ratio between the potential disturbance value and the current change value to determine the limiting resistance value.

[0075] 102. Conduct corrosive analysis on soil parameters to obtain the first corrosion risk index of each numbered pipe section;

[0076] In this example, the collected soil parameters are substituted into a pre-established soil corrosivity assessment model. This model, constructed based on extensive experimental data and practical engineering experience, uses multiple linear regression analysis to determine the weights of soil parameters such as soil moisture, pH, and salinity on pipeline corrosion. Specifically, soil parameters are collected from various locations along an operational pipeline project, along with the pipeline corrosion conditions at those locations. This data serves as a sample. Linear regression equations are then established, using the pipeline corrosion conditions as the dependent variable and the soil parameters as independent variables. The regression coefficients corresponding to the variables in this linear regression equation are then estimated using the least squares method. The core concept is to minimize the sum of squared residuals between the observed values ​​and the model's predicted values. The estimated regression coefficients represent the weights of each soil parameter's influence on pipeline corrosion. After normalizing soil parameters such as soil moisture, pH, and salinity, a weighted sum is calculated based on the influence weights of each soil parameter to obtain the first corrosion risk index for each numbered pipe segment. This first corrosion risk index ranges from 0 to 1, with larger values ​​indicating a higher corrosion risk for the corresponding numbered pipe segment. It is understood that in another implementation, soil parameters such as soil moisture, pH, and salinity can be used as training features to construct a deep learning-based corrosion risk index prediction model. This corrosion risk index prediction model is then used to obtain the first corrosion risk index for each numbered pipe segment.

[0077] 103. Calculate the stray current density of each numbered pipe section based on the electrochemical parameters;

[0078] The stray current density value refers to the magnitude of the stray current passing through a unit area. Stray current refers to current that deviates from the original circuit path. It is usually generated by electrified railways, high-voltage transmission lines, cathodic protection systems, etc., and flows through buried pipelines through electrolyte media such as soil and water. The stray current density value reflects the distribution intensity of the stray current on the pipeline surface. The larger the value, the stronger the current effect per unit area and the higher the risk of electrochemical corrosion in the pipeline. In this embodiment, the known current potential value, natural potential value, and limiting resistance value, combined with Ohm's law, can accurately calculate the density of the stray current per unit area for each numbered pipe section.

[0079] 104. Compare the stray current density of each numbered pipe section with a preset threshold gradient, and determine a second corrosion risk index of each numbered pipe section based on the comparison result;

[0080] In this embodiment, multiple different stray current density thresholds are pre-set to form a preset threshold gradient. The calculated stray current density of each numbered pipe segment is compared with the preset threshold gradient, and a corresponding second corrosion risk index is assigned. A larger second corrosion risk index value indicates a greater degree of impact of stray current on the corrosion risk of the pipe segment.

[0081] 105. Perform weighted calculation on the first corrosion risk index and the second corrosion risk index to generate a corrosion risk level distribution for each numbered pipe section;

[0082] In this embodiment, different weights are assigned to the first and second corrosion risk indices based on the degree to which soil and stray current factors affect pipeline corrosion. The weighted corrosion risk indices are then classified into different levels based on pre-set corrosion risk classification criteria. This generates a corrosion risk level distribution for each numbered pipeline segment, which can be visually displayed in graphical form, such as a heat map or bar chart, clearly illustrating the corrosion risk status of each pipeline segment.

[0083] 106. Determine the numbered pipe sections whose corresponding corrosion risk levels meet the preset levels as risky pipe sections, and adjust the cathodic protection parameters of the risky pipe sections according to the parameter adjustment mechanism.

[0084] The medium risk and above corrosion risk levels are set as preset levels. When the corrosion risk level of a certain numbered pipe section reaches or exceeds the preset level, it is determined as a risky pipe section, and the cathodic protection parameters of the risky pipe section are adjusted according to the parameter adjustment mechanism.

[0085] In this embodiment, soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested are first collected. The electrochemical parameters include the current potential value, the natural potential value, and the limit resistance value. Then, the soil parameters are corrosively analyzed to obtain the first corrosion risk index of each numbered pipe section. Then, the stray current density of each numbered pipe section is calculated based on the electrochemical parameters. Then, the stray current density of each numbered pipe section is compared with a preset threshold gradient, and the second corrosion risk index of each numbered pipe section is determined based on the comparison result. The first corrosion risk index and the second corrosion risk index are further weighted to generate a corrosion risk level distribution for each numbered pipe section. Finally, the numbered pipe section whose corresponding corrosion risk level meets the preset level is determined as a risk pipe section, and the cathodic protection parameters of the risk pipe section are adjusted according to the parameter adjustment mechanism. In this way, the effects of the two main influencing factors of soil environment and stray current on the corrosion of the buried pipeline can be comprehensively considered to fully reflect the corrosion status of each numbered pipe section of the pipeline. Then, the cathodic protection parameters can be accurately adjusted according to the corrosion risk level of each numbered pipe section, thereby improving the corrosion protection level of each numbered pipe section and further improving the overall safety factor of the buried pipeline in a complex environment.

[0086] See also Figure 2-1 、 Figure 2-2 and Figure 2-3 As shown, another embodiment of the method for synchronously monitoring multi-point data of buried pipeline corrosion in the present application includes:

[0087] 201. Divide the buried pipeline into different sections according to the geographical features along the pipeline to be tested;

[0088] 202. Numbering all pipe sections in the pipe section area according to the numbering rule to obtain numbered pipe sections;

[0089] Optionally, in this embodiment, geographic characteristics are important environmental factors affecting buried pipeline corrosion. Different geographic characteristics can lead to significant differences in soil properties and hydrological conditions, which in turn have varying impacts on pipeline corrosion. When demarcating pipeline segments, a detailed geographic survey along the pipeline route is required to collect relevant geographic data, including but not limited to topography (e.g., plains, mountains, hills, river valleys), soil types (e.g., clay, sand, loam, saline-alkali soils), hydrological conditions (e.g., groundwater depth, presence of waterlogged areas), and the surrounding environment (e.g., proximity to industrial and commercial areas, farmland, railways, highways, etc.). Based on this geographic data, the pipeline segments are then divided according to the following rules. For example, for topography, plains are divided into a single segment unit, mountainous areas with slopes exceeding 15° are individually divided into separate units, hilly areas are divided into units every 2-3 kilometers based on the degree of undulation, and river valleys are divided into units within a 50-meter radius on either side of the river. For soil types, each major soil type is divided into an independent pipe section area unit. If a certain soil type is widely distributed and has obvious differences within it, it will be further subdivided. For hydrological conditions, areas with a groundwater level of less than 1 meter are divided into one unit, areas with a depth of 1-3 meters are divided into one unit, and areas with a depth of more than 3 meters are divided into one unit; areas with seasonal waterlogging are divided into a separate unit. For the surrounding environment, areas within 500 meters close to industrial areas are divided into one unit, areas within 30 meters close to railways and highways are divided into one unit, and farmland areas are divided into different units according to the types of crops planted. The specific division rules are not limited here. After the pipe section area is divided, the pipe sections in all pipe section areas are numbered according to the numbering rules, for example: using letters + numbers for numbering.

[0090] 203. Collect soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, wherein the electrochemical parameters include current potential value, natural potential value, and limit resistance value;

[0091] 204. Conduct corrosivity analysis on soil parameters to obtain the first corrosion risk index of each numbered pipe section;

[0092] Steps 203 and 204 in this embodiment are the same as those in the previous Figure 1 Steps 101 and 102 in the illustrated embodiment are similar and will not be described in detail here.

[0093] 205. Calculate the difference between the current potential value and the natural potential value in the electrochemical parameter to obtain a potential offset value;

[0094] 206. Calculate the ratio between the potential offset value and the limit resistance value;

[0095] 207. Determine the correction factor based on the environmental parameters and the anti-corrosion layer operation data of each numbered pipe section;

[0096] 208. Multiply the ratio by the correction coefficient to obtain the stray current density of each numbered pipe section;

[0097] Optionally, in this embodiment, the current potential and natural potential values ​​are extracted from the acquired electrochemical parameters, and the potential offset value is calculated by taking their difference. The current potential value is the actual measured potential on the surface of each numbered pipe section at the current moment, while the natural potential value is the inherent potential baseline value of that pipe section in an environment without stray current interference. The difference between the two directly reflects the degree of offset in the pipeline potential caused by stray currents. The limiting resistance value represents the threshold for the pipeline to resist stray current corrosion under specific conditions. Dividing the potential offset value obtained in the previous step by this limiting resistance value yields a ratio that provides a preliminary indication of the impact of stray currents on the pipeline. The correction coefficient must be determined by comprehensively considering environmental parameters and the anti-corrosion coating operating data for each numbered pipe section. Environmental parameters include soil physical and chemical properties such as resistivity, moisture content, and pH, as well as external conditions such as ambient temperature and humidity. The anti-corrosion coating operating data includes indicators reflecting the coating's performance, such as coating thickness, number of damage points, and insulation resistance. By performing a weighted analysis of these parameters, a correction coefficient that accurately reflects actual operating conditions can be obtained. Specifically, each environmental parameter and each anti-corrosion coating parameter is first standardized. Then, weights are assigned based on their impact on stray current. The impact of each environmental parameter and each anti-corrosion coating parameter on stray current is determined through historical data or expert experience. For example, when soil resistivity is low, stray current is more likely to flow, potentially causing greater corrosion to the pipeline, and thus can be assigned a higher weight. The ratios obtained in the above steps are then combined with the correction factor using the following formula:

[0098]

[0099] in, Indicates the stray current density, the unit of which is ampere per square meter (A / m 2 ); Indicates the potential offset between the current potential value and the natural potential value, and its unit is volt (V); Indicates the limit resistance value, its unit is ohm ( ); S represents the effective surface area of ​​the pipe in contact with the soil, and its unit is m 2 ; Indicates the correction factor.

[0100] By introducing a correction coefficient, this calculation process effectively eliminates the interference of environmental differences and anti-corrosion layer performance degradation on the stray current monitoring results, making the obtained stray current density data more consistent with the actual corrosion risk conditions of the pipeline, and providing an accurate quantitative basis for subsequent corrosion risk assessment.

[0101] 209. Compare the stray current density of each numbered pipe section with a preset threshold gradient, where the preset threshold gradient includes a first threshold, a second threshold, and a third threshold that increase in sequence;

[0102] 210. When the stray current density is less than or equal to the first threshold, the second corrosion risk index of the corresponding numbered pipe section is the first preset index; when the stray current density is greater than the first threshold and less than or equal to the second threshold, the second corrosion risk index of the corresponding numbered pipe section is the second preset index; when the stray current density is greater than the second threshold and less than or equal to the third threshold, the second corrosion risk index of the corresponding numbered pipe section is the third preset index; when the stray current density is greater than the third threshold, the second corrosion risk index of the corresponding numbered pipe section is the fourth preset index;

[0103] Optionally, in this embodiment, the preset threshold gradient is a key indicator system used to measure the impact of stray current density on pipeline corrosion risk. It includes a first threshold, a second threshold, and a third threshold, which are set in ascending order. These three thresholds are determined based on extensive experimental data, analysis of actual pipeline corrosion cases, and relevant industry standards. They accurately reflect the differences in pipeline corrosion risk under different stray current density ranges.

[0104] When the stray current density is less than or equal to the first threshold, it indicates that the stray current in the environment of the pipe section has a very weak impact on the corrosion of the pipeline. At this time, the second corrosion risk index of the corresponding pipe section is the first preset index. The first preset index represents a low corrosion risk level, indicating that the pipeline is less likely to corrode in the current stray current environment and can maintain a good operating condition for a long time.

[0105] When the stray current density is greater than the first threshold and less than or equal to the second threshold, it indicates that the corrosion effect of the stray current on the pipeline has increased, but is still within a controllable range. The second corrosion risk index of the corresponding numbered pipe section is the second preset index. The second preset index corresponds to a higher corrosion risk than the first preset index.

[0106] When the stray current density is greater than the second threshold and less than or equal to the third threshold, it indicates that the stray current has a significant impact on pipeline corrosion and the corrosion risk faced by the pipeline is significantly increased. At this time, the second corrosion risk index of the corresponding numbered pipe section is the third preset index. The third preset index corresponds to a higher corrosion risk level.

[0107] When the stray current density is greater than the third threshold, it indicates that the stray current in the environment of the pipe section is extremely strong, causing severe corrosion to the pipeline. The pipeline faces an extremely high corrosion risk. The second corrosion risk index of the corresponding pipe section is the fourth preset index, which represents the highest corrosion risk level.

[0108] It should be noted that the first, second, third, and fourth preset indices are all constants ranging from 0 to 1. For example, the first, second, third, and fourth preset indices are 0.2, 0.4, 0.6, and 0.8, respectively. By determining the second corrosion risk index based on the comparison of the preset threshold gradient and the stray current density, the corrosion risk status of each numbered pipe section under the influence of stray current can be accurately and quantitatively assessed, thereby improving the reliability of the second corrosion risk index.

[0109] 211. Assign different weights to the first corrosion risk index and the second corrosion risk index according to the preset weight ratio;

[0110] 212. Sum the weighted first corrosion risk index and the second corrosion risk index to obtain a target corrosion risk index for each numbered pipe section;

[0111] 213. Compare the target corrosion risk index with the preset index range, and determine the corrosion risk level of each numbered pipe section based on the comparison results;

[0112] Optionally, in this embodiment, the preset weight ratio can be determined based on the importance of the first and second corrosion risk indices in reflecting pipeline corrosion risk. For example, in areas with complex geological environments and highly corrosive soil, the first corrosion risk index may be given a relatively higher weight; whereas, in areas with severe stray current interference, the second corrosion risk index may be appropriately weighted. This preset weight ratio can be adjusted based on the actual operating environment and historical corrosion data of the buried pipeline being tested to ensure that the weighted calculation results are more consistent with the actual corrosion risk conditions. The specifics are not limited here. After determining the preset weight ratio, the first and second corrosion risk indices are weighted according to the preset weight ratio. The weighted first and second corrosion risk indices are then summed to obtain a target corrosion risk index for each numbered pipe segment. This summation operation integrates the two different corrosion risk indicators into a comprehensive target corrosion risk index, thereby more comprehensively reflecting the overall corrosion risk faced by each numbered pipe segment. The target corrosion risk index is then compared with the preset index range, and the corrosion risk level of each numbered pipe segment is determined based on the comparison result. The preset index range can be divided into low-risk range, medium-low-risk range, medium-risk range, medium-high-risk range, and high-risk range based on a large amount of pipeline corrosion accident statistics, safe operation standards, and risk acceptability. When the target corrosion risk index falls within a preset index range, the corresponding numbered pipe section is determined to have the corrosion risk level corresponding to that range. For example, if the target corrosion risk index is in the low-risk range, the corrosion risk level of the pipe section is low risk; if it is in the high-risk range, the corrosion risk level is high risk. In this way, the abstract target corrosion risk index can be converted into an intuitive and easy-to-understand corrosion risk level, making it easier for staff to quickly understand the corrosion risk situation of each pipe section and then take targeted prevention, control, and maintenance measures.

[0113] 214. Determine the numbered pipe sections whose corresponding corrosion risk levels meet the preset levels as risky pipe sections;

[0114] 215. Obtain geographical environment information of the areas where each risk pipe section is located;

[0115] 216. Determine the parameter adjustment mechanism based on geographic environment information;

[0116] 217. Use the parameter adjustment mechanism to adjust the cathodic protection parameters of the risk pipe section;

[0117] Optionally, in this embodiment, the geographical environment information for the area where each risky pipeline section is located may include information such as topography, geomorphology, geography, and climate characteristics. The parameter adjustment mechanism is a set of rules established based on the correlation between geographical environment information and cathodic protection parameters. Different combinations of geographical environment information correspond to different cathodic protection parameter adjustment strategies. Cathodic protection parameters include protection potential, protection current density, and anode output current. For example, when the soil resistivity in the area where a risky pipeline section is located is low, the parameter adjustment mechanism may require an appropriate increase in the cathodic protection current density to enhance the protection effect on the pipeline. When the groundwater level in the area is high and the water is corrosive, the parameter adjustment mechanism may require a shortened cathodic protection system inspection cycle and, if necessary, adjust the protection potential range to address the additional corrosion risk caused by water immersion. When the area has complex terrain and uneven pipeline stress, the parameter adjustment mechanism may specifically adjust the cathodic protection parameters in certain local areas to ensure that all parts of the pipeline are effectively protected. Through such adjustments, the cathodic protection system of the risky pipeline section can better adapt to its geographical environment, effectively reducing the corrosion risk of the pipeline and ensuring the safe and stable operation of the buried pipeline.

[0118] 218. Obtain a historical data set within a preset time period, the historical data set including soil parameters, electrochemical parameters, and cathodic protection parameters of the risk pipe section collected at fixed time intervals;

[0119] 219. Arrange the historical data set in chronological order to obtain a training sample set, and use the historical corrosion rate based on a fixed time interval as a label set;

[0120] 220. Training a pre-trained model based on a random forest algorithm according to the training sample set and the label set, and determining that the trained pre-trained model is a corrosion rate prediction model;

[0121] 221. Predict the corrosion rate of each risk pipe section in the next time period based on the corrosion rate prediction model;

[0122] 222. When the predicted corrosion rate value is greater than the safety threshold, an early warning message is generated.

[0123] Optionally, in this embodiment, fixed time intervals can be set based on the corrosion risk level of each numbered pipe section and actual monitoring needs. For example, for high-risk pipe sections, this can be set daily, and for medium- and low-risk pipe sections, it can be set weekly to ensure data timeliness and continuity. Historical datasets can be obtained by accessing historical records from the monitoring system or monitoring data stored in a database. Preliminary data screening can also be performed to remove significantly abnormal or invalid data to ensure dataset reliability. Chronological arrangement can reflect data trends over time, providing temporal correlation information for subsequent model training. Historical corrosion rates based on these fixed time intervals are used as label sets. Historical corrosion rates can be determined from previous pipeline inspection data, such as those obtained through ultrasonic thickness measurement or weight loss testing. These rates must correspond to the time intervals in the training sample set, ensuring that each training sample has a corresponding label value. This allows the model to learn the mapping between input parameters and corrosion rates. The random forest algorithm has excellent capabilities for handling nonlinear data and resisting overfitting, making it suitable for corrosion rate prediction scenarios with multiple input parameters. During the training process, soil parameters, electrochemical parameters, and cathodic protection parameters from the training sample set are used as input features, and historical corrosion rates from the label set are used as output targets. Through multiple rounds of iterative training, the model parameters are continuously adjusted to improve the model's prediction accuracy. A cross-validation approach can be used during training to divide the training sample set into a training subset and a validation subset. The validation subset is used to evaluate the model's performance. Training is terminated when the prediction error on the validation subset reaches a preset threshold. The resulting model is considered the trained corrosion rate prediction model. The latest soil parameters, electrochemical parameters, and cathodic protection parameters collected at fixed intervals before the current moment are then input into the trained corrosion rate prediction model. Based on the learned mapping relationships, the corrosion rate prediction model outputs predicted corrosion rates for each risky pipeline segment in the next time period. When the predicted corrosion rate exceeds a safety threshold, it indicates that the risky pipeline segment faces a high corrosion risk in the future, potentially affecting its safe operation. At this point, an early warning message can be generated, including the location of the risky pipeline segment, the predicted corrosion rate value, the safety threshold, and recommended protective measures. By sending the early warning to relevant management personnel, timely countermeasures can be taken to reduce the losses caused by pipeline corrosion and improve the prevention level of pipeline corrosion.

[0124] See also Figure 3 As shown, an embodiment of the system for synchronously monitoring multi-point data of buried pipeline corrosion in the present application includes:

[0125] The collection unit 301 is used to collect soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, where the electrochemical parameters include current potential value, natural potential value and limit resistance value;

[0126] An analysis unit 302 is configured to perform a corrosive analysis on soil parameters to obtain a first corrosion risk index for each numbered pipe segment;

[0127] The calculation unit 303 is used to calculate the stray current density of each numbered pipe section according to the electrochemical parameters;

[0128] The comparison unit 304 is configured to compare the stray current density of each numbered pipe section with a preset threshold gradient, and determine a second corrosion risk index of each numbered pipe section based on the comparison result;

[0129] A weighting unit 305 is used to perform weighted calculation on the first corrosion risk index and the second corrosion risk index to generate a corrosion risk level distribution for each numbered pipe section;

[0130] The adjustment unit 306 is used to determine the numbered pipe sections whose corresponding corrosion risk levels meet the preset levels as risky pipe sections, and adjust the cathodic protection parameters of the risky pipe sections according to the parameter adjustment mechanism.

[0131] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0133] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0134] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0135] If the integrated unit is implemented in the form of 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, the technical solution of the present application, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk, and other media that can store program code.

Claims

1. A method for synchronously monitoring multi-point data of buried pipeline corrosion, characterized in that: include: Collect soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, wherein the electrochemical parameters include current potential value, natural potential value and limit resistance value; Performing a corrosive analysis on the soil parameters to obtain a first corrosion risk index for each numbered pipe section; Calculating the stray current density of each numbered pipe section according to the electrochemical parameters; Comparing the stray current density of each numbered pipe section with a preset threshold gradient, and determining a second corrosion risk index of each numbered pipe section according to the comparison result; Performing weighted calculation on the first corrosion risk index and the second corrosion risk index to generate a corrosion risk level distribution for each numbered pipe section; Determine the numbered pipe sections whose corresponding corrosion risk levels meet the preset levels as risky pipe sections, and adjust the cathodic protection parameters of the risky pipe sections according to the parameter adjustment mechanism; The step of calculating the stray current density of each numbered pipe section according to the electrochemical parameters includes: Calculating the difference between the current potential value and the natural potential value in the electrochemical parameter to obtain a potential offset value; Calculating a ratio between the potential offset value and the limit resistance value; Determine the correction coefficient based on the environmental parameters and the anti-corrosion layer operation data of each numbered pipe section; Multiplying the ratio by the correction coefficient to obtain the stray current density of each numbered pipe section; Wherein, after adjusting the cathodic protection parameters of the risky pipe section according to the parameter adjustment mechanism, the method further includes: Acquiring a historical data set within a preset time period, the historical data set including soil parameters, electrochemical parameters, and cathodic protection parameters of the risky pipe section collected at fixed time intervals; Arranging the historical data set in chronological order to obtain a training sample set, and using the historical corrosion rate based on the fixed time interval as a label set; Training a pre-trained model based on a random forest algorithm according to the training sample set and the label set, and determining that the pre-trained model that has been trained is a corrosion rate prediction model; Predicting a corrosion rate prediction value of each of the risk pipe sections in the next time period according to the corrosion rate prediction model; When the corrosion rate prediction value is greater than a safety threshold, an early warning message is generated.

2. The method for synchronously monitoring multi-point data of buried pipeline corrosion according to claim 1, characterized in that: Determining the second corrosion risk index of each numbered pipe section according to the comparison result includes: The preset threshold gradient includes a first threshold, a second threshold and a third threshold that increase in sequence; When the stray current density is less than or equal to the first threshold, the second corrosion risk index of the corresponding numbered pipe section is the first preset index; When the stray current density is greater than the first threshold and less than or equal to the second threshold, the second corrosion risk index of the corresponding numbered pipe section is a second preset index; When the stray current density is greater than the second threshold value and less than or equal to the third threshold value, the second corrosion risk index of the corresponding numbered pipe section is the third preset index; When the stray current density is greater than the third threshold, the second corrosion risk index of the corresponding numbered pipe section is a fourth preset index.

3. The method for synchronously monitoring multi-point data of buried pipeline corrosion according to claim 1, characterized in that: The weighted calculation of the first corrosion risk index and the second corrosion risk index to generate the corrosion risk level distribution of each numbered pipe section includes: assigning different weights to the first corrosion risk index and the second corrosion risk index respectively according to a preset weight ratio; Summing the weighted first corrosion risk index and the second corrosion risk index to obtain a target corrosion risk index for each numbered pipe section; The target corrosion risk index is compared with a preset index range, and the corrosion risk level of each numbered pipe section is determined according to the comparison result.

4. The method for synchronously monitoring multi-point data of buried pipeline corrosion according to any one of claims 1 to 3, characterized in that: The adjusting the cathodic protection parameters of the risky pipe section according to the parameter adjustment mechanism includes: Obtaining geographical environment information of the area where each risk pipe section is located; determining a parameter adjustment mechanism based on the geographic environment information; The parameter adjustment mechanism is used to adjust the cathodic protection parameters of the risk pipe section.

5. The method for synchronously monitoring multi-point data of buried pipeline corrosion according to any one of claims 1 to 3, characterized in that: Before collecting soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, the method includes: Divide the pipeline into different sections according to the geographical features along the buried pipeline to be tested; All pipe sections within the pipe section area are numbered according to the numbering rules to obtain numbered pipe sections.

6. A system for synchronously monitoring the corrosion of buried pipelines using multiple data points, based on the method for synchronously monitoring the corrosion of buried pipelines using multiple data points according to any one of claims 1 to 5, characterized in that: include: The acquisition unit is used to collect soil parameters and electrochemical parameters of each numbered pipe section in the buried pipeline to be tested, wherein the electrochemical parameters include current potential value, natural potential value and limit resistance value; an analysis unit, configured to perform a corrosive analysis on the soil parameters to obtain a first corrosion risk index for each numbered pipe segment; a calculation unit, configured to calculate the stray current density of each numbered pipe section according to the electrochemical parameters; a comparing unit, configured to compare the stray current density of each numbered pipe section with a preset threshold gradient, and determine a second corrosion risk index of each numbered pipe section according to the comparison result; a weighting unit, configured to perform weighted calculation on the first corrosion risk index and the second corrosion risk index to generate a corrosion risk level distribution for each numbered pipe section; An adjustment unit, configured to determine a numbered pipe section whose corresponding corrosion risk level meets a preset level as a risk pipe section, and adjust the cathodic protection parameters of the risk pipe section according to a parameter adjustment mechanism; The step of calculating the stray current density of each numbered pipe section according to the electrochemical parameters includes: Calculating the difference between the current potential value and the natural potential value in the electrochemical parameter to obtain a potential offset value; Calculating a ratio between the potential offset value and the limit resistance value; Determine the correction coefficient based on the environmental parameters and the anti-corrosion layer operation data of each numbered pipe section; Multiplying the ratio by the correction coefficient to obtain the stray current density of each numbered pipe section; Wherein, after adjusting the cathodic protection parameters of the risky pipe section according to the parameter adjustment mechanism, the method further includes: Acquiring a historical data set within a preset time period, the historical data set including soil parameters, electrochemical parameters, and cathodic protection parameters of the risky pipe section collected at fixed time intervals; Arranging the historical data set in chronological order to obtain a training sample set, and using the historical corrosion rate based on the fixed time interval as a label set; Training a pre-trained model based on a random forest algorithm according to the training sample set and the label set, and determining that the pre-trained model that has been trained is a corrosion rate prediction model; Predicting a corrosion rate prediction value of each of the risk pipe sections in the next time period according to the corrosion rate prediction model; When the corrosion rate prediction value is greater than a safety threshold, an early warning message is generated.

7. The system for synchronously monitoring multi-point data of buried pipeline corrosion according to claim 6, characterized in that: The computing unit is specifically configured to: Calculating the difference between the current potential value and the natural potential value in the electrochemical parameter to obtain a potential offset value; Calculating a ratio between the potential offset value and the limit resistance value; Determine the correction coefficient based on the environmental parameters and the anti-corrosion layer operation data of each numbered pipe section; The ratio is multiplied by the correction coefficient to obtain the stray current density of each numbered pipe section.

8. The system for synchronously monitoring multi-point data of buried pipeline corrosion according to claim 6, characterized in that: The system further comprises: an acquisition unit, configured to acquire a historical data set within a preset time period, wherein the historical data set includes soil parameters, electrochemical parameters, and cathodic protection parameters of the risky pipe section collected at fixed time intervals; an arranging unit, configured to arrange the historical data set in chronological order to obtain a training sample set, and use the historical corrosion rate based on the fixed time interval as a label set; A training unit, configured to train a pre-trained model based on a random forest algorithm according to the training sample set and the label set, and determine that the pre-trained model after training is a corrosion rate prediction model; A prediction unit, configured to predict a corrosion rate prediction value of each of the risk pipe sections in a next time period according to the corrosion rate prediction model; A generating unit is used to generate early warning information when the corrosion rate prediction value is greater than a safety threshold.

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