Cathodic protection monitoring method and device based on multi-physical field coupling

Abstract

The application discloses a kind of cathodic protection monitoring method and device based on multi-physical field coupling, it is related to pipeline system technical field, wherein the method comprises: obtaining the multi-physical field data of pipeline monitoring area;Wherein, the multi-physical field data includes vibration data, soil temperature data, soil humidity data, soil pH data;The multi-physical field data is input into the preset multi-physical field coupling model, and monitoring data is obtained;Wherein, the preset multi-physical field coupling model is coupled to obtain the physical parameter corresponding to the physical field data, and the monitoring data includes pipeline comprehensive stress data, pipeline corrosion rate data, pipeline anticorrosion coating damage probability;According to the monitoring data, the acquisition frequency of the multi-physical field data is adjusted.According to the embodiment of the application, by coupling the multi-physical field data together, the pipeline corrosion risk can be accurately evaluated, and the pipeline can also be adaptively monitored, and the intelligent degree of monitoring is improved.

Description

Technical Field

[0001] This invention relates to the field of pipeline system technology, and in particular to a cathodic protection monitoring method and device based on multi-physics field coupling. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Pipeline transportation, as a primary mode of transporting energy media such as oil and natural gas, is crucial for safe operation. Pipeline corrosion is one of the main factors threatening pipeline integrity, and cathodic protection, as an effective electrochemical corrosion prevention method, is widely used for the external corrosion protection of buried steel pipelines. However, the effectiveness of cathodic protection systems requires continuous and accurate monitoring data for evaluation and adjustment.

[0004] Currently, pipeline cathodic protection detection mainly relies on manual inspections or fixed-frequency data acquisition equipment. Manual inspections are inefficient, have limited coverage, and are slow to respond; fixed-frequency data acquisition is relatively rigid and is prone to missing critical transient data when encountering sudden disturbances such as third-party construction or geological subsidence, leading to delayed early warnings. Summary of the Invention

[0005] This invention provides a cathodic protection monitoring method based on multi-physics coupling to solve at least some of the problems in the prior art. The method includes: Acquire multi-physics field data of the pipeline monitoring area; wherein, the multi-physics field data includes vibration data, soil temperature data, soil moisture data, and soil pH data; The multiphysics data is input into a preset multiphysics coupling model to obtain monitoring data; wherein, the preset multiphysics coupling model is obtained by coupling the physical parameters corresponding to the physical field data, and the monitoring data includes pipeline comprehensive stress data, pipeline corrosion rate data, and pipeline anti-corrosion coating damage probability; The acquisition frequency of the multiphysics data is adjusted based on the monitoring data.

[0006] Advantageously, the multiphysics coupling model includes a pipeline integrated stress model, a pipeline corrosion rate model, and a pipeline anti-corrosion coating failure probability model, wherein, The pipeline integrated stress model includes a pipeline axial stress model and a pipeline vibration stress model. The pipeline axial stress model is obtained based on the physical parameters corresponding to the soil moisture data, so as to obtain the axial stress data of the pipeline based on the soil moisture data. The pipeline vibration stress model is obtained based on the physical parameters corresponding to the vibration data, so as to obtain the alternating stress data of the pipeline caused by vibration based on the vibration data. The vibration data includes vibration acceleration data and vibration frequency data. The pipeline integrated stress model obtains the pipeline integrated stress data based on the axial stress data and the alternating stress data. The pipeline corrosion rate model is obtained based on the physical parameters corresponding to the soil temperature data, soil moisture data, soil pH data, and pipeline comprehensive stress data. The pipeline corrosion rate model obtains basic corrosion rate data based on the soil temperature data, soil moisture data, and soil pH data, and obtains the pipeline corrosion rate data by weighting the basic corrosion rate data according to the pipeline comprehensive stress data. The pipeline anti-corrosion coating damage probability model is obtained based on the soil temperature data, soil moisture data, soil pH data, and pipeline comprehensive stress data, so as to obtain the pipeline anti-corrosion coating damage probability based on the soil temperature data, soil moisture data, and soil pH data.

[0007] Advantageously, the axial stress model of the pipe is as follows: ,in, For the axial stress of the pipe, The elastic modulus of the pipe material. This represents the actual axial deformation of the pipeline. The allowable deformation under soil constraints is determined by soil moisture. Decide, This is the original length of the pipe; The pipeline vibration stress model is as follows: ,in, For pipeline vibration stress, Let D be the density of the pipe material, D be the outer diameter of the pipe, and a be the vibration acceleration. The actual vibration frequency, The natural frequency of the pipeline, The correction factor is determined by soil moisture. Decide; The pipeline comprehensive stress model is as follows: ,in, This refers to the overall stress in the pipeline.

[0008] Advantageously, the pipeline corrosion rate model is as follows: ,in, For pipeline corrosion rate, Based on the basic corrosion rate model, The stress corrosion sensitivity coefficient is... For the overall stress of the pipeline, This refers to the allowable stress of the pipeline. The basic corrosion rate model is as follows: k is the material coefficient, T is the soil temperature, and pH is the soil acidity / alkalinity. Soil moisture.

[0009] Advantageously, the probability model for damage to the pipeline anti-corrosion coating is as follows: ,in, The value represents the probability of damage to the pipeline's anti-corrosion coating, where k is the material coefficient, T is the soil temperature, and pH is the soil acidity / alkalinity. For soil moisture, For the overall stress of the pipeline, This represents the allowable stress for the pipeline.

[0010] Advantageously, adjusting the acquisition frequency of the multiphysics data based on the monitoring data includes: When the pipeline stress data exceeds a preset stress threshold, the acquisition frequency is increased; or When the pipeline corrosion rate data exceeds a preset rate threshold, the acquisition frequency is increased; or When the probability of damage to the pipeline anti-corrosion coating exceeds a preset probability threshold, the acquisition frequency is increased; or Based on the monitoring data and the multiphysics data, the pipeline corrosion risk is predicted by a preset LSTM model. When the predicted pipeline corrosion risk exceeds the preset prediction value, the acquisition frequency is increased. The preset LSTM model is obtained by training a neural network model based on historical multiphysics data and historical monitoring data.

[0011] This invention also provides a cathodic protection monitoring device based on multiphysics coupling to solve at least some of the problems in the prior art. The device includes: The acquisition module is used to acquire multi-physics field data of the pipeline monitoring area; wherein, the multi-physics field data includes vibration data, soil temperature data, soil moisture data, and soil pH data; The processing module is used to input the multiphysics data into a preset multiphysics coupling model to obtain monitoring data; wherein, the preset multiphysics coupling model is obtained by coupling the physical parameters corresponding to the physical field data, and the monitoring data includes pipeline comprehensive stress data, pipeline corrosion rate data, and pipeline anti-corrosion coating damage probability; An adjustment module is used to adjust the acquisition frequency of the multiphysics data based on the monitoring data.

[0012] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described cathodic protection monitoring method based on multiphysics coupling.

[0013] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described cathodic protection monitoring method based on multi-physics coupling.

[0014] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described cathodic protection monitoring method based on multi-physics coupling.

[0015] According to an embodiment of the present invention, multi-physics field data of a pipeline monitoring area is acquired; wherein, the multi-physics field data includes vibration data, soil temperature data, soil moisture data, and soil pH data; the multi-physics field data is input into a preset multi-physics field coupling model to obtain monitoring data; wherein, the preset multi-physics field coupling model is obtained by coupling the physical parameters corresponding to the physical field data, and the monitoring data includes pipeline comprehensive stress data, pipeline corrosion rate data, and pipeline anti-corrosion coating damage probability; the acquisition frequency of the multi-physics field data is adjusted according to the monitoring data. By coupling the multi-physics field data together, the pipeline corrosion risk can be accurately assessed, and adaptive monitoring of the pipeline can be performed, improving the intelligence of the monitoring. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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. In the drawings: Figure 1 This is a schematic flowchart illustrating an example of a cathodic protection monitoring method based on multiphysics coupling according to an embodiment of the present invention. Figure 2 This is a schematic structural diagram of an example of a cathodic protection monitoring device based on multiphysics coupling according to an embodiment of the present invention; Figure 3 This is a schematic structural diagram of an example of a computer device according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0018] Figure 1 This is a schematic flowchart illustrating an example of a cathodic protection monitoring method based on multiphysics coupling according to an embodiment of the present invention. Figure 1 As shown, the cathodic protection monitoring method based on multiphysics coupling according to an embodiment of the present invention includes: Step 10: Obtain multi-physics data of the pipeline monitoring area; wherein, the multi-physics data includes vibration data, soil temperature data, soil moisture data, and soil pH data; Step 20: Input the multiphysics data into a preset multiphysics coupling model to obtain monitoring data; wherein, the preset multiphysics coupling model is obtained by coupling the physical parameters corresponding to the physical field data, and the monitoring data includes pipeline comprehensive stress data, pipeline corrosion rate data, and pipeline anti-corrosion coating damage probability; Step 30: Adjust the acquisition frequency of the multiphysics data based on the monitoring data.

[0019] According to an embodiment of the present invention, an intelligent monitoring method based on adaptive real-time detection of cathodic protection data is provided, which realizes dynamic adjustment of detection frequency and couples multi-physics field data, enabling comprehensive monitoring of pipeline environment and structural status.

[0020] In some embodiments, advantageously, the multiphysics coupling model includes a pipeline integrated stress model, a pipeline corrosion rate model, and a pipeline anti-corrosion coating failure probability model, wherein... The pipeline integrated stress model includes a pipeline axial stress model and a pipeline vibration stress model. The pipeline axial stress model is obtained based on the physical parameters corresponding to the soil moisture data, so as to obtain the axial stress data of the pipeline based on the soil moisture data. The pipeline vibration stress model is obtained based on the physical parameters corresponding to the vibration data, so as to obtain the alternating stress data of the pipeline caused by vibration based on the vibration data. The vibration data includes vibration acceleration data and vibration frequency data. The pipeline integrated stress model obtains the pipeline integrated stress data based on the axial stress data and the alternating stress data. The pipeline corrosion rate model is obtained based on the physical parameters corresponding to the soil temperature data, soil moisture data, soil pH data, and pipeline comprehensive stress data. The pipeline corrosion rate model obtains basic corrosion rate data based on the soil temperature data, soil moisture data, and soil pH data, and obtains the pipeline corrosion rate data by weighting the basic corrosion rate data according to the pipeline comprehensive stress data. The pipeline anti-corrosion coating damage probability model is obtained based on the soil temperature data, soil moisture data, soil pH data, and pipeline comprehensive stress data, so as to obtain the pipeline anti-corrosion coating damage probability based on the soil temperature data, soil moisture data, and soil pH data.

[0021] In some examples, multiphysics data can be acquired by sensor networks, allowing for simultaneous acquisition and preprocessing of heterogeneous data from multiple sources, thus providing the foundation for model input. Outlier removal (such as abrupt changes caused by sensor drift) and missing value imputation (using interpolation of data from adjacent time points) are performed on the acquired raw data. Data of different dimensions are standardized, for example, mapping parameters such as soil moisture content and pH to reasonable ranges to eliminate the impact of differences in data magnitude on model calculations.

[0022] In some embodiments, the axial stress model of the pipe is optionally: ,in, For the axial stress of the pipe, The elastic modulus (Pa) of the pipe material. This represents the actual axial deformation of the pipeline (m). The allowable deformation (m) under soil constraints is determined by soil moisture. Decide, Soil moisture content, in % The original length of the pipeline (m) is used; this process directly links geological environmental factors with pipeline mechanical stress through soil moisture parameters.

[0023] The pipeline vibration stress model is as follows: ,in, For pipeline vibration stress, Let be the density of the pipe material (kg / m³), D be the outer diameter of the pipe (m), and a be the vibration acceleration (m / s²). The actual vibration frequency (Hz) The natural frequency of the pipe (Hz). The correction factor is determined by soil moisture. The decision was made to combine measured vibration acceleration and frequency with the inherent properties of the pipeline to quantify the attenuation effect of soil moisture on vibration energy, thereby correcting the vibration stress caused by environmental factors.

[0024] The pipeline comprehensive stress model is as follows: ,in, This process integrates stress components from different directions to reflect the actual equivalent destructive stress experienced by the pipeline, providing core parameters for subsequent stress-accelerated corrosion analysis.

[0025] Optionally, Due to soil temperature and humidity It is determined that soil elasticity increases with increasing humidity; the relationship can be expressed as follows: =0.012 +0.003; Soil moisture When >20%, vibration attenuation coefficient =0.8-0.005 Dynamic stress needs to be multiplied by .

[0026] In some embodiments, the pipeline corrosion rate model is optionally: ,in, For pipeline corrosion rate, Based on the corrosion rate model (mm / a). The stress corrosion sensitivity coefficient is... The total stress of the pipeline (N). Allowable stress (N) for the pipeline; optionally, for steel pipelines. = 0.6~1.2, when the pipeline is in acidic soil and subjected to high stress, This doubles the effect, highlighting the accelerated corrosion caused by the superposition of harsh environments and high stress, achieving a deep coupling between mechanical and electrochemical factors. For example, when pH < 5.5 and >0.7 hour, Doubled (susceptibility to stress corrosion increases dramatically in acidic environments).

[0027] The basic corrosion rate model is as follows: k is the material coefficient, T is the soil temperature (°C), and pH is the soil acidity / alkalinity. Soil moisture is represented. An exponential function is used to illustrate the accelerating effect of temperature on the corrosion reaction rate. A piecewise function distinguishes the different degrees of influence of acidic, neutral, and alkaline soils on corrosion. A coating coefficient k is introduced to correct the protective effect of coating integrity on corrosion. In some examples, k=0.025 for carbon steel and k=0.08 after the 3PE coating is damaged.

[0028] In some embodiments, the pipeline anti-corrosion coating failure probability model is optionally: ,in, The value represents the probability of damage to the pipeline's anti-corrosion coating, where k is the material coefficient, T is the soil temperature, and pH is the soil acidity / alkalinity. For soil moisture, For the overall stress of the pipeline, The allowable stress of the pipeline is given. This model quantifies and allocates the weights of various factors on coating aging and damage, directly outputting the probability of coating damage risk, which can provide a quantitative basis for adaptive detection frequency adjustment.

[0029] In some embodiments, step 30, adjusting the acquisition frequency of the multiphysics data based on the monitoring data, may include: When the pipeline stress data exceeds a preset stress threshold, the acquisition frequency is increased; or When the pipeline corrosion rate data exceeds a preset rate threshold, the acquisition frequency is increased; or When the probability of damage to the pipeline anti-corrosion coating exceeds a preset probability threshold, the acquisition frequency is increased; or Based on the monitoring data and the multiphysics data, the pipeline corrosion risk is predicted by a preset LSTM model. When the predicted pipeline corrosion risk exceeds the preset prediction value, the acquisition frequency is increased. The preset LSTM model is obtained by training a neural network model based on historical multiphysics data and historical monitoring data.

[0030] For example, the probability of coating damage can be used as the core control threshold, and the acquisition frequency can be linked to it. a. When the probability of coating damage is <0.1: the pipeline is in a low-risk state, and routine inspections are performed (e.g., once per hour) to reduce equipment energy consumption and communication costs; b. When 0.1 ≤ coating damage probability < 0.3: the pipeline is in a medium-risk state, perform medium-frequency inspections (e.g., once every 10 minutes), and increase data collection density; c. When the probability of coating damage is ≥0.3: the pipeline is in a high-risk state, triggering the highest detection frequency (e.g., 1 time / minute) and capturing instantaneous stress changes.

[0031] For example, when the overall stress in the pipeline is >0.8 If the pipeline corrosion rate is greater than 0.15 mm / a, the detection frequency should be increased. The same principle applies to using other data as the core control threshold, and will not be elaborated further here.

[0032] Optionally, the preset LSTM model can be an LSTM-based pipeline condition assessment and trend prediction model. The model input layer consists of preprocessed multi-source feature data, and the hidden layer has three memory units to capture the temporal correlation and nonlinear mapping relationships between data. The output layer is the predicted value of pipeline corrosion risk level (low / medium / high) and environmental change trend. During the model training phase, historical monitoring data and actual pipeline failure case data can be used as the training set. The model weights are optimized through the backpropagation algorithm to ensure that the risk level output by the model matches the actual working conditions to a preset threshold. After training, the model is deployed to a cloud platform server to support real-time online inference.

[0033] In some other examples, the monitoring data and the multiphysics data can be input into the LSTM model to predict the corrosion risk trend in the next 7 to 30 days. If the LSTM model predicts that the risk continues to rise, the detection frequency can be increased in advance even if the current coating failure probability is less than 0.1. If the predicted risk decreases, the detection frequency can be appropriately reduced to form a closed-loop control of "prediction-control-verification".

[0034] Historical pipeline failure case data (such as stress and environmental parameters at known corrosion perforation locations) can be used to calibrate the model parameters and adjust key parameters such as stress corrosion sensitivity coefficient α and coating coefficient k, so that the matching degree between the model calculation results and the actual failure state reaches a preset threshold (such as above 90%).

[0035] This invention also provides a cathodic protection monitoring device based on multi-physics field coupling, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the cathodic protection monitoring method based on multi-physics field coupling, the implementation of this device can refer to the implementation of the cathodic protection monitoring method based on multi-physics field coupling; repeated details will not be elaborated further.

[0036] Figure 2 This is a schematic structural diagram of an example of a cathodic protection monitoring device based on multiphysics coupling according to an embodiment of the present invention. Figure 2 As shown, the cathodic protection monitoring device based on multiphysics coupling according to an embodiment of the present invention includes: The acquisition module 100 is used to acquire multi-physics field data of the pipeline monitoring area; wherein, the multi-physics field data includes vibration data, soil temperature data, soil moisture data, and soil pH data; The processing module 200 is used to input the multiphysics data into a preset multiphysics coupling model to obtain monitoring data; wherein, the preset multiphysics coupling model is obtained by coupling the physical parameters corresponding to the physical field data, and the monitoring data includes pipeline comprehensive stress data, pipeline corrosion rate data, and pipeline anti-corrosion coating damage probability; The adjustment module 300 is used to adjust the acquisition frequency of the multiphysics data according to the monitoring data.

[0037] This invention also provides a computer device. Figure 3 This is a schematic structural diagram of an example of a computer device according to an embodiment of the present invention. Figure 3 As shown, the computer device 500 according to an embodiment of the present invention includes a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 530, it implements the above-described cathodic protection monitoring method based on multi-physics coupling.

[0038] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described cathodic protection monitoring method based on multi-physics coupling.

[0039] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described cathodic protection monitoring method based on multi-physics coupling.

[0040] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0041] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0042] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0043] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0044] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cathodic protection monitoring method based on multiphysics coupling, used for adaptive real-time detection of cathodic protection data, characterized in that, include: Acquire multi-physics field data of the pipeline monitoring area; wherein, the multi-physics field data includes vibration data, soil temperature data, soil moisture data, and soil pH data; The multiphysics data is input into a preset multiphysics coupling model to obtain monitoring data; wherein, the preset multiphysics coupling model is obtained by coupling the physical parameters corresponding to the physical field data, and the monitoring data includes pipeline comprehensive stress data, pipeline corrosion rate data, and pipeline anti-corrosion coating damage probability; The acquisition frequency of the multiphysics data is adjusted based on the monitoring data. The multiphysics coupling model includes a pipeline comprehensive stress model, a pipeline corrosion rate model, and a pipeline anti-corrosion coating failure probability model, wherein... The pipeline integrated stress model includes a pipeline axial stress model and a pipeline vibration stress model. The pipeline axial stress model is obtained based on the physical parameters corresponding to the soil moisture data, so as to obtain the axial stress data of the pipeline based on the soil moisture data. The pipeline vibration stress model is obtained based on the physical parameters corresponding to the vibration data, so as to obtain the alternating stress data of the pipeline caused by vibration based on the vibration data. The vibration data includes vibration acceleration data and vibration frequency data. The pipeline integrated stress model obtains the pipeline integrated stress data based on the axial stress data and the alternating stress data. The pipeline corrosion rate model is obtained based on the physical parameters corresponding to the soil temperature data, soil moisture data, soil pH data, and pipeline comprehensive stress data. The pipeline corrosion rate model obtains basic corrosion rate data based on the soil temperature data, soil moisture data, and soil pH data, and obtains the pipeline corrosion rate data by weighting the basic corrosion rate data according to the pipeline comprehensive stress data. The pipeline anti-corrosion coating damage probability model is obtained based on the soil temperature data, soil humidity data, soil pH data, and pipeline comprehensive stress data, so as to obtain the pipeline anti-corrosion coating damage probability based on the soil temperature data, soil humidity data, soil pH data, and pipeline comprehensive stress data; The axial stress model of the pipeline is as follows: ,in, For the axial stress of the pipe, The elastic modulus of the pipe material. This represents the actual axial deformation of the pipeline. The allowable deformation under soil constraints is determined by soil moisture. Decide, This is the original length of the pipe; The pipeline vibration stress model is as follows: ,in, For pipeline vibration stress, Let D be the density of the pipe material, D be the outer diameter of the pipe, and a be the vibration acceleration. The actual vibration frequency, The natural frequency of the pipeline, This is a correction factor, derived from soil moisture. Decide; The pipeline comprehensive stress model is as follows: ,in, This refers to the overall stress in the pipeline.

2. The method as described in claim 1, characterized in that, The pipeline corrosion rate model is as follows: ,in, For pipeline corrosion rate, Based on the basic corrosion rate model, The stress corrosion sensitivity coefficient is... For the overall stress of the pipeline, This refers to the allowable stress of the pipeline. The basic corrosion rate model is as follows: k is the material coefficient, T is the soil temperature, and pH is the soil acidity / alkalinity. Soil moisture.

3. The method as described in claim 1, characterized in that, The probability model for damage to the pipeline anti-corrosion coating is as follows: ,in, The value represents the probability of damage to the pipeline's anti-corrosion coating, where T is the soil temperature and pH is the soil acidity / alkalinity. For soil moisture, For the overall stress of the pipeline, This represents the allowable stress for the pipeline.

4. The method according to any one of claims 1-3, characterized in that, Adjusting the acquisition frequency of the multiphysics data based on the monitoring data includes: When the pipeline stress data exceeds a preset stress threshold, the acquisition frequency is increased; or When the pipeline corrosion rate data exceeds a preset rate threshold, the acquisition frequency is increased; or When the probability of damage to the pipeline anti-corrosion coating exceeds a preset probability threshold, the acquisition frequency is increased; or Based on the monitoring data and the multiphysics data, the pipeline corrosion risk is predicted by a preset LSTM model. When the predicted pipeline corrosion risk exceeds the preset prediction value, the acquisition frequency is increased. The preset LSTM model is obtained by training a neural network model based on historical multiphysics data and historical monitoring data.

5. A cathodic protection monitoring device based on multi-physics field coupling, employing the cathodic protection monitoring method based on multi-physics field coupling as described in any one of claims 1-4, characterized in that, include: The acquisition module is used to acquire multi-physics field data of the pipeline monitoring area; wherein, the multi-physics field data includes vibration data, soil temperature data, soil moisture data, and soil pH data; The processing module is used to input the multiphysics data into a preset multiphysics coupling model to obtain monitoring data; wherein, the preset multiphysics coupling model is obtained by coupling the physical parameters corresponding to the physical field data, and the monitoring data includes pipeline comprehensive stress data, pipeline corrosion rate data, and pipeline anti-corrosion coating damage probability; An adjustment module is used to adjust the acquisition frequency of the multiphysics data based on the monitoring data.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 4.

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