Pipeline wall thickness calculation method and device based on magnetic signal change, electronic equipment and storage medium

Abstract

The invention relates to the technical field of pipeline monitoring, and discloses a pipeline wall thickness calculation method and device based on magnetic signal change, electronic equipment and a storage medium, and the method comprises the steps: arranging an external magnetic field around a pipeline, and obtaining the magnetic signal data of the pipeline; performing noise processing on the magnetic signal data to obtain second magnetic signal data; performing normalization processing on the second magnetic signal data to obtain third magnetic signal data; collecting external environment data to correct the third magnetic signal data; performing feature extraction on the corrected third magnetic signal data, and establishing a mathematical relationship model of the magnetic signal and the pipeline wall thickness by using a linear regression method; calculating the wall thickness of the pipeline; and performing trend analysis on the wall thickness of the pipeline based on the historical wall thickness data to obtain predicted wall thickness data, setting a wall thickness safety threshold according to the predicted wall thickness data, and triggering an alarm system if the specific wall thickness value of the current pipeline is lower than the wall thickness safety threshold. The problem that the pipeline wall thickness detection is not convenient and accurate enough in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline monitoring, and in particular to a pipeline wall thickness calculation method, device, electronic equipment and storage medium based on magnetic signal changes. Background Art

[0002] Pipelines play an important role in the industrial and civil fields and are widely used in many industries such as oil, natural gas, water treatment, and electricity. The safety and reliability of pipelines are directly related to production efficiency, environmental protection, and personal safety. As the use of pipelines increases and the external environment changes, the thickness of the pipeline wall may gradually decrease due to corrosion, wear, fatigue, and other factors, which not only affects the bearing capacity of the pipeline, but may also cause pipeline leakage or rupture, leading to major safety accidents. Therefore, regular monitoring and evaluation of pipeline wall thickness is a necessary measure to ensure the safe operation of pipelines.

[0003] Currently, the main methods for detecting pipeline wall thickness include ultrasonic testing: using the difference in the propagation speed of ultrasonic waves in the material to measure the wall thickness of the pipeline. This method has high accuracy, but requires expensive equipment and complex operation. X-ray testing: obtaining internal structural information of the pipeline through X-ray imaging, which is suitable for inspecting welds and defects, but there are radiation risks and high costs. Magnetic testing: mainly used to detect pipelines made of ferromagnetic materials, and determines the pipeline wall thickness by sensing changes, but it also requires complex equipment and high operating techniques.

[0004] In recent years, detection methods based on magnetic signal changes have gradually attracted attention. The magnetic signal change method uses the changes in the magnetic field inside and outside the pipeline to study the changes in the pipeline wall thickness. This method can avoid physical damage to the pipeline, is suitable for various complex environments, can realize online monitoring, and is convenient for real-time acquisition of pipeline status information. Compared with traditional detection methods, the equipment cost is lower and it is easy to promote and apply. Although some studies have shown that there is a certain correlation between magnetic signal changes and pipeline wall thickness, systematic methods are still lacking.

[0005] Therefore, developing a pipeline wall thickness calculation method based on magnetic signal changes to improve the accuracy and convenience of pipeline detection has become a technical problem that needs to be urgently solved in the current pipeline monitoring field. Summary of the invention

[0006] In view of this, the present invention proposes a pipeline wall thickness calculation method, device, electronic device and storage medium based on magnetic signal changes, aiming to solve the problem that pipeline wall thickness detection in current technology is not convenient and accurate enough.

[0007] The present invention proposes a method for calculating pipe wall thickness based on magnetic signal changes, comprising:

[0008] S1: Arrange a uniform external magnetic field around the pipeline and penetrate the pipeline, and use a magnetic field sensor to obtain the magnetic signal data of the pipeline;

[0009] S2: performing noise processing on the magnetic signal data to obtain second magnetic signal data;

[0010] S3: performing normalization processing on the second magnetic signal data to obtain third magnetic signal data;

[0011] S4: Collecting external environment data to correct the third magnetic signal data to obtain corrected third magnetic signal data;

[0012] S5: extracting characteristic parameters from the corrected third magnetic signal data, and establishing a mathematical relationship model between the magnetic signal and the pipe wall thickness using a linear regression method according to the characteristic parameters;

[0013] S6: Bringing the characteristic parameters into the mathematical relationship model between the magnetic signal and the pipe wall thickness to calculate the specific wall thickness value of the current pipe;

[0014] S7: Performing a wall thickness trend analysis on the pipeline wall thickness based on the historical wall thickness data to obtain predicted wall thickness data, setting a wall thickness safety threshold according to the predicted wall thickness data, and triggering an alarm system if the specific wall thickness value of the current pipeline is lower than the wall thickness safety threshold;

[0015] Among them, step S4 specifically involves collecting temperature data of the external environment of the pipeline through a temperature sensor, and performing temperature correction on the third magnetic signal data according to the thermal expansion coefficient of the pipeline material.

[0016] In S1, the external magnetic field is an integrated arrangement of an electromagnet and a magnetic signal enhancer;

[0017] The magnetic field sensor in S1 is any one of a Hall sensor, a fluxgate sensor and a magnetoresistive sensor.

[0018] The specific content of performing noise processing on the magnetic signal data in S2 is:

[0019] The magnetic signal data is obtained, and obvious abnormal values ​​in the magnetic signal data are eliminated by a triple standard deviation method, including:

[0020] Calculate the mean and standard deviation of the magnetic signal data using the formula:

[0021]

[0022]

[0023] Among them, μ is the mean value of magnetic signal data, σ is the standard deviation of magnetic signal data, X i is the i-th magnetic signal data point, and N is the total number of magnetic signal data;

[0024] When |X i When -μ|>3σ, the magnetic signal data of point i is eliminated, and the data of the eliminated position is supplemented according to the linear interpolation method to obtain the supplemented data. The formula is:

[0025]

[0026] Among them, X′ i To complete the data, X i-1 is the previous magnetic signal data point of the i-th magnetic signal data point, X i+1 is the next magnetic signal data point of the i-th magnetic signal data point;

[0027] The complementary data is added to the removed position to obtain the second magnetic signal data.

[0028] The specific content of normalizing the second magnetic signal data in S3 is:

[0029] Normalizing the second magnetic signal data using a minimum-maximum normalization method, replacing the second magnetic signal data before noise processing with the normalized second magnetic signal data and outputting the replaced data to obtain third magnetic signal data;

[0030] The normalization formula is:

[0031]

[0032] Among them, X″ i is the normalized second magnetic signal data, i.e., the third magnetic signal data, X min is the minimum value in the second magnetic signal data, X max is the maximum value in the second magnetic signal data.

[0033] The specific content of the third magnetic signal data obtained by collecting external environment data and correcting the third magnetic signal data in S4 is:

[0034] The temperature data of the external environment of the pipeline is collected by the temperature sensor, and the temperature correction of the third magnetic signal data is performed according to the thermal expansion coefficient of the pipeline material. The formula is:

[0035] X″′=X″ i ×(1+α×(TT 0 ));

[0036] Wherein, X″′ is the corrected third magnetic signal data, X″ i is the third magnetic signal data, α is the linear thermal expansion coefficient of the pipeline material, in °C -1, T is the temperature of the external environment collected by the temperature sensor, in °C, T 0 is the standard temperature in °C.

[0037] The characteristic parameters in S5 include the amplitude and phase of the corrected third magnetic signal data.

[0038] The specific contents of extracting characteristic parameters from the corrected third magnetic signal data in S5, and establishing a mathematical relationship model between the magnetic signal and the pipe wall thickness using a linear regression method according to the characteristic parameters are as follows:

[0039] The amplitude and phase of the corrected third magnetic signal data are extracted by using a wavelet transformation method, and a mathematical relationship model between the magnetic signal and the pipe wall thickness is established by using a linear regression method according to the amplitude and phase of the corrected third magnetic signal data, including:

[0040] The amplitude and phase of the corrected third magnetic signal data are obtained using the formula:

[0041] A(t)=|W s (a,b)|;

[0042] Wherein, A(t) is the amplitude of the third magnetic signal data after correction; W s (a, b) Wavelet transform coefficients at scale a and position b;

[0043] φ(t)=arg(W s (a,b));

[0044] Wherein, φ(t) is the phase of the corrected third magnetic signal data;

[0045]

[0046] Among them, a is the scale factor, b is the translation factor, is the complex conjugate of the mother wavelet;

[0047] A mathematical relationship model between the magnetic signal and the pipe wall thickness is established by using a linear regression method according to the amplitude and phase of the corrected third magnetic signal data. The formula is:

[0048] d=β 0 +β 1 A(t)+β 2 φ(t)+∈;

[0049] Where d is the pipe wall thickness, β 0 is the intercept term of the regression model, β 1 is the first regression coefficient related to the amplitude, β 2 is the second regression coefficient related to the phase, and ∈ is the random error term.

[0050] The magnetic field sensors in S1 are arranged evenly along the axial direction of the pipeline.

[0051] The frequency of collecting the magnetic signal data in S1 is adjustable.

[0052] In S7, based on the historical wall thickness data, a moving average method is used to perform wall thickness trend analysis on the pipeline wall thickness to obtain predicted wall thickness data.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention discloses a pipeline wall thickness calculation method based on magnetic signal change, which can improve the accuracy of pipeline wall thickness detection: by using an external magnetic field uniformly arranged around the pipeline and a high-precision magnetic field sensor, the magnetic signal data of the pipeline can be accurately obtained. The present invention combines noise processing, normalization and temperature correction technology, significantly improves the accuracy of wall thickness measurement, and reduces the influence of environmental factors on the detection results. The present invention can enhance the real-time monitoring capability: the present invention realizes online monitoring of pipeline wall thickness, can obtain the current wall thickness value in real time, and timely reflect the state change of the pipeline. This is of great significance for early discovery of potential risks of pipelines and prevention of accidents. The present invention can effectively analyze and predict trends: by analyzing historical wall thickness data and using the moving average method to predict trends, a scientific basis can be provided for subsequent pipeline maintenance and management. The present invention can help relevant personnel formulate maintenance plans in advance and reduce maintenance costs. The present invention can realize an intelligent alarm system: when the current pipeline wall thickness value is lower than the set safety threshold, the system can automatically trigger an alarm, and timely remind relevant management personnel to conduct inspections and maintenance, further improving the safety and reliability of the pipeline. The present invention can simplify the operation process: by introducing the wavelet transform method for signal processing and feature extraction, the data processing process is simplified, making the calculation method of pipeline wall thickness more efficient and easy to operate. This reduces the requirements for the professional skills of operators, making the technology easy to promote and apply. The present invention can reduce the detection cost: the detection method based on magnetic signal changes can greatly reduce the equipment procurement and maintenance costs compared to traditional detection methods. The implementation of the present invention can improve the quality and frequency of pipeline monitoring without increasing too much investment. The present invention is suitable for various types of pipelines, whether new or old, and can effectively monitor the changes in their wall thickness. It has good adaptability and broad application prospects. The present invention adopts a linear regression model, which can flexibly adjust the model according to the actual magnetic signal characteristic parameters, thereby improving the correlation between wall thickness and magnetic signals, making the results more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0056] Figure 1 A flow chart of a method for calculating pipe wall thickness based on magnetic signal changes provided by an embodiment of the present invention;

[0057] Figure 2 It is a structural schematic diagram of a pipeline wall thickness calculation device based on magnetic signal changes provided by an embodiment of the present invention;

[0058] Figure 3 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0060] The pipeline wall thickness calculation method based on magnetic signal changes analyzes the changing law of magnetic signals and establishes a quantitative relationship between wall thickness and magnetic signals, providing effective technical support for pipeline safety monitoring. Through this method, it is expected to achieve real-time and accurate detection of pipeline wall thickness to ensure the safe and reliable operation of the pipeline system.

[0061] See also Figure 1 As shown, an embodiment of the present invention provides a method for calculating pipe wall thickness based on magnetic signal changes, including:

[0062] S1: Arrange a uniform external magnetic field around the pipeline that penetrates the pipeline, and use a magnetic field sensor to obtain the magnetic signal data of the pipeline. The uniformly arranged external magnetic field ensures that the magnetic signals around the pipeline can be fully monitored, reducing local interference and improving measurement accuracy.

[0063] S2: performing noise processing on the magnetic signal data to obtain second magnetic signal data. The noise processing can effectively eliminate irrelevant interference signals, making subsequent data analysis more accurate.

[0064] S3: performing normalization processing on the second magnetic signal data to obtain third magnetic signal data. The normalization processing converts the data into a unified range, so that different data features are comparable in analysis, reducing errors caused by different dimensions, and the normalized data is more suitable for machine learning and statistical analysis, which helps to improve the convergence speed and accuracy of the model;

[0065] S4: Collecting external environment data to correct the third magnetic signal data to obtain corrected third magnetic signal data;

[0066] S5: extracting characteristic parameters from the corrected third magnetic signal data, and establishing a mathematical relationship model between the magnetic signal and the pipe wall thickness using a linear regression method according to the characteristic parameters;

[0067] S6: Bring the characteristic parameters into the mathematical relationship model between the magnetic signal and the pipeline wall thickness to calculate the specific wall thickness value of the current pipeline. By calculating the wall thickness of the current pipeline in real time, potential problems can be discovered in time, the safety and reliability of the pipeline can be improved, and accurate wall thickness data can be provided to support subsequent maintenance decisions and help operation managers formulate reasonable maintenance plans;

[0068] S7: Performing a wall thickness trend analysis on the pipeline wall thickness based on the historical wall thickness data to obtain predicted wall thickness data, setting a wall thickness safety threshold according to the predicted wall thickness data, and triggering an alarm system if the specific wall thickness value of the current pipeline is lower than the wall thickness safety threshold;

[0069] Among them, step S4 specifically involves collecting temperature data of the external environment of the pipeline through a temperature sensor, and performing temperature correction on the third magnetic signal data according to the thermal expansion coefficient of the pipeline material.

[0070] Furthermore, in S1, the external magnetic field is an integrated arrangement of an electromagnet and a magnetic signal enhancer;

[0071] The magnetic field sensor in S1 is any one of a Hall sensor, a fluxgate sensor and a magnetoresistive sensor. The selection of multiple sensors provides flexibility for different application scenarios, making the method more adaptable and able to be adjusted according to actual needs.

[0072] Furthermore, the specific content of performing noise processing on the magnetic signal data in S2 is:

[0073] The magnetic signal data is obtained, and obvious abnormal values ​​in the magnetic signal data are eliminated by a triple standard deviation method, including:

[0074] Calculate the mean and standard deviation of the magnetic signal data using the formula:

[0075]

[0076]

[0077] Among them, μ is the mean value of magnetic signal data, σ is the standard deviation of magnetic signal data, X i is the i-th magnetic signal data point, and N is the total number of magnetic signal data;

[0078] Specifically, by calculating the mean and standard deviation of the magnetic signal data at each measurement point, the triple standard deviation method can effectively eliminate extreme values. This is because in a normal distribution, 95% of the data should fall within the range of ±2 standard deviations from the mean, and the triple standard deviation further limits the occurrence of extreme values, ensuring the representativeness and reliability of the data.

[0079] If the magnetic signal data at a certain measurement point is [7.2, 7.0, 7.4, 85.0] (assuming 85.0 is an outlier), this method can eliminate 85.0 to ensure that subsequent calculations are based on reasonable data and avoid individual outliers affecting the entire measurement result.

[0080] When |X i When -μ|>3σ, the magnetic signal data of point i is eliminated, and the data of the eliminated position is supplemented according to the linear interpolation method to obtain the supplemented data. The integrity of the data set is ensured through the supplementation process, and the analysis deviation caused by abnormal values ​​is reduced. The formula is:

[0081]

[0082] Among them, X′ i To complete the data, X i-1 is the previous magnetic signal data point of the i-th magnetic signal data point, X i+1 is the next magnetic signal data point of the i-th magnetic signal data point;

[0083] The complementary data is added to the removed position to obtain the second magnetic signal data.

[0084] Specifically, assuming that a certain value is omitted in a measurement sequence, and the previous and next values ​​are 7.2 and 7.4 respectively, interpolation can generate 7.3 as the completion value, thereby ensuring that the data record of the measurement point is continuous, which is crucial for subsequent trend analysis and monitoring.

[0085] Furthermore, the specific content of normalizing the second magnetic signal data in S3 is:

[0086] Normalizing the second magnetic signal data using a minimum-maximum normalization method, replacing the second magnetic signal data before noise processing with the normalized second magnetic signal data and outputting the replaced data to obtain third magnetic signal data;

[0087] The normalization formula is:

[0088]

[0089] Among them, X″ i is the normalized second magnetic signal data, i.e., the third magnetic signal data, X min is the minimum value in the second magnetic signal data, X max is the maximum value in the second magnetic signal data.

[0090] Specifically, the application of minimum-maximum normalization is:

[0091] By normalizing the magnetic signal data to a range of 0 to 1, the problem of different data magnitudes at different measurement points due to environmental changes or equipment differences can be eliminated. Normalized data can be more easily compared and analyzed, especially in subsequent model construction, avoiding algorithm instability or performance degradation caused by differences in data magnitude.

[0092] After the magnetic signal data at different measurement points are normalized, they can be mapped to the interval [0,1] regardless of the original magnitude of the data, so that when data fusion is performed, the contribution of each measurement point is balanced, ensuring the fairness and accuracy of the analysis.

[0093] The effect of comprehensive noise processing:

[0094] Through the above steps, the entire data processing process becomes more robust. Since the elimination and completion process can effectively eliminate sudden external interference and measurement errors, effective data can be stably obtained in complex environments, enhancing the anti-interference ability of the monitoring equipment.

[0095] Risk analysis supported by accurate data:

[0096] In the subsequent wall thickness trend analysis and risk assessment, the processed data provides an accurate basis, making the assessment of historical data trends more timely and effective. Accurate wall thickness data can help engineers detect abnormal changes in pipelines early, implement corresponding maintenance measures, and reduce potential safety hazards.

[0097] If trend analysis finds that the wall thickness at a certain measuring point is continuously decreasing, and data processing confirms that the data is reliable, the maintenance process can be initiated in a timely manner to prevent possible pipeline leakage or rupture incidents in the future, thereby reducing economic losses and environmental risks.

[0098] Furthermore, the specific content of the corrected third magnetic signal data obtained by collecting the external environment data in S4 is:

[0099] It should be noted that the introduction of temperature correction helps to correct the deviation of magnetic signal data caused by temperature changes, thereby improving the accuracy of the measurement results and making the corrected third magnetic signal data closer to the actual situation. By considering the linear expansion coefficient and pressure factors of the pipeline, the measurement error caused by environmental changes can be effectively reduced, the reliability of the data can be enhanced, and a more solid foundation can be provided for subsequent data analysis. The calculation method of the temperature correction coefficient allows adaptive adjustment under different environmental conditions to ensure that stable measurement results can still be obtained in a variable temperature environment. The accurate corrected third magnetic signal data provides a more reliable basis for subsequent trend analysis and risk assessment, helps to identify potential safety hazards in advance, and improves the efficiency of pipeline management and maintenance. The correction considering temperature and pressure factors can prevent the risk of pipeline damage caused by changes in environmental conditions to a certain extent, and ensure the safety and stability of the pipeline during operation. The introduction of temperature correction coefficient and its related parameter calculation makes monitoring more intelligent, can respond to environmental changes in real time, and achieve more accurate monitoring and management.

[0100] The temperature data of the external environment of the pipeline is collected by the temperature sensor. The temperature correction can eliminate the signal error caused by environmental changes, ensuring that the obtained magnetic signal data is more reliable. The third magnetic signal data is temperature corrected according to the thermal expansion coefficient of the pipeline material. Taking into account the thermal expansion characteristics of the pipeline material, the measurement results are more in line with the actual situation, which enhances the scientific nature of the method. The formula is:

[0101] X″′=X″ i ×(1+α×(TT 0 ));

[0102] Wherein, X″′ is the corrected third magnetic signal data, X″ i is the third magnetic signal data, α is the linear thermal expansion coefficient of the pipeline material, in °C -1 , T is the temperature of the external environment collected by the temperature sensor, in °C, T 0 is the standard temperature in °C.

[0103] Furthermore, by introducing the environmental temperature factor, the necessary correction can be made to the magnetic signal data, so that the corrected third magnetic signal data is closer to the actual situation. This is because environmental factors are very important in the operation of the pipeline and directly affect the wall thickness performance of the pipeline. The increase in temperature may cause thermal expansion of the pipeline material. Through the corresponding correction, the actual wall thickness of the pipeline can be more accurately reflected. Through a unified correction formula, the magnetic signal data of different measuring points can be compared under the same standard. For data obtained under different environmental conditions, the impact caused by environmental differences can be eliminated after correction, which enhances the comparability between data. The corrected data provides a reliable basis for subsequent trend analysis and risk assessment, and helps to make effective predictions and interventions before potential problems occur. For example, if the corrected data shows that the wall thickness of a certain measuring point decreases faster, the relevant management department can take maintenance measures in time to prevent potential pipeline leakage or rupture events. In multiple measurements and environmental changes, corrections based on environmental parameters help reduce measurement errors caused by environmental fluctuations, make monitoring data more consistent, and thus enhance stability. For example, in the case of long-term monitoring, real-time correction of environmental parameters can ensure the continued reliability of monitoring results, allowing the maintenance team to rely on this data for longer-term management and maintenance.

[0104] By collecting and correcting environmental parameters, the accuracy, stability and comparability of pipeline wall thickness monitoring can be effectively improved, risk assessment capabilities can be enhanced, and reliability of operation in complex temperature environments can be ensured. These beneficial effects not only improve the quality of monitoring data, but also provide important guarantees for the safe operation of pipelines.

[0105] Furthermore, the characteristic parameters in S5 include the amplitude and phase of the corrected third magnetic signal data.

[0106] Furthermore, the specific contents of extracting characteristic parameters of the corrected third magnetic signal data in S5 and establishing a mathematical relationship model between the magnetic signal and the pipe wall thickness using a linear regression method according to the characteristic parameters are as follows:

[0107] The amplitude and phase of the corrected third magnetic signal data are extracted by wavelet transformation. The amplitude and phase are extracted by wavelet transformation, which effectively obtains useful information in the signal and improves the prediction ability of the model. According to the amplitude and phase of the corrected third magnetic signal data, a linear regression method is used to establish a mathematical relationship model between the magnetic signal and the pipe wall thickness. The linear regression model provides a theoretical basis for subsequent wall thickness calculation, establishes a direct relationship between the magnetic signal and the wall thickness, and improves the application value of the method, including:

[0108] The amplitude and phase of the corrected third magnetic signal data are obtained using the formula:

[0109] A(t)=|W s (a,b)|;

[0110] Wherein, A(t) is the amplitude of the third magnetic signal data after correction; W s (a, b) Wavelet transform coefficients at scale a and position b;

[0111] φ(t)=arg(W s (a,b));

[0112] Wherein, φ(t) is the phase of the corrected third magnetic signal data;

[0113]

[0114] Among them, a is the scale factor, b is the translation factor, is the conjugate complex number of the mother wavelet, t is the integral variable, and X″′ is the corrected third magnetic signal data;

[0115] A mathematical relationship model between the magnetic signal and the pipe wall thickness is established by using a linear regression method according to the amplitude and phase of the corrected third magnetic signal data. The formula is:

[0116] d=β 0 +β 1 A(t)+β 2 φ(t)+∈;

[0117] Where d is the pipe wall thickness, β 0 is the intercept term of the regression model, β 1 is the first regression coefficient related to the amplitude, β 2 is the second regression coefficient related to the phase, and ∈ is the random error term.

[0118] It should be noted that linear regression is a basic and widely used statistical method with simple calculation and easy implementation. Compared with complex nonlinear models, linear regression can quickly obtain results, which is conducive to real-time data processing and monitoring; linear regression has good stability and can still maintain good prediction effects under data noise or slight abnormalities, ensuring the stability of pipeline wall thickness detection. At the same time, if the data points are reasonably distributed, linear regression can well fit the relationship between magnetic signals and wall thickness; linear regression models are easy to adjust and optimize, and the accuracy of the model can be improved by adding or removing specific variables. Various regularization techniques (such as Lasso or Ridge) can also be conveniently used to avoid overfitting and improve the generalization ability of the model; linear regression is highly efficient when processing large amounts of data, and is suitable for large amounts of magnetic signal data continuously acquired in real-time monitoring systems, which helps to quickly establish and update models and improve the accuracy of pipeline wall thickness prediction.

[0119] Furthermore, the magnetic field sensors in S1 are arranged evenly along the axial direction of the pipeline.

[0120] Specifically, according to the diameter of the pipeline and the area to be monitored, select an appropriate number of sensors. The sensors should be evenly placed around the outer surface of the pipeline to form a sensor network around the pipeline. Ensure that the position of the sensors can cover all key areas of the pipeline. During installation, ensure that all sensors are at the same height to avoid inconsistent data due to height differences. For example, for a pipeline with a diameter of 100 mm, it is recommended to install 5-10 sensors. Evenly arrange the sensors around the pipeline and ensure that the distance between adjacent sensors is as consistent as possible.

[0121] It should be noted that the uniformly distributed sensor layout along the pipeline axis ensures comprehensive monitoring of the magnetic field around the pipeline, which helps to capture tiny magnetic signal changes caused by uneven pipeline wall thickness; this layout design can improve measurement accuracy, reduce errors caused by local magnetic field interference, and make wall thickness calculation more accurate; the uniform distribution of sensors can quickly respond to changes in the environment around the pipeline, such as temperature and pressure fluctuations, and adjust data processing strategies in time; the uniform arrangement of sensors reduces the impact of single sensor failure on the overall monitoring system, and improves the reliability and stability of the system.

[0122] Furthermore, the frequency of collecting the magnetic signal data in S1 is adjustable.

[0123] Specifically, the frequency of collecting magnetic signal data can be set to once every 5 seconds to ensure the real-time nature of the data.

[0124] It should be noted that the adjustable acquisition frequency can be flexibly adjusted according to actual needs and environmental conditions to improve the adaptability of the system. For example, when the pipeline is maintained or monitored, the frequency can be increased to obtain more data; by reducing the acquisition frequency when high-frequency data is not needed, the system can save power and data storage resources and extend the service life of the equipment; using high-frequency acquisition during abnormal monitoring or critical moments can capture instantaneous changes, ensure the accuracy and integrity of the data, and thus improve the reliability of subsequent analysis; flexible acquisition frequency settings enable the system to respond quickly and increase the speed of data acquisition when a fault or abnormal situation occurs, and provide alarms or warning information in a timely manner; the adjustable acquisition frequency setting provides users with greater flexibility, allowing operators to easily adjust the system configuration according to specific monitoring needs and improve the user experience.

[0125] Furthermore, in S7, based on the historical wall thickness data, a moving average method is used to perform a wall thickness trend analysis on the pipeline wall thickness to obtain predicted wall thickness data.

[0126] Specifically, the moving average method is used to obtain the predicted wall thickness data, and the formula is:

[0127]

[0128] Among them, T 预测 To predict the wall thickness data, T 历史 (i) is the i-th historical wall thickness data, M is the number of samples of the historical wall thickness data, M can be set to all the historical wall thickness data, or it can be set to the most recent M historical wall thickness data, and it can be set specifically according to actual conditions, and this application does not impose any specific restrictions on this.

[0129] Furthermore, the specific wall thickness value of the current pipeline is monitored in real time, and it is regularly compared with the set safety threshold. The safety threshold is regularly adjusted according to the use of the pipeline and changes in the external environment. If the wall thickness value of the current pipeline is lower than the set safety threshold, the alarm system is triggered and an alarm is sounded. The alarm method can include sound and light alarm, SMS notification, email notification, etc., to ensure that relevant personnel can respond in time. After the alarm is triggered, the system can automatically record the alarm event and generate a report for subsequent analysis and investigation. After receiving the alarm, the pipeline maintenance personnel should quickly go to the site to check, evaluate the pipeline condition, and perform necessary maintenance or replacement operations.

[0130] It should be noted that trend analysis and safety threshold setting based on historical data can identify possible risks in pipeline wall thickness in advance and ensure safe operation of the pipeline; by analyzing historical data, managers can formulate maintenance and operation strategies more scientifically and improve management efficiency; real-time monitoring and alarm systems can respond to potential safety hazards in a timely manner, thereby reducing the probability of accidents and protecting the safety of people and property; by dynamically adjusting the safety threshold, it can ensure that the pipeline can maintain a safe state under various operating conditions and improve overall operational efficiency.

[0131] Furthermore, the present invention also includes monitoring the pipeline in the deep sea or the rapid river area, where the environment changes frequently. The stability of the monitoring data can be ensured by real-time correction, and the safety monitoring capability of the pipeline can be improved. At this time, the external environment data in the correction of the third magnetic signal data by collecting external environment data in S4 is the water flow velocity and the water depth;

[0132] The water flow velocity outside the pipeline is collected by the water flow velocity sensor, and the water flow velocity correction is performed on the third magnetic signal data. The formula is:

[0133] X″′=X″ i ×(1+β×(VV 0 ))

[0134] Among them, β is the correction coefficient of the influence of water flow velocity on magnetic signal, V is the current water flow velocity, V 0 is the standard water velocity, X″′ and X i″ are respectively the magnetic signal data after correction and the magnetic signal data before correction;

[0135] It should be noted that the role of the water flow rate correction in correcting the third magnetic signal data is:

[0136] Improve measurement accuracy: Water velocity correction takes into account the effect of flow velocity on magnetic signals, ensuring that the corrected data can truly reflect the actual wall thickness of the pipe. This improvement in accuracy allows for high reliability under different water flow conditions, effectively avoiding measurement errors caused by changes in flow velocity.

[0137] Reduce environmental interference: In an underwater environment, changes in flow rate may cause fluctuations in the measurement signal. By introducing a water flow rate correction factor, the interference of flow rate on the magnetic signal can be effectively filtered out, thereby significantly reducing the impact of environmental noise on the monitoring results, making the data more stable and reliable.

[0138] Enhanced adaptability: The application of water velocity correction makes it more adaptable when facing complex underwater environments. Whether it is still water or strong current conditions, the consistency of data can be maintained through real-time correction, thereby improving the versatility in practical applications.

[0139] Ensure pipeline safety: Through accurate water flow rate correction, abnormal conditions that may occur in the pipeline can be discovered in advance, and maintenance measures can be taken in time. In this way, the risk of pipeline damage or leakage caused by inaccurate measurement can be effectively prevented, ensuring the safety and reliability of the pipeline in various underwater environments.

[0140] Improved credibility: Accurate water velocity correction methods improve the credibility of monitoring. This is crucial for promoting new technologies and gaining user recognition, and helps to promote the widespread application and popularization of technologies.

[0141] The water depth sensor is used to collect the water position of the pipeline, and the water depth correction is performed on the third magnetic signal data. The formula is:

[0142] X″′=X″ i ×(1+γ×(DD 0 ))

[0143] Among them, γ is the correction coefficient of water depth to magnetic signal, D is the current depth of the pipeline in water, and D 0 is the standard detection depth, X″′ and X i ″ are the magnetic signal data after correction and the magnetic signal data before correction respectively.

[0144] It should be noted that the introduction of water depth correction is to ensure that the magnetic data can be effectively corrected at different measurement depths and eliminate data deviations caused by depth changes.

[0145] By correcting the depth, the impact of different measurement depths on magnetic data can be effectively reduced, ensuring that the data of each measurement point are comparable when the depth is inconsistent. Since the depth correction takes into account the impact of depth differences on measurement accuracy, more accurate wall thickness data can be obtained under measurement conditions at different depths. Changes in depth have a cumulative effect on wall thickness measurement. Through depth correction, this cumulative error can be minimized to ensure the consistency of data at different depths. By correcting the depth and water density, the corrected wall thickness data is more stable and less affected by environmental factors, which is helpful for subsequent data analysis and processing. This method can automatically correct according to different measurement depths and is suitable for wall thickness data acquisition and processing in various underwater environments.

[0146] After the above corrections, measurements taken in environments with faster and slower water flow can obtain directly comparable results after correction, thereby effectively evaluating the health status of each measurement point. The water flow rate and depth of the underwater environment are often variable. By real-time monitoring of these environmental parameters and performing data correction, it is possible to adapt to different underwater conditions. This enables efficient working conditions to be maintained under various complex conditions. For example, when monitoring in the deep sea or rapid river areas, the environment changes frequently. Real-time correction can ensure the stability of the monitoring data and improve the safety monitoring capabilities of the pipeline. The corrected data provides a reliable basis for subsequent trend analysis and risk assessment, and helps to effectively predict and intervene before potential problems arise.

[0147] In summary, the pipeline wall thickness calculation method based on magnetic signal changes of the present invention can improve the accuracy of pipeline wall thickness detection: by using an external magnetic field uniformly arranged around the pipeline and a high-precision magnetic field sensor, the magnetic signal data of the pipeline can be accurately obtained. The present invention combines noise processing, normalization and temperature correction technology, which significantly improves the accuracy of wall thickness measurement and reduces the impact of environmental factors on the detection results; it can enhance real-time monitoring capabilities: the present invention realizes online monitoring of pipeline wall thickness, can obtain the current wall thickness value in real time, and promptly reflect the state changes of the pipeline. This is of great significance for early discovery of potential risks of pipelines and prevention of accidents; it can effectively analyze and predict trends: by analyzing historical wall thickness data and using the moving average method for trend prediction, it can provide a scientific basis for subsequent pipeline maintenance and management. The present invention can help relevant personnel to formulate maintenance plans in advance and reduce maintenance costs; it can realize an intelligent alarm system: when the current pipeline wall thickness value is lower than the set safety threshold, the system can automatically trigger an alarm, and promptly remind relevant management personnel to conduct inspections and maintenance, further improving the safety and reliability of the pipeline; it can simplify the operation process: by introducing the wavelet transform method for signal processing and feature extraction, the data processing process is simplified, making the calculation method of pipeline wall thickness more efficient and easy to operate. This reduces the requirements for the professional skills of operators and makes the technology easy to promote and apply; it can reduce the detection cost: the detection method based on magnetic signal changes can greatly reduce the equipment procurement and maintenance costs compared with traditional detection methods. The implementation of the present invention can improve the quality and frequency of pipeline monitoring without increasing too much investment; strong adaptability: the present invention is applicable to various types of pipelines, whether new pipelines or old pipelines, and can effectively monitor their wall thickness changes, with good adaptability and broad application prospects; flexibility of data processing and analysis: due to the use of a linear regression model, the model can be flexibly adjusted according to the actual magnetic signal characteristic parameters, thereby improving the correlation between wall thickness and magnetic signals, making the results more reliable.

[0148] Figure 2 The present invention provides a schematic diagram of a pipe wall thickness calculation device based on magnetic signal changes in an embodiment of the present invention. The pipe wall thickness calculation device based on magnetic signal changes in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal and / or server to implement the pipe wall thickness calculation method based on magnetic signal changes in an embodiment of the present invention. The device may specifically include: a magnetic signal data acquisition module 310, a noise processing module 320, a data normalization processing module 330, a magnetic signal data correction module 340, a characteristic parameter extraction module 350, a wall thickness value calculation module 360, and an alarm module 370.

[0149] Among them, the magnetic signal data acquisition module 310 is used to arrange a uniform external magnetic field around the pipeline and penetrate the pipeline, and use a magnetic field sensor to acquire the magnetic signal data of the pipeline; the noise processing module 320 is used to perform noise processing on the magnetic signal data to obtain second magnetic signal data; the data normalization processing module 330 is used to perform normalization processing on the second magnetic signal data to obtain third magnetic signal data; the magnetic signal data correction module 340 is used to collect external environment data to correct the third magnetic signal data to obtain corrected third magnetic signal data; the collection of external environment data to correct the third magnetic signal data to obtain corrected third magnetic signal data includes: collecting temperature data of the external environment of the pipeline through a temperature sensor The third magnetic signal data is used for temperature correction according to the thermal expansion coefficient of the pipeline material; a characteristic parameter extraction module 350 is used to extract characteristic parameters of the corrected third magnetic signal data, and establish a mathematical relationship model between the magnetic signal and the pipeline wall thickness using a linear regression method according to the characteristic parameters; a wall thickness value calculation module 360 ​​is used to bring the characteristic parameters into the mathematical relationship model between the magnetic signal and the pipeline wall thickness to calculate the specific wall thickness value of the current pipeline; an alarm module 370 is used to perform a wall thickness trend analysis on the pipeline wall thickness based on the historical wall thickness data to obtain predicted wall thickness data, set a wall thickness safety threshold according to the predicted wall thickness data, and trigger an alarm system if the specific wall thickness value of the current pipeline is lower than the wall thickness safety threshold.

[0150] The technical solution of this embodiment can accurately obtain the magnetic signal data of the pipeline by applying the external magnetic field and high-precision magnetic field sensors uniformly arranged around the pipeline. The present invention combines noise processing, normalization and temperature correction technology, significantly improves the accuracy of wall thickness measurement, and reduces the influence of environmental factors on the detection results. The present invention can enhance the real-time monitoring capability: the present invention realizes online monitoring of pipeline wall thickness, can obtain the current wall thickness value in real time, and timely reflect the state change of the pipeline. This is of great significance for early discovery of potential risks of pipelines and prevention of accidents. The present invention can effectively analyze and predict trends: by analyzing historical wall thickness data and using the moving average method to predict trends, a scientific basis can be provided for subsequent pipeline maintenance and management. The present invention can help relevant personnel formulate maintenance plans in advance and reduce maintenance costs. The present invention can realize an intelligent alarm system: when the current pipeline wall thickness value is lower than the set safety threshold, the system can automatically trigger an alarm, and promptly remind relevant management personnel to conduct inspections and maintenance, further improving the safety and reliability of the pipeline. The present invention can simplify the operation process: by introducing the wavelet transform method for signal processing and feature extraction, the data processing process is simplified, making the calculation method of pipeline wall thickness more efficient and easy to operate. This reduces the requirements for the professional skills of operators, making the technology easy to promote and apply. The present invention can reduce the detection cost: the detection method based on the change of magnetic signals can greatly reduce the equipment procurement and maintenance costs compared with traditional detection methods. The implementation of the present invention can improve the quality and frequency of pipeline monitoring without increasing too much investment. The present invention is suitable for various types of pipelines, whether new or old, and can effectively monitor the changes in their wall thickness. It has good adaptability and broad application prospects. The present invention adopts a linear regression model, which can flexibly adjust the model according to the actual magnetic signal characteristic parameters, thereby improving the correlation between wall thickness and magnetic signals, making the results more reliable.

[0151] The above-mentioned pipeline wall thickness calculation device based on magnetic signal changes can execute the pipeline wall thickness calculation method based on magnetic signal changes provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the pipeline wall thickness calculation method based on magnetic signal changes.

[0152] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the electronic device includes a processor 410, a memory 420, an input device 430, and an output device 440; the number of processors 410 in the device can be one or more. Figure 3 A processor 410 is taken as an example; the processor 410, memory 420, input device 430 and output device 440 in the device can be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0153] The memory 420, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to a method for calculating pipe wall thickness based on magnetic signal changes in an embodiment of the present invention. The processor 410 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 420.

[0154] The memory 420 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 420 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 420 may further include a memory remotely arranged relative to the processor 410, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0155] The input device 430 may be used to receive input digital or character information and generate signal input related to user settings and function control of the device. The output device 440 may include a display device such as a display screen.

[0156] The embodiment of the present invention also provides a storage medium containing computer executable instructions, which are used to execute a pipeline wall thickness calculation method based on magnetic signal changes when executed by a computer processor. The method includes: S1: arranging a uniform external magnetic field around the pipeline and penetrating the pipeline, and using a magnetic field sensor to obtain magnetic signal data of the pipeline; S2: performing noise processing on the magnetic signal data to obtain second magnetic signal data; S3: performing normalization processing on the second magnetic signal data to obtain third magnetic signal data; S4: collecting external environment data to correct the third magnetic signal data to obtain corrected third magnetic signal data; S5: performing normalization processing on the corrected third magnetic signal data The characteristic parameters are extracted according to the historical wall thickness data, and a mathematical relationship model between the magnetic signal and the pipe wall thickness is established by a linear regression method according to the characteristic parameters; S6: the characteristic parameters are brought into the mathematical relationship model between the magnetic signal and the pipe wall thickness to calculate the specific wall thickness value of the current pipe; S7: a wall thickness trend analysis is performed on the pipe wall thickness based on the historical wall thickness data to obtain predicted wall thickness data, a wall thickness safety threshold is set according to the predicted wall thickness data, and an alarm system is triggered if the specific wall thickness value of the current pipe is lower than the wall thickness safety threshold; wherein, step S4 specifically involves collecting temperature data of the external environment of the pipe through a temperature sensor, and performing temperature correction on the third magnetic signal data according to the thermal expansion coefficient of the pipe material.

[0157] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0158] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0159] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0160] Computer program code for performing the operation of embodiments of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0161] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for calculating pipe wall thickness based on magnetic signal changes, characterized in that: include: S1: Arrange a uniform external magnetic field around the pipeline and penetrate the pipeline, and use a magnetic field sensor to obtain the magnetic signal data of the pipeline; S2: performing noise processing on the magnetic signal data to obtain second magnetic signal data; S3: performing normalization processing on the second magnetic signal data to obtain third magnetic signal data; S4: Collecting external environment data to correct the third magnetic signal data to obtain corrected third magnetic signal data; S5: extracting characteristic parameters from the corrected third magnetic signal data, and establishing a mathematical relationship model between the magnetic signal and the pipe wall thickness using a linear regression method according to the characteristic parameters; S6: Bringing the characteristic parameters into the mathematical relationship model between the magnetic signal and the pipe wall thickness to calculate the specific wall thickness value of the current pipe; S7: Performing a wall thickness trend analysis on the pipeline wall thickness based on the historical wall thickness data to obtain predicted wall thickness data, setting a wall thickness safety threshold according to the predicted wall thickness data, and triggering an alarm system if the specific wall thickness value of the current pipeline is lower than the wall thickness safety threshold; Among them, step S4 specifically involves collecting temperature data of the external environment of the pipeline through a temperature sensor, and performing temperature correction on the third magnetic signal data according to the thermal expansion coefficient of the pipeline material.

2. The method for calculating pipe wall thickness based on magnetic signal changes according to claim 1, characterized in that: In S1, the external magnetic field is an integrated arrangement of an electromagnet and a magnetic signal enhancer; The magnetic field sensor in S1 is any one of a Hall sensor, a fluxgate sensor and a magnetoresistive sensor.

3. The method for calculating pipe wall thickness based on magnetic signal changes according to claim 2, characterized in that: The specific content of performing noise processing on the magnetic signal data in S2 is: The magnetic signal data is obtained, and obvious abnormal values ​​in the magnetic signal data are eliminated by a triple standard deviation method, including: Calculate the mean and standard deviation of the magnetic signal data using the formula: Among them, μ is the mean value of magnetic signal data, σ is the standard deviation of magnetic signal data, X i is the i-th magnetic signal data point, and N is the total number of magnetic signal data; When |X i When -μ|>3σ, the magnetic signal data of point i is eliminated, and the data of the eliminated position is supplemented according to the linear interpolation method to obtain the supplemented data. The formula is: Among them, X′ i To complete the data, X i-1 is the previous magnetic signal data point of the i-th magnetic signal data point, X i+1 is the next magnetic signal data point of the i-th magnetic signal data point; The complementary data is added to the removed position to obtain the second magnetic signal data.

4. The method for calculating pipe wall thickness based on magnetic signal changes according to claim 3 is characterized in that: The specific content of normalizing the second magnetic signal data in S3 is: Normalizing the second magnetic signal data using a minimum-maximum normalization method, replacing the second magnetic signal data before noise processing with the normalized second magnetic signal data and outputting the replaced data to obtain third magnetic signal data; The normalization formula is: Among them, X″ i is the normalized second magnetic signal data, i.e., the third magnetic signal data, X min is the minimum value in the second magnetic signal data, X max is the maximum value in the second magnetic signal data.

5. The method for calculating pipe wall thickness based on magnetic signal changes according to claim 4, characterized in that: The specific content of the third magnetic signal data obtained by collecting external environment data and correcting the third magnetic signal data in S4 is: The temperature data of the external environment of the pipeline is collected by the temperature sensor, and the temperature correction of the third magnetic signal data is performed according to the thermal expansion coefficient of the pipeline material. The formula is: X″′=X″ i ×(1+α×(T-T0)); Wherein, X″′ is the corrected third magnetic signal data, X″ i is the third magnetic signal data, α is the linear thermal expansion coefficient of the pipeline material, in °C -1 , T is the temperature of the external environment collected by the temperature sensor, in °C, T0 is the standard temperature, in °C; The characteristic parameters in S5 include the amplitude and phase of the corrected third magnetic signal data.

6. The method for calculating pipe wall thickness based on magnetic signal changes according to claim 5, characterized in that: The specific contents of extracting characteristic parameters from the corrected third magnetic signal data in S5, and establishing a mathematical relationship model between the magnetic signal and the pipe wall thickness using a linear regression method according to the characteristic parameters are as follows: The amplitude and phase of the corrected third magnetic signal data are extracted by using a wavelet transformation method, and a mathematical relationship model between the magnetic signal and the pipe wall thickness is established by using a linear regression method according to the amplitude and phase of the corrected third magnetic signal data, including: The amplitude and phase of the corrected third magnetic signal data are obtained using the formula: A(t)=|W s (a,b)|; Wherein, A(t) is the amplitude of the third magnetic signal data after correction; W s (a, b) Wavelet transform coefficients at scale a and position b; φ(t)6(W s (a,b)) Wherein, φ(t) is the phase of the corrected third magnetic signal data; Among them, a is the scale factor, b is the translation factor, is the complex conjugate of the mother wavelet; A mathematical relationship model between the magnetic signal and the pipe wall thickness is established by using a linear regression method according to the amplitude and phase of the corrected third magnetic signal data. The formula is: d=β0+β1A(t)+β2φ(t)+∈; Among them, d is the pipe wall thickness, β0 is the intercept term of the regression model, β1 is the first regression coefficient related to the amplitude, β2 is the second regression coefficient related to the phase, and ∈ is the random error term.

7. The method for calculating pipe wall thickness based on magnetic signal changes according to claim 6, characterized in that: The magnetic field sensors in S1 are arranged evenly along the axial direction of the pipeline; The magnetic signal data acquisition frequency described in S1 is adjustable; In S7, based on the historical wall thickness data, a moving average method is used to perform wall thickness trend analysis on the pipeline wall thickness to obtain predicted wall thickness data.

8. A pipe wall thickness calculation device based on magnetic signal changes, characterized in that: include: A magnetic signal data acquisition module is used to arrange a uniform external magnetic field around the pipeline and penetrate the pipeline, and use a magnetic field sensor to acquire the magnetic signal data of the pipeline; A noise processing module, used for performing noise processing on the magnetic signal data to obtain second magnetic signal data; A data normalization processing module, used for performing normalization processing on the second magnetic signal data to obtain third magnetic signal data; A magnetic signal data correction module, used for collecting external environment data to correct the third magnetic signal data to obtain corrected third magnetic signal data; The collecting of external environment data to correct the third magnetic signal data to obtain corrected third magnetic signal data includes: collecting temperature data of the external environment of the pipeline through a temperature sensor, and performing temperature correction on the third magnetic signal data according to the thermal expansion coefficient of the pipeline material; A characteristic parameter extraction module, used for extracting characteristic parameters from the corrected third magnetic signal data, and establishing a mathematical relationship model between the magnetic signal and the pipe wall thickness using a linear regression method according to the characteristic parameters; A wall thickness value calculation module is used to bring the characteristic parameters into the mathematical relationship model between the magnetic signal and the pipe wall thickness to calculate the specific wall thickness value of the current pipe; The alarm module is used to perform wall thickness trend analysis on the pipeline wall thickness based on the historical wall thickness data to obtain predicted wall thickness data, set a wall thickness safety threshold according to the predicted wall thickness data, and trigger an alarm system if the specific wall thickness value of the current pipeline is lower than the wall thickness safety threshold.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the pipeline wall thickness calculation method based on magnetic signal changes as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the pipeline wall thickness calculation method based on magnetic signal changes as described in any one of claims 1 to 7 is implemented.

Images