Underwater pipeline wall thickness monitoring processing method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of pipeline monitoring, and discloses an underwater pipeline wall thickness monitoring processing method and system, electronic equipment and a storage medium, and the method comprises the steps: measuring a measurement point through a WAND sensor, and obtaining original wall thickness data; after noise processing is conducted on the original wall thickness data, environment parameters are collected, the original wall thickness data are corrected according to the environment parameters, and corrected wall thickness data are obtained; performing data fusion on the corrected wall thickness data of each measurement point to obtain fused wall thickness data, and marking abnormal points according to the historical wall thickness data, the nominal wall thickness data and the fused wall thickness data; performing wall thickness trend analysis on the wall thickness of the pipeline based on the historical wall thickness data to obtain predicted wall thickness data; and finally, determining that the measurement point is a high-risk area or an area with a damage risk. According to the invention, through noise processing, environmental parameter correction and data fusion, the effectiveness of a measurement result is ensured, a high-risk area and a potential damage risk are identified, and the overall safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline monitoring, and in particular to an underwater pipeline wall thickness monitoring and processing method, system, electronic equipment and storage medium. Background Art

[0002] With the continuous development of global marine engineering, the safety and reliability of underwater pipelines, as important infrastructure for marine resource development, transportation and environmental protection, have become the focus of attention of all parties. These pipelines carry oil, natural gas and other important resources, so their operating status is directly related to the stability of energy supply and environmental safety.

[0003] Traditional pipeline wall thickness monitoring methods mostly use ultrasonic, magnetic or electromagnetic technologies, which have some significant defects in practical applications. First, the measurement accuracy is insufficient. The measurement results of many traditional devices in high water flow rates or complex underwater environments are easily interfered with and cannot truly reflect the actual wall thickness of the pipeline, resulting in misjudgment of the health status of the pipeline. Secondly, noise interference is serious. In underwater environments, mechanical noise and electromagnetic interference are ubiquitous, and traditional methods often find it difficult to effectively filter these interference signals, thus affecting the accuracy and reliability of the data. However, many traditional methods fail to take the above factors into account, resulting in poor accuracy of monitoring results.

[0004] These defects make it difficult for traditional monitoring methods to achieve a comprehensive and accurate assessment of pipeline status, and to predict potential risks and maintenance needs in a timely manner. For example, failure to identify abnormal changes in pipeline wall thickness at an early stage may lead to serious pipeline damage or leakage, resulting in waste of resources and environmental pollution, and may even cause larger-scale safety accidents.

[0005] Therefore, it is necessary to design a more advanced underwater pipeline wall thickness monitoring and processing method or system. Summary of the invention

[0006] In view of this, the present invention proposes an underwater pipeline wall thickness monitoring and processing method, system, electronic equipment and storage medium, aiming to solve the problem of inaccurate underwater pipeline wall thickness measurement in current technology.

[0007] On the one hand, the present invention provides a method for monitoring and processing underwater pipeline wall thickness, comprising:

[0008] Several measuring points are set according to the length of the pipeline. Each measuring point is measured N times by the WAND sensor. The measurement results of the WAND sensor are collected and recorded as the original wall thickness data. The original wall thickness data is in the form of Where j represents the measurement point number, j = 1, 2, ..., n; k represents the kth measurement, k = 1, 2, ..., N;

[0009] After the original wall thickness data is subjected to noise processing, environmental parameters are collected, and the original wall thickness data is corrected according to the environmental parameters to obtain corrected wall thickness data. The corrected wall thickness data is in the form of The environmental parameters include water velocity, temperature and depth;

[0010] The corrected wall thickness data of each measuring point is fused to obtain fused wall thickness data, which is in the form of Calculating the variance of the fused wall thickness data and performing a comparison, and if the variance of the fused wall thickness data is greater than or equal to a set variance threshold, re-measuring;

[0011] If the variance of the fused wall thickness data is less than the set variance threshold, the historical wall thickness data and the nominal wall thickness data of the pipeline are obtained, the difference between the fused wall thickness data and the minimum value of the historical wall thickness data is recorded as the first difference, and the difference between the fused wall thickness data and the nominal wall thickness data is recorded as the second difference. When the first difference is greater than or equal to a preset first deviation threshold or the second difference is greater than or equal to a preset second deviation threshold, the corresponding measurement point is marked as an abnormal point;

[0012] Based on the historical wall thickness data, a wall thickness trend analysis is performed on the pipeline wall thickness to obtain predicted wall thickness data. The predicted wall thickness data is in the form of The pipe wall thickness change rate is obtained according to the historical wall thickness data. When the wall thickness change rate is greater than or equal to a preset wall thickness change safety threshold, the measuring point is determined to be a high-risk area. When the predicted wall thickness data is lower than a preset pipe wall thickness critical value, the measuring point area is determined to have a risk of damage. Wherein t represents time, Indicates the predicted wall thickness data at measuring point j at set time t.

[0013] Furthermore, when the original wall thickness data is subjected to noise processing, it includes:

[0014] The original wall thickness data was obtained, and obvious outliers were eliminated by the triple standard deviation method:

[0015]

[0016]

[0017] Among them, μ j represents the average value of the original wall thickness data at the measuring point j, σ j represents the standard deviation of the original wall thickness data at the measuring point j, and N represents the number of measurements at the measuring point j;

[0018] When satisfied When , the original wall thickness data at the measuring point j is eliminated, and the data at the eliminated position is supplemented according to the linear interpolation method. The supplemented original wall thickness data is calculated by the following formula:

[0019]

[0020] in, Represents the original wall thickness data after completion, for The maximum value in the data, k∈[1,N]; for The maximum value in the data, k∈[1,N]; is the kth original wall thickness data of measuring point j;

[0021] The filtered data is normalized using the minimum-maximum normalization method according to the following formula, and the original wall thickness data after normalization is replaced with the original wall thickness data before noise processing and output; the normalization is calculated by the following formula:

[0022]

[0023] in, Represents the original wall thickness data after normalization.

[0024] Further, when collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain corrected wall thickness data, it includes:

[0025] While collecting the measurement results of the WAND sensor, the ambient temperature, water flow velocity and measurement depth of the WAND sensor setting area are collected, and the corrected wall thickness data is calculated by the following formula:

[0026]

[0027] Among them, α j Indicates the temperature correction coefficient; T j represents the ambient temperature of test point j; T ref Indicates the reference temperature; β j Indicates the water flow rate correction coefficient; V j represents the water flow velocity at test point j; γ j Depth influence coefficient; D j Indicates the depth of test point j, i.e., the measured depth; D ref Indicates the base depth.

[0028] Furthermore, when collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain the corrected wall thickness data, the temperature correction coefficient is also obtained by the following formula:

[0029]

[0030] Among them, α0 is the linear expansion coefficient of the pipeline at the reference depth, m is a constant, 0.1≤m≤0.3, which represents the pressure change value for each unit increase in water depth, and P represents the pressure value at the test point.

[0031] Furthermore, when collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain the corrected wall thickness data, the water flow rate correction coefficient is also obtained by the following formula:

[0032]

[0033] Where max(P) represents the maximum pressure value among the k measured values ​​at the measuring point j, min(P) represents the minimum pressure value among the multiple measured values ​​at the measuring point j, and max(V j ) represents the maximum water velocity among the k measured values ​​at the measuring point j, min(V j ) represents the minimum water velocity among the k measured values ​​at the measuring point j, V ref Indicates the reference water velocity, V j The current water flow velocity at the measuring point j represents the reference water flow velocity.

[0034] Furthermore, when collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain the corrected wall thickness data, the depth correction coefficient is also obtained by the following formula:

[0035] γ j =(1+f·(D j -D ref )·ρ D );

[0036] Where, f is a constant, f is 0.03; D j Indicates the depth of test point j; D ref represents the reference depth; ρ D Indicates that at depth D j The water density at .

[0037] Furthermore, after the corrected wall thickness data of each measuring point is fused, it specifically includes:

[0038] The average value of the corrected wall thickness data obtained by multiple detections of the same measuring point is calculated to obtain the fused wall thickness data, and the variance of the corrected wall thickness data is calculated according to the corrected wall thickness data using the following formula:

[0039]

[0040] The variance of the corrected wall thickness data is compared with a set variance threshold, wherein the variance threshold is determined in the following manner:

[0041] Acquire historical wall thickness data, calculate the variance of the historical wall thickness data at the measuring point, and use the variance threshold as the variance threshold;

[0042] The historical wall thickness data is the corrected wall thickness data collected in the past.

[0043] Furthermore, when a wall thickness trend analysis is performed on the pipeline wall thickness based on the historical wall thickness data, it includes:

[0044] The historical wall thickness data of the same measurement point is obtained, and a time data set of the historical wall thickness data is established based on the historical wall thickness data and the corresponding measurement time. A wall thickness trend function is obtained by linear regression fitting according to the measurement time and the historical wall thickness data, and the measurement time is substituted into the wall thickness trend function to obtain the predicted wall thickness data.

[0045] Further, the first deviation threshold is 10%-20% of the minimum value of the historical wall thickness data;

[0046] The second deviation threshold value ranges from 20% to 30% of the nominal wall thickness data.

[0047] On the other hand, the present invention also proposes a system for implementing the above-mentioned underwater pipeline wall thickness monitoring and processing method, the system comprising:

[0048] WAND sensor, configured to collect raw wall thickness data at set measurement points;

[0049] A data collector configured to collect environmental parameters acquired by sensors in real time;

[0050] A noise processing module is configured to perform noise processing on the original wall thickness data;

[0051] An environmental correction module is configured to correct the original wall thickness data according to the collected environmental parameters and calculate the corrected wall thickness data;

[0052] A data fusion module is configured to fuse the corrected wall thickness data measured multiple times and calculate the average value and variance of the corrected wall thickness data;

[0053] The trend analysis module is configured to perform wall thickness trend analysis based on historical wall thickness data, establish a time data set of historical wall thickness data, and obtain a wall thickness trend function through linear regression fitting.

[0054] The prediction module is configured to substitute the measurement time into the wall thickness trend function to obtain predicted wall thickness data.

[0055] The abnormality judgment module is configured to judge whether the measurement point is an abnormal point according to the difference between the fused wall thickness data and the historical wall thickness data and the nominal wall thickness data.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] By using WAND sensors to perform multiple measurements at multiple measurement points, combined with noise processing and environmental parameter correction, the measurement accuracy of wall thickness data is significantly improved. Compared with traditional methods, the present invention can effectively reduce the error caused by external environmental interference, thereby more accurately reflecting the actual wall thickness of the pipeline. At the same time, the triple standard deviation method is used to eliminate obvious outliers, and the data of the eliminated positions are supplemented by linear interpolation to ensure the reliability of the original data. The introduction of the noise processing module enables the system to effectively filter environmental noise and mechanical noise, further improving the accuracy and stability of the data. In addition, the measurement results are corrected by real-time acquisition of environmental parameters such as water flow rate, temperature and depth. This adaptability enables the monitoring method to maintain high efficiency in complex and changeable underwater environments and ensure the credibility of the measurement data. Secondly, by comparing the variance of the fused wall thickness data, the system can automatically trigger re-measurement when an abnormal situation is detected to ensure that potential problems are discovered in time. Trend analysis combined with historical wall thickness data can predict the risk of pipeline damage in advance, provide a scientific basis for maintenance decisions, and effectively avoid safety accidents. Thirdly, after data fusion, the measurement points are judged to be abnormal by the set first and second deviation thresholds. When the detected wall thickness data exceeds the set range, the system will automatically mark it as an abnormal point to facilitate subsequent maintenance and processing. This automated processing improves the efficiency and accuracy of monitoring. Finally, the use of data fusion technology to integrate multiple measurement results can effectively reduce random errors and improve the representativeness of data. The trend analysis module combined with the linear regression fitting method can predict future wall thickness changes through historical data, making pipeline maintenance more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] 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:

[0059] Figure 1 This is a flow chart of a method for monitoring and processing underwater pipeline wall thickness according to an embodiment of the present invention;

[0060] Figure 2 This is a functional block diagram of an underwater pipeline wall thickness monitoring and processing system according to an embodiment of the present invention;

[0061] Figure 3 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] 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.

[0063] It should be noted that WANDTH means the pipe wall thickness data obtained by measuring with a WAND sensor, and TH means Thickness. In the following embodiments, the reference temperature is 4°C and the reference depth is 3m.

[0064] See also Figure 1 As shown, an embodiment of the present invention provides a method for monitoring and processing the wall thickness of an underwater pipeline, comprising:

[0065] Several measuring points are set according to the length of the pipeline. Each measuring point is measured N times by the WAND sensor. The measurement results of the WAND sensor are collected and recorded as the original wall thickness data. The original wall thickness data is in the form of Where j represents the measurement point number, j = 1, 2, ..., n; k represents the kth measurement, k = 1, 2, ..., N;

[0066] After the original wall thickness data is processed for noise, the environmental parameters are collected and the original wall thickness data is corrected according to the environmental parameters to obtain the corrected wall thickness data. The corrected wall thickness data is in the form of Environmental parameters include water velocity, temperature and depth;

[0067] The corrected wall thickness data of each measuring point is fused to obtain fused wall thickness data in the form of AvgWANDTH j , calculate the variance of the fused wall thickness data and compare them. If the variance of the fused wall thickness data is greater than or equal to the set variance threshold, re-measure;

[0068] If the variance of the fused wall thickness data is less than the set variance threshold, the historical wall thickness data and the nominal wall thickness data of the pipeline are obtained, and the difference between the fused wall thickness data and the minimum value of the historical wall thickness data is recorded as the first difference, and the difference between the fused wall thickness data and the nominal wall thickness data is recorded as the second difference. When the first difference is greater than or equal to the preset first deviation threshold or the second difference is greater than or equal to the preset second deviation threshold, the corresponding measurement point is marked as an abnormal point;

[0069] Based on the historical wall thickness data, the wall thickness trend analysis of the pipeline is carried out to obtain the predicted wall thickness data. The predicted wall thickness data is in the form of The pipeline wall thickness change rate is obtained based on the historical wall thickness data. When the wall thickness change rate is greater than or equal to the preset wall thickness change safety threshold, the measurement point is judged to be a high-risk area; when the predicted wall thickness data is lower than the preset pipeline wall thickness critical value, the area of ​​the measurement point is judged to be at risk of damage.

[0070] Among them, the nominal wall thickness data refers to the standard wall thickness of the pipe. This value is usually defined by the manufacturer during the design and production stages and serves as one of the design parameters or specifications of the pipe.

[0071] It should be noted that when several measuring points are set according to the length of the pipeline, the setting basis is as follows:

[0072] Longer pipelines require more measurement points to ensure comprehensive monitoring of the overall status of the pipeline. In this embodiment, a measurement area is set every 5-10 meters, and the measurement points are located within the measurement area. For pipelines with larger diameters, multiple measurement points need to be set around to capture changes in wall thickness at different locations. In addition, several measurement points can be added to pipelines in high-risk areas and areas with risk of damage.

[0073] In addition, the corrected wall thickness data of each measuring point is fused to obtain fused wall thickness data in the form of AvgWANDTH j, calculate the variance of the fused wall thickness data and compare them. If the variance of the fused wall thickness data is greater than or equal to the set variance threshold, re-measure: First, measure each measurement point multiple times, and correct the original wall thickness data according to environmental parameters (such as temperature, water flow velocity, depth, etc.) to obtain the corrected wall thickness data. The corrected wall thickness data of the same measurement point are fused, usually by calculating the average value. This can effectively reduce the fluctuation caused by single measurement error or noise, thereby improving the accuracy and stability of the data. Variance is a statistic that measures the degree of discreteness of a data set and reflects the fluctuation of the data. Calculating the variance of the fused wall thickness data can help determine the consistency of the measurement results. After calculating the variance of the fused wall thickness data, the data quality of the measurement point can be evaluated. If the variance of the fused wall thickness data is very small, it means that the multiple measurement results are close, indicating that the measurement process is stable; conversely, if the variance is large, it means that the measurement results have large fluctuations. Compare the calculated variance with the set variance threshold. If the variance of the fused wall thickness data is greater than or equal to the set threshold, it means that the data of the measurement point may be disturbed or abnormal. If the variance is greater than the set threshold, it means that the instability of the data may affect the assessment of the pipe wall thickness. At this time, it will be judged that the measurement result may be unreliable. In order to ensure the accuracy of the measurement result, a re-measurement step will be triggered. By measuring again, more reliable data can be obtained, thereby better assessing the actual wall thickness of the pipe and reducing the risk caused by misjudgment.

[0074] It should be pointed out that the wall thickness change safety threshold refers to the maximum allowable value of the pipe wall thickness change within a certain period of time. The pipe wall thickness critical value refers to the minimum safety standard value reached by the pipe wall thickness. Below this value, the structural strength and safety of the pipe may be seriously threatened. Both values ​​are preset values, and the setting range is determined according to the actual situation and safety policy requirements.

[0075] In some embodiments of the present application, when the original wall thickness data is subjected to noise processing, the following steps are included:

[0076] Get the original wall thickness data and remove obvious outliers using the triple standard deviation method:

[0077]

[0078]

[0079] Among them, μ j represents the average value of the original wall thickness data at the measuring point j, σ j represents the standard deviation of the original wall thickness data at the measuring point j, and N represents the number of measurements at the measuring point j;

[0080] When satisfied When , the original wall thickness data at the measuring point j is eliminated, and the data at the eliminated position is supplemented according to the linear interpolation method. The supplemented original wall thickness data is calculated by the following formula:

[0081]

[0082] in, Indicates the original wall thickness data after completion;

[0083] The filtered data is normalized using the minimum-maximum normalization method using the following formula, and the original wall thickness data after normalization is replaced with the original wall thickness data before noise processing and output; the normalization is calculated using the following formula:

[0084]

[0085] in, represents the original wall thickness data after normalization, for The maximum value in the data, k∈[1,N]; for The maximum value in the data, k∈[1,N]; is the kth original wall thickness data of measuring point j.

[0086] It should be noted that noise processing can improve data accuracy, specifically:

[0087] Application of triple standard deviation method: By calculating the mean and standard deviation of the original wall thickness data of each measuring 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.

[0088] If the original wall thickness data at a certain measurement point is [5.1, 5.0, 5.3, 100.0] (assuming 100.0 is an outlier), this method can be used to remove 100.0, ensuring that subsequent calculations are based on reasonable data and avoiding the impact of individual outliers on the entire measurement result.

[0089] Implementation of linear interpolation:

[0090] In linear interpolation, for the removed outlier positions, the previous and next measurements are used to extrapolate and generate the missing wall thickness data. This method helps fill the data gaps and maintain data continuity without introducing additional errors.

[0091] Assume that a value is omitted in a measurement sequence, and the previous and next values ​​are 5.0 and 5.2 respectively. Interpolation can generate 5.1 as the completion value, thus ensuring that the data record of the measurement point is continuous, which is crucial for subsequent trend analysis and monitoring.

[0092] Application of min-max normalization:

[0093] By normalizing the wall thickness 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 data fusion and model building, avoiding algorithm instability or performance degradation caused by differences in data magnitude.

[0094] The original wall thickness data of different measurement points may be between 5mm and 100mm. After normalization, no matter what the original magnitude of the data is, it can be mapped to the interval of [0,1], so that when data fusion is performed, the contribution of each measurement point is balanced, ensuring the fairness and accuracy of the analysis.

[0095] The effect of comprehensive noise processing:

[0096] 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 underwater environments, enhancing the anti-interference ability of the monitoring equipment.

[0097] In an environment with turbulent water flow, traditional methods may cause unstable measurement results due to water flow disturbances, but after noise processing, they can provide more reliable data under the same conditions, reducing the risk of false alarms and missed reports.

[0098] Risk analysis supported by accurate data:

[0099] 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.

[0100] 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.

[0101] In some embodiments of the present application, when collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain the corrected wall thickness data, the process includes:

[0102] While collecting the measurement results of the WAND sensor, the ambient temperature, water flow velocity and measurement depth of the WAND sensor setting area are collected, and the corrected wall thickness data is calculated using the following formula:

[0103]

[0104] Among them, α j Indicates the temperature correction coefficient; T j represents the ambient temperature of test point j; T ref Indicates the reference temperature; β j Indicates the water flow rate correction coefficient; V j represents the water flow velocity at test point j; γ j Depth influence coefficient; D j Indicates the depth of test point j, i.e., the measured depth; D ref Indicates the base depth.

[0105] It should be noted that by introducing factors such as ambient temperature, water flow velocity and measurement depth, the original wall thickness data can be corrected as necessary, so that the corrected wall thickness data is closer to the actual situation. This is because environmental factors are very important in the operation of underwater pipelines and directly affect the wall thickness performance of pipelines. Temperature increase may cause thermal expansion of pipeline materials, and water flow velocity may affect the data acquisition accuracy during the measurement process. Through corresponding corrections, the actual wall thickness of the pipeline can be more accurately reflected. Through a unified correction formula, the data at different measurement points can be compared under the same standard. For data obtained under different environmental conditions, the effects caused by environmental differences can be eliminated after correction, thereby enhancing the comparability between data. Measurements conducted in environments with faster and slower water flow velocities can obtain directly comparable results after correction, thereby effectively evaluating the health status of each measurement point. The temperature, water flow velocity and depth of the underwater environment are often variable. By real-time monitoring of these environmental parameters and data correction, different underwater conditions can be adapted. 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 monitoring data and improve the safety monitoring capabilities of pipelines. The corrected data provides a reliable basis for subsequent trend analysis and risk assessment, which helps to effectively predict and intervene before potential problems occur. For example, if the corrected data shows that the wall thickness of a certain measuring point is decreasing at an accelerated rate, 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 can 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 these data for longer-term management and maintenance.

[0106] By collecting and correcting environmental parameters, the accuracy, stability and comparability of underwater pipeline wall thickness monitoring can be effectively improved, risk assessment capabilities can be enhanced, and reliability of operation in complex underwater 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.

[0107] In some embodiments of the present application, when collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain the corrected wall thickness data, the temperature correction coefficient is also obtained by the following formula:

[0108]

[0109] Wherein, α0 is the linear expansion coefficient of the pipeline at the reference depth, the linear expansion coefficient is the elongation per degree of increase, m is a constant, 0.1≤m≤0.3, u represents the pressure change value for each unit increase in water depth, and P represents the pressure value at the test point.

[0110] in, represents the adjustment of the linear expansion coefficient α0, It represents the adjustment of linear expansion coefficient due to depth; log(u·P) represents the adjustment of linear expansion coefficient due to pressure. Specifically, u represents the pressure change when the water depth increases by unit, and the pressure change is related to ρ, that is, the density of water. u represents the adjustment of linear expansion coefficient due to water density, and P represents the adjustment of elongation due to pressure.

[0111] It should be noted that the introduction of the temperature correction coefficient helps to correct the deviation of the pipeline wall thickness data caused by temperature changes, thereby improving the accuracy of the measurement results and making the corrected wall thickness 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 changing underwater environment. Accurate corrected wall thickness 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 the temperature correction coefficient and its related parameter calculations makes monitoring more intelligent, can respond to environmental changes in real time, and achieve more accurate monitoring and management.

[0112] In some embodiments of the present application, when collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain the corrected wall thickness data, the water flow rate correction coefficient is also obtained by the following formula:

[0113]

[0114] Where max(P) represents the maximum pressure value among the k measured values ​​at the measuring point j, min(P) represents the minimum pressure value among the multiple measured values ​​at the measuring point j, and max(V j ) represents the maximum water velocity among the k measured values ​​at the measuring point j, min(V j ) represents the minimum water velocity among the k measured values ​​at the measuring point j, V ref Indicates the reference water velocity, V j Represents the current water flow velocity at measuring point j.

[0115] It should be noted that the role of the water flow rate correction coefficient in correcting the original wall thickness data is:

[0116] Improve measurement accuracy: The water flow rate correction factor takes into account the effect of flow rate on wall thickness measurement, 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 rate.

[0117] 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 coefficient, the interference of flow rate on the original wall thickness data can be effectively filtered out, thereby significantly reducing the impact of environmental noise on the monitoring results, making the data more stable and reliable.

[0118] Enhanced adaptability: The application of water velocity correction coefficient 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.

[0119] 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.

[0120] 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.

[0121] In some embodiments of the present application, when collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain the corrected wall thickness data, the depth correction coefficient is also obtained by the following formula:

[0122] γ j =(1+f·(D j -D ref )·ρ D );

[0123] Where, f is a constant, f is 0.03; D j Indicates the depth of test point j; D ref represents the reference depth; ρ D Indicates that at depth D j The water density at .

[0124] It should be noted that the introduction of the depth correction coefficient is to ensure that the original wall thickness data can be effectively corrected at different measurement depths and eliminate the data deviation caused by depth changes. f is a constant (value 0.03): This constant is used to quantify the impact of depth changes on wall thickness data, and 0.03 represents the magnitude of the impact of each unit depth change on data correction. Dj is the depth of test point j: The depth of each measurement point is one of the key factors affecting the wall thickness data. The different water pressure and density at different depths may cause errors in the measurement data. Therefore, the actual depth of the measurement point is included in the correction formula. Dref is the reference depth: The reference depth is used as a comparison standard to compare the difference with the depth of each measurement point to ensure that the correction is based on the relative change in depth. ρ is the water density at depth: The water density changes at different depths. As the depth increases, the compressibility of water causes the density to increase. By introducing the water density parameter, the wall thickness data deviation caused by depth changes can be further accurately corrected.

[0125] By correcting the depth, the impact of different measurement depths on the wall thickness data can be effectively reduced, ensuring that the data of each measurement point are comparable when the depth is inconsistent. Since the depth correction coefficient 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. The change in depth has a cumulative effect on wall thickness measurement. The depth correction coefficient can minimize this cumulative error and 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.

[0126] In some embodiments of the present application, after the corrected wall thickness data of each measuring point is fused, the following steps are specifically performed:

[0127] The average value of the corrected wall thickness data obtained from multiple tests at the same measuring point is calculated to obtain the fused wall thickness data. The variance of the corrected wall thickness data is calculated using the following formula:

[0128]

[0129] The variance of the corrected wall thickness data is compared with the set variance threshold, where the variance threshold is determined by:

[0130] Obtain historical wall thickness data, calculate the variance of the historical wall thickness data at the measuring point, and use it as the variance threshold;

[0131] The historical wall thickness data is the corrected wall thickness data collected in the past.

[0132] It should be noted that through multiple tests and variance calculations, the influence of individual abnormal measurement data can be eliminated, and the stability and accuracy of the corrected wall thickness data can be improved. The variance threshold is dynamically adjusted according to historical data to ensure that different measurement points are treated differently according to their actual historical fluctuations, avoiding errors that may be caused by fixed thresholds. When the variance exceeds the set threshold, timely re-measurement can effectively prevent interference from abnormal data and ensure the quality of the final data obtained. Through reasonable control of the variance, the data accuracy of each measurement point is ensured to meet the requirements, and the stability and consistency of the measurement process are improved.

[0133] In some embodiments of the present application, when performing a wall thickness trend analysis on the pipe wall thickness based on historical wall thickness data, the process includes:

[0134] The historical wall thickness data of the same measuring point is obtained, and a time data set of the historical wall thickness data is established based on the historical wall thickness data and the corresponding measurement time. The wall thickness trend function is obtained by linear regression fitting according to the measurement time and the historical wall thickness data, and the measurement time is substituted into the wall thickness trend function to obtain the predicted wall thickness data.

[0135] It should be noted that when the wall thickness trend analysis of the pipeline wall thickness is performed based on the historical wall thickness data, the specific analysis can be:

[0136] Get historical wall thickness data:

[0137] Collect and store the historical wall thickness data of each measuring point of the pipeline. The data should include the measurement time, measurement point number, and the corrected wall thickness value obtained by measurement. The corrected wall thickness value obtained by measurement can be the value corrected and fused by multiple measurement values ​​of the measurement point, that is, the fused wall thickness data value of the measurement point, or the corrected wall thickness value corrected by a single measurement value of the measurement point. Data format: (time, measurement point number, corrected wall thickness value).

[0138] Construct the time dataset:

[0139] For each measurement point, the corresponding historical wall thickness data and measurement time are extracted to form a time series data set. For measurement point j, the time data set is: {(t1,T j (t1),(t2,T j (t2),…,(t n ,T j (t n )}; where, where, t n Indicates the time of the nth measurement, T j (t n ) represents the measurement point j at time t n Corrected wall thickness data.

[0140] Perform a linear regression fit:

[0141] Using the linear regression method, the measurement time t of the time data set is n As an independent variable, the modified wall thickness value T j (t n ) is used as the dependent variable, and the wall thickness trend function of the measuring point j is obtained by fitting.

[0142] Linear regression formula: T j (t) = a j ·t+b j ;

[0143] Among them, T j (t n ) is the trend function of wall thickness changing with time, a j and b j are the regression coefficients obtained by linear regression fitting, which represent the speed of wall thickness change and the initial wall thickness value respectively.

[0144] Generate a wall thickness trend function:

[0145] Calculate the corresponding wall thickness trend function T for each measuring point j (t), which is used to predict the wall thickness change at future time.

[0146] Predicted wall thickness data:

[0147] Select a future time and substitute it into the wall thickness trend function T j (t), calculate the predicted wall thickness data: T j (t future )=a j ·t future +b j , where T j (t future ) is the measurement point j at the future time t future The predicted wall thickness value at .

[0148] Result output and analysis:

[0149] Output the wall thickness trend function and predicted wall thickness data of each measuring point to analyze the future wall thickness change trend of the pipeline.

[0150] Based on the predicted wall thickness data, it is possible to assess whether there is a risk of the wall thickness of the pipeline being thinned to a critical value in the future, so that preventive measures can be taken in advance.

[0151] It is understandable that through the analysis of historical data and trend prediction, the change of pipeline wall thickness can be predicted in advance to avoid accidental leakage and damage. According to the trend function, the maintenance cycle of the pipeline can be dynamically adjusted to reduce unnecessary inspections and repairs and improve maintenance efficiency. Through data-driven wall thickness trend analysis, a scientific basis can be provided for pipeline management and maintenance decisions to reduce the risk of failure. The linear regression method is used for trend analysis, which is simple to calculate and suitable for real-time data processing and trend analysis.

[0152] In some embodiments of the present application, the first deviation threshold is 10%-20% of the minimum value of the historical wall thickness data;

[0153] The second deviation threshold value ranges from 20% to 30% of the nominal wall thickness data.

[0154] See also Figure 2 As shown, the embodiment of the present invention also provides an underwater pipeline wall thickness monitoring and processing system, including:

[0155] WAND sensor, configured to collect raw wall thickness data at set measurement points;

[0156] A data collector configured to collect environmental parameters acquired by sensors in real time;

[0157] A noise processing module is configured to perform noise processing on the original wall thickness data;

[0158] An environmental correction module is configured to correct the original wall thickness data according to the collected environmental parameters and calculate the corrected wall thickness data;

[0159] A data fusion module is configured to fuse the corrected wall thickness data measured multiple times and calculate the average value and variance of the fused wall thickness data;

[0160] The trend analysis module is configured to perform wall thickness trend analysis based on historical wall thickness data, establish a time data set of historical wall thickness data, and obtain a wall thickness trend function through linear regression fitting.

[0161] The prediction module is configured to substitute the measurement time into the wall thickness trend function to obtain predicted wall thickness data.

[0162] The abnormality judgment module is configured to judge whether the measurement point is an abnormal point according to the difference between the fused wall thickness data and the historical wall thickness data and the nominal wall thickness data.

[0163] It should be noted that for the collection of environmental parameters:

[0164] Choose a thermocouple or RTD sensor to measure the temperature of the water body, choose an ultrasonic flow sensor or an electromagnetic flow sensor to accurately measure the flow rate in an underwater environment, and choose a pressure sensor or an ultrasonic depth sounder to measure the water depth in an underwater environment.

[0165] Install the sensor near the pipe or outside the pipe to ensure that the sensor can contact the environmental medium. Connect the data output interface of the temperature sensor, water velocity sensor and depth sensor to the data logger. Use a microcontroller or data logger to collect and store environmental parameters in real time. Transmit the collected environmental parameters wirelessly (such as Wi-Fi, Bluetooth) or wired (such as Ethernet, serial port).

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0167] 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, the memory 420, the input device 430 and the 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.

[0168] 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 monitoring and processing the wall thickness of an underwater pipeline 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.

[0169] 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.

[0170] 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.

[0171] The embodiment of the present invention further provides a storage medium containing computer executable instructions, wherein the computer executable instructions are used to execute a method for monitoring and processing the wall thickness of an underwater pipeline when executed by a computer processor, the method comprising: setting a plurality of measurement points according to the length of the pipeline, performing N measurements at each measurement point by a WAND sensor, collecting the measurement results of the WAND sensor and recording them as original wall thickness data, wherein the original wall thickness data is in the form of Wherein j represents the measurement point number, j=1, 2, ..., n; k represents the kth measurement, k=1, 2, ..., N; after the original wall thickness data is subjected to noise processing, environmental parameters are collected, and the original wall thickness data is corrected according to the environmental parameters to obtain corrected wall thickness data, and the corrected wall thickness data is in the form of The environmental parameters include water velocity, temperature and depth; the corrected wall thickness data of each measuring point is fused to obtain fused wall thickness data, and the fused wall thickness data is in the form of AvgWANDTH j , calculate the variance of the corrected wall thickness data of each measuring point and compare them. If the variance of the corrected wall thickness data is greater than or equal to the set variance threshold, re-measure; if the variance of the corrected wall thickness data is less than the set variance threshold, obtain the historical wall thickness data and the nominal wall thickness data of the pipeline, record the difference between the fused wall thickness data and the minimum value in the historical wall thickness data as the first difference, and record the difference between the fused wall thickness data and the nominal wall thickness data as the second difference. When the first difference is greater than or equal to the preset first deviation threshold or the second difference is greater than or equal to the preset second deviation threshold, mark the corresponding measuring point as an abnormal point; perform wall thickness trend analysis on the pipeline wall thickness based on the historical wall thickness data to obtain predicted wall thickness data. The predicted wall thickness data is in the form of The pipe wall thickness change rate is obtained according to the historical wall thickness data. When the wall thickness change rate is greater than or equal to a preset wall thickness change safety threshold, the measuring point is determined to be a high-risk area. When the predicted wall thickness data is lower than a preset pipe wall thickness critical value, the measuring point area is determined to have a risk of damage. Wherein t represents time, Indicates the predicted wall thickness data at measuring point j at set time t.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 monitoring and processing the wall thickness of an underwater pipeline, characterized in that: include: Several measuring points are set according to the length of the pipeline. Each measuring point is measured N times by the WAND sensor. The measurement results of the WAND sensor are collected and recorded as the original wall thickness data. The original wall thickness data is in the form of Where j represents the measurement point number, j = 1, 2, ..., n; k represents the kth measurement, k = 1, 2, ..., N; After the original wall thickness data is subjected to noise processing, environmental parameters are collected, and the original wall thickness data is corrected according to the environmental parameters to obtain corrected wall thickness data. The corrected wall thickness data is in the form of The environmental parameters include water velocity, temperature and depth; The corrected wall thickness data of each measuring point is fused to obtain fused wall thickness data, which is in the form of AvgWANDTH j , calculate the variance of the corrected wall thickness data of each measuring point and compare them. If the variance of the corrected wall thickness data is greater than or equal to the set variance threshold, re-measure; If the variance of the corrected wall thickness data is less than the set variance threshold, the historical wall thickness data and the nominal wall thickness data of the pipeline are obtained, the difference between the fused wall thickness data and the minimum value of the historical wall thickness data is recorded as the first difference, and the difference between the fused wall thickness data and the nominal wall thickness data is recorded as the second difference. When the first difference is greater than or equal to a preset first deviation threshold or the second difference is greater than or equal to a preset second deviation threshold, the corresponding measurement point is marked as an abnormal point; Based on the historical wall thickness data, a wall thickness trend analysis is performed on the pipeline wall thickness to obtain predicted wall thickness data. The predicted wall thickness data is in the form of The pipe wall thickness change rate is obtained according to the historical wall thickness data. When the wall thickness change rate is greater than or equal to a preset wall thickness change safety threshold, the measuring point is determined to be a high-risk area. When the predicted wall thickness data is lower than a preset pipe wall thickness critical value, the measuring point area is determined to have a risk of damage. Wherein t represents time, Indicates the predicted wall thickness data at measuring point j at set time t.

2. The underwater pipeline wall thickness monitoring and processing method according to claim 1 is characterized in that: When the original wall thickness data is subjected to noise processing, it includes: The original wall thickness data was obtained, and obvious outliers were eliminated by the triple standard deviation method: Among them, μ j represents the average value of the original wall thickness data at the measuring point j, σ j represents the standard deviation of the original wall thickness data at the measuring point j, and N represents the number of measurements at the measuring point j; When satisfied When the original wall thickness data at the measuring point j is removed, the data at the removed position is supplemented according to the linear interpolation method, and the supplemented original wall thickness data is calculated by the following formula: in, Indicates the original wall thickness data after completion; The filtered data is normalized using the minimum-maximum normalization method according to the following formula, and the original wall thickness data after normalization is replaced with the original wall thickness data before noise processing and output; the normalization is calculated by the following formula: in, represents the original wall thickness data after normalization, for The maximum value in the data, k∈[1,N]; for The maximum value in the data, k∈[1,N]; is the kth original wall thickness data of measuring point j.

3. The underwater pipeline wall thickness monitoring and processing method according to claim 2 is characterized in that: When collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain corrected wall thickness data, the method includes: While collecting the measurement results of the WAND sensor, the ambient temperature, water flow velocity and measurement depth of the WAND sensor setting area are collected, and the corrected wall thickness data is calculated by the following formula: Among them, α j Indicates the temperature correction coefficient; T j represents the ambient temperature of test point j; T ref Indicates the reference temperature; β j Indicates the water flow rate correction coefficient; V j represents the water flow velocity at test point j; γ j Depth influence coefficient; D j Indicates the depth of test point j, i.e., the measured depth; D ref Indicates the base depth.

4. The underwater pipeline wall thickness monitoring and processing method according to claim 3 is characterized in that: When collecting environmental parameters and correcting the original wall thickness data according to the environmental parameters to obtain the corrected wall thickness data, the temperature correction coefficient is also obtained by the following formula: Wherein, α0 is the linear expansion coefficient of the pipeline at the reference depth, m is a constant, 0.1≤m≤0.3, u represents the pressure change value for each unit increase in water depth, and P represents the pressure value at the test point.

5. The underwater pipeline wall thickness monitoring and processing method according to claim 4 is characterized in that: When collecting environmental parameters, the original wall thickness data is corrected according to the environmental parameters to obtain the corrected wall thickness data, and the water flow rate correction coefficient is obtained by the following formula: Where max(P) represents the maximum pressure value among the k measured values ​​at the measuring point j, min(P) represents the minimum pressure value among the multiple measured values ​​at the measuring point j, and max(V j ) represents the maximum water velocity among the k measured values ​​at the measuring point j, min(V j ) represents the minimum water velocity among the k measured values ​​at the measuring point j, V ref Indicates the reference water velocity, V j The current water flow velocity at the measuring point j represents the reference water flow velocity.

6. The underwater pipeline wall thickness monitoring and processing method according to claim 5 is characterized in that: When collecting environmental parameters, the original wall thickness data is corrected according to the environmental parameters to obtain the corrected wall thickness data, and the depth correction coefficient is also obtained by the following formula: c j =(1+f·(D j -D ref )·r D ); Where, f is a constant, f is 0.03; D j Indicates the depth of test point j; D ref represents the reference depth; ρ D Indicates that at depth D j The water density at .

7. The underwater pipeline wall thickness monitoring and processing method according to claim 6 is characterized in that: After the corrected wall thickness data of each measuring point is fused, the following steps are specifically performed: The average value of the corrected wall thickness data obtained by multiple detections of the same measuring point is calculated to obtain the fused wall thickness data, and the variance of the corrected wall thickness data is calculated according to the corrected wall thickness data using the following formula: The variance of the corrected wall thickness data is compared with a set variance threshold, wherein the variance threshold is determined in the following manner: Acquire historical wall thickness data, calculate the variance of the historical wall thickness data at the measuring point, and use the variance threshold as the variance threshold; The historical wall thickness data is the corrected wall thickness data collected in the past.

8. The underwater pipeline wall thickness monitoring and processing method according to claim 7 is characterized in that: When the wall thickness trend analysis of the pipeline wall thickness is performed based on the historical wall thickness data, it includes: The historical wall thickness data of the same measurement point is obtained, and a time data set of the historical wall thickness data is established based on the historical wall thickness data and the corresponding measurement time. A wall thickness trend function is obtained by linear regression fitting according to the measurement time and the historical wall thickness data, and the measurement time is substituted into the wall thickness trend function to obtain predicted wall thickness data.

9. The underwater pipeline wall thickness monitoring and processing method according to claim 8, characterized in that: The first deviation threshold is 10%-20% of the minimum value of the historical wall thickness data; The second deviation threshold value ranges from 20% to 30% of the nominal wall thickness data.

10. An underwater pipeline wall thickness monitoring and processing system, used to implement the underwater pipeline wall thickness monitoring and processing method according to any one of claims 1 to 9, characterized in that: The system comprises: WAND sensor, configured to collect raw wall thickness data at set measurement points; A data collector configured to collect environmental parameters acquired by sensors in real time; A noise processing module is configured to perform noise processing on the original wall thickness data; An environmental correction module is configured to correct the original wall thickness data according to the collected environmental parameters and calculate the corrected wall thickness data; A data fusion module is configured to fuse the corrected wall thickness data measured multiple times and calculate the average value and variance of the corrected wall thickness data; A trend analysis module is configured to perform wall thickness trend analysis based on historical wall thickness data, establish a time data set of historical wall thickness data, and obtain a wall thickness trend function through linear regression fitting; A prediction module is configured to substitute the measurement time into the wall thickness trend function to obtain predicted wall thickness data; The abnormality judgment module is configured to judge whether the measurement point is an abnormal point according to the difference between the fused wall thickness data and the historical wall thickness data and the nominal wall thickness data.

11. 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 underwater pipeline wall thickness monitoring and processing method as described in any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the underwater pipeline wall thickness monitoring and processing method as described in any one of claims 1 to 9 is implemented.

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