An underwater pipeline wall thickness monitoring processing method, system, electronic device and storage medium

Abstract

The application relates to the technical field of pipeline monitoring, and discloses an underwater pipeline wall thickness monitoring processing method and system, an electronic device and a storage medium, the method comprising the following steps: measuring a measurement point through a WAND sensor to obtain original wall thickness data; after noise processing is performed on the original wall thickness data, environmental parameters are collected, the original wall thickness data is corrected according to the environmental parameters, and corrected wall thickness data is obtained; the corrected wall thickness data of each measurement point is subjected to data fusion to obtain fused wall thickness data, and an abnormal point is marked according to historical wall thickness data, nominal wall thickness data and the fused wall thickness data; wall thickness trend analysis is performed on the pipeline wall thickness based on the historical wall thickness data to obtain predicted wall thickness data; finally, it is determined whether the measurement point is a high-risk area and an area with a damage risk. Through noise processing, environmental parameter correction and data fusion, the effectiveness of the 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 application relates to the technical field of pipeline monitoring, in particular to a method and system for monitoring and processing the wall thickness of an underwater pipeline, an electronic device and a storage medium. BACKGROUND

[0002] With the continuous development of global marine engineering, underwater pipelines, as important infrastructure for the development, transportation and environmental protection of marine resources, have become the focus of attention in terms of safety and reliability. These pipelines carry oil, natural gas and other important resources, so their operating status directly affects 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 drawbacks in practical applications. First, the measurement accuracy is insufficient. Many traditional devices are easily disturbed in high water flow or complex underwater environments, and cannot truly reflect the actual wall thickness of the pipeline, leading to misjudgment of the health status of the pipeline. Second, noise interference is serious. In underwater environments, mechanical noise and electromagnetic interference are common, and traditional methods often have difficulty effectively filtering these interference signals, affecting the accuracy and reliability of the data. However, many traditional methods do not take into account the above factors, resulting in poor accuracy of the monitoring results.

[0004] These defects make it difficult for traditional monitoring methods to achieve comprehensive and accurate assessment of the pipeline status, and cannot timely predict potential risks and maintenance needs. For example, failure to identify abnormal changes in pipeline wall thickness at an early stage can lead to serious pipeline damage or leakage events, causing resource waste and environmental pollution, and even triggering larger-scale safety accidents.

[0005] Therefore, it is necessary to design a more advanced underwater pipeline wall thickness monitoring method or system. SUMMARY

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

[0007] On the one hand, the present application proposes a method for monitoring and processing the wall thickness of an underwater pipeline, comprising:

[0008] A number of measurement points are set according to the length of the pipeline, and each measurement point is measured N times by a WAND sensor. The measurement results of the WAND sensor are collected and recorded as original wall thickness data, and 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 noise processing the original wall thickness data, environmental parameters are collected, the original wall thickness data is corrected according to the environmental parameters to obtain corrected wall thickness data, the form of the corrected wall thickness data is The environmental parameters include water flow rate, temperature and depth;

[0010] The corrected wall thickness data of each measurement point is data fused to obtain fused wall thickness data, the form of the fused wall thickness data is The variance of the fused wall thickness data is calculated and compared, if the variance of the fused wall thickness data is greater than or equal to a set variance threshold, re-measurement is performed;

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

[0012] Based on the historical wall thickness data, wall thickness trend analysis of the pipeline wall thickness is performed to obtain predicted wall thickness data, the form of the predicted wall thickness data is According to the historical wall thickness data, the pipeline wall thickness change rate is obtained, when the wall thickness change rate is greater than or equal to a pre-set wall thickness change safety threshold, the measurement point is determined as a high-risk area; when the predicted wall thickness data is lower than a pre-set pipeline wall thickness critical value, it is judged that the area of the measurement point has a damage risk; wherein t represents time, represents the predicted wall thickness data of the jth measurement point at a set time t.

[0013] Further, when the original wall thickness data is noise processed, it includes:

[0014] The original wall thickness data is obtained, and obvious outliers are removed by a three-sigma method:

[0015]

[0016]

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

[0018] When the following condition is met At this time, the original wall thickness data at the measurement point j is removed, and the data at the removed position is completed according to a linear interpolation method. The completed original wall thickness data is calculated by the following formula:

[0019]

[0020] wherein, represents the completed original wall thickness data, and is the maximum value in the data, k [1, N] ; is the maximum value in the data, k [1, N] ; is the kth original wall thickness data of the measurement point j; The filtered data is normalized by using a minimum-maximum normalization method by the following formula. The normalized original wall thickness data is replaced with the original wall thickness data before noise processing and output. The normalized processing is calculated by the following formula:

[0021]

[0022]

[0023] wherein, represents the normalized original wall thickness data.

[0024] Further, when the environmental parameters are collected, the original wall thickness data is corrected according to the environmental parameters to obtain corrected wall thickness data, comprising:

[0025] The environmental temperature, water flow velocity and measurement depth of the WAND sensor setting area are collected at the same time as the measurement result of the WAND sensor, and the corrected wall thickness data is calculated by the following formula:

[0026]

[0027] wherein, a j represents a temperature correction coefficient; T j represents the environmental temperature of the test point j; T ref represents a reference temperature; b j represents a water flow velocity correction coefficient; V j represents the water flow velocity of the test point j; g j represents a depth influence coefficient; D j represents the depth of the test point j, that is, the measurement depth; D ref represents a reference depth.

[0028] Further, when the environmental parameters are collected, the original wall thickness data is corrected according to the environmental parameters to obtain corrected wall thickness data, and the temperature correction coefficient is obtained by the following formula:​​

[0029]

[0030] wherein a0 is the linear expansion coefficient of the pipeline at the reference depth, m is a constant, 0.1≤m≤0.3, representing the pressure change value per unit increase in water depth, and P represents the pressure value at the test point.

[0031] Further, when the environmental parameters are collected, the original wall thickness data is corrected according to the environmental parameters to obtain corrected wall thickness data, the water flow velocity correction coefficient is obtained by the following formula:

[0032]

[0033] wherein max(P) represents the maximum pressure value among the k measurement values at the measurement point j, min(P) represents the minimum pressure value among the multiple measurement values at the measurement point j, max(V j ) represents the maximum water flow velocity among the k measurement values at the measurement point j, min(V j ) represents the minimum water flow velocity among the k measurement values at the measurement point j, V ref represents the reference water flow velocity, and V j represents the current water flow velocity at the measurement point j.

[0034] Further, when the environmental parameters are collected, the original wall thickness data is corrected according to the environmental parameters to obtain corrected wall thickness data, the depth correction coefficient is obtained by the following formula:

[0035] γ j =(1+f·(D j -D ref )·ρ D );

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

[0037] Further, after the corrected wall thickness data of each measurement point is data fused, the method specifically comprises:

[0038] calculating the average value of the corrected wall thickness data obtained by multiple detections of the same measurement point to obtain fused wall thickness data, and calculating the variance of the corrected wall thickness data according to the corrected wall thickness data by 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 by:

[0041] The historical wall thickness data is obtained, the variance of the historical wall thickness data at the measurement point is calculated, and the variance threshold is set as the variance of the historical wall thickness data;

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

[0043] Further, when the wall thickness trend analysis is performed on the pipeline wall thickness based on the historical wall thickness data, comprising:

[0044] The historical wall thickness data of the same measurement point is obtained, 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 ranges from 20% to 30% of the nominal wall thickness data.

[0047] In another aspect, the present application also provides a system for implementing the above-mentioned underwater pipeline wall thickness monitoring processing method, the system comprising:

[0048] A WAND sensor configured to collect the original wall thickness data at a set measurement point;

[0049] A data collector configured to collect the environmental parameters obtained by the sensor in real time;

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

[0051] An environmental correction module 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 configured to perform data fusion on the corrected wall thickness data measured multiple times and calculate the average value and variance of the corrected wall thickness data;

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

[0054] A prediction module configured to substitute the measurement time into the wall thickness trend function to obtain the 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 fusion wall thickness data and the historical wall thickness data and the nominal wall thickness data.

[0056] Compared with the prior art, the present application has the beneficial effects that:

[0057] By using the WAND sensor to perform multiple measurements at multiple measurement points and combining noise processing and environmental parameter correction, the measurement accuracy of the wall thickness data is significantly improved. Compared with the traditional method, the present application 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 three-sigma method is used to eliminate obvious outliers, and the linear interpolation method is used to complete the data at the eliminated positions, ensuring 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, by real-time acquisition of environmental parameters such as water flow rate, temperature and depth, the measurement results are corrected. This adaptability enables the monitoring method to remain efficient in complex and variable underwater environments, ensuring the credibility of the measurement data. Secondly, by comparing the variances of the fusion wall thickness data, the system can automatically trigger re-measurement when an abnormal situation is detected, ensuring timely detection of potential problems. Trend analysis combined with historical wall thickness data can predict the risk of pipeline damage in advance, providing a scientific basis for maintenance decisions, thereby effectively avoiding safety accidents. Thirdly, after data fusion, the first and second deviation thresholds are set to judge the abnormality of the measurement points. When the detected wall thickness data exceeds the set range, the system will automatically mark it as an abnormal point, facilitating subsequent maintenance and processing. This automated processing improves the efficiency and accuracy of monitoring. Finally, the data fusion technology is used to integrate multiple measurement results, which can effectively reduce random errors and improve the representativeness of the data. The trend analysis module combines the linear regression fitting method to predict future wall thickness changes based on historical data, making pipeline maintenance more scientific and reasonable. BRIEF DESCRIPTION OF DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments with reference made to the accompanying drawings. The drawings are for purposes of illustration only and are not intended to be limiting in any respect. Moreover, the use of the same reference symbols in different drawings indicates similar or identical items.

[0059] Figure 1 The flowchart of the underwater pipeline wall thickness monitoring processing method of the embodiment of the present application;

[0060] Figure 2 The functional block diagram of the underwater pipeline wall thickness monitoring processing system of the embodiment of the present application;

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

[0062] Exemplary embodiments of the present disclosure will be described in detail with reference to the drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood, and the scope of the present disclosure can be accurately conveyed to those skilled in the art. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0063] It should be noted that WANDTH represents the pipe wall thickness data obtained by WAND sensor measurement, and TH represents Thickness. In the following embodiments, the reference temperature is 4℃, and the reference depth is 3m.

[0064] Referring to Figure 1 , the embodiment of the present application provides a method for monitoring the wall thickness of an underwater pipeline, comprising:

[0065] A plurality of measurement points are set according to the length of the pipeline, and each measurement point is measured N times by a WAND sensor. The measurement results of the WAND sensor are collected and recorded as original wall thickness data, and the original wall thickness data has 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 noise processing of the original wall thickness data, the 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 has the form of The environmental parameters include water flow rate, temperature and depth;

[0067] The corrected wall thickness data of each measurement point is data fused to obtain fused wall thickness data, and the fused wall thickness data has the form of AvgWANDTH j The variance of the fused wall thickness data is calculated and compared, and if the variance of the fused wall thickness data is greater than or equal to the set variance threshold, the measurement is re-measured.

[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, the difference between the fused wall thickness data and the minimum value in 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 pre-set first deviation threshold or the second difference is greater than or equal to the pre-set second deviation threshold, the corresponding measurement point is marked as an abnormal point.

[0069] Based on the historical wall thickness data, wall thickness trend analysis is performed on the pipeline wall thickness to obtain predicted wall thickness data. The form of the predicted wall thickness data is According to the historical wall thickness data, the wall thickness change rate of the pipeline is obtained. When the wall thickness change rate is greater than or equal to the pre-set wall thickness change safety threshold, it is determined that the measurement point is in a high-risk area. When the predicted wall thickness data is lower than the pre-set pipeline wall thickness critical value, it is determined that the area of the measurement point has a risk of damage.

[0070] The nominal wall thickness data refers to the standard wall thickness of the pipeline, which is usually defined by the manufacturer during the design and production stage and serves as one of the design parameters or specifications of the pipeline.

[0071] It should be noted that when a plurality of measurement points are set according to the length of the pipeline, the settings are as follows:

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

[0073] In addition, the corrected wall thickness data of each measurement point is fused to obtain fused wall thickness data. The form of the fused wall thickness data is AvgWANDTH j, the variance of the fused wall thickness data is calculated and compared, and if the variance of the fused wall thickness data is greater than or equal to the set variance threshold, re-measurement is performed: first, multiple measurements are performed at each measurement point, and the original wall thickness data is corrected according to environmental parameters (such as temperature, water flow speed, depth, etc.) to obtain corrected wall thickness data. The corrected wall thickness data of the same measurement point is fused, usually by calculating the average value. In this way, the fluctuations caused by single measurement error or noise can be effectively reduced, thereby improving the accuracy and stability of the data. Variance is a statistical quantity that measures the dispersion of a data set, reflecting the fluctuation of the data. Calculating the variance of the fused wall thickness data can help judge 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 small, it means that the multiple measurement results are close, indicating that the measurement process is stable; on the contrary, if the variance is large, it means that the measurement results have large fluctuations. The calculated variance is compared 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 this 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 evaluation of the pipe wall thickness. At this time, it is judged that the measurement result may not be reliable, in order to ensure the accuracy of the measurement result, the step of re-measurement is triggered. By re-measuring, more reliable data can be obtained, so as to better evaluate the actual wall thickness of the pipe and reduce the risk caused by misjudgment.

[0074] It should be noted that the wall thickness change safety threshold refers to the maximum allowable value of the change of the pipe wall thickness within a certain period of time. The pipe wall thickness critical value refers to the minimum safety standard value that the pipe wall thickness reaches. 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 processed for noise, it includes:

[0076] Obtain the original wall thickness data, and remove the obvious abnormal values by the three-sigma method:

[0077]

[0078]

[0079] Wherein, μ j represents the average value of the original wall thickness data at the measurement point j, σ j represents the standard deviation of the original wall thickness data at the measurement point j, and N represents the number of measurements at the measurement point j.

[0080] When the following condition is met At that time, the original wall thickness data at measurement point j is discarded, and the data at the discarded position is completed using linear interpolation. The completed original wall thickness data is calculated using the following formula:

[0081]

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

[0083] The filtered data is normalized using the min-max normalization method according to the following formula. The normalized original wall thickness data is then used to replace the original wall thickness data before noise processing, and the normalization is calculated using the following formula:

[0084]

[0085] in, This 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]; This is the original wall thickness data for the k-th measurement point j.

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

[0087] Application of the three-standard-deviation method: By calculating the mean and standard deviation of the original wall thickness data for each measurement point, the three-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 the mean ± 2 standard deviations, and the three-standard-deviation method 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 remove 100.0, ensuring that subsequent calculations are based on reasonable data and avoiding the influence of individual outliers on the overall measurement results.

[0089] Implementation of linear interpolation:

[0090] In linear interpolation, for the locations of outliers that have been removed, the missing wall thickness data is extrapolated using measurements before and after the outlier. This method helps to fill in data gaps and maintain data continuity without introducing additional errors.

[0091] Suppose there is a value that is rejected in a measurement sequence, with the previous and subsequent values being 5.0 and 5.2 respectively, interpolation can produce 5.1 as the completed value, 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 standardizing the wall thickness data to the range of 0 to 1, the problem of different measurement points having different data magnitudes 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 construction, avoiding the problem of algorithm instability or performance degradation caused by data magnitude differences.

[0094] The original wall thickness data of different measurement points may be between 5mm and 100mm. After normalization, regardless of the original magnitude of the data, it can be mapped to the interval [0,1], so that in data fusion, the contribution of each measurement point is balanced, ensuring the fairness and accuracy of the analysis.

[0095] Effect of comprehensive noise processing:

[0096] Through the above steps, the entire data processing process becomes more robust. Since the rejection and completion process can effectively eliminate sudden external interference and measurement errors, in complex underwater environments, valid data can be stably obtained, enhancing the anti-interference ability of the monitoring equipment.

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

[0098] Risk analysis supported by accurate data:

[0099] In subsequent wall thickness trend analysis and risk assessment, the processed data provides an accurate basis, enabling timely and effective assessment of historical data trends. Accurate wall thickness data can help engineers detect abnormal changes in the pipeline early and implement appropriate maintenance measures to reduce potential safety hazards.

[0100] If a trend analysis reveals that the wall thickness of a measurement point is continuously decreasing, and the data processing confirms that the data is reliable, maintenance procedures can be initiated in a timely manner to prevent future pipeline leaks or ruptures, reducing economic losses and environmental risks.

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

[0102] The ambient temperature, water flow velocity and measurement depth of the WAND sensor setting area are collected at the same time as the measurement results of the WAND sensor, and the corrected wall thickness data is calculated by the following formula:

[0103]

[0104] Wherein, α j represents the temperature correction coefficient; T j represents the ambient temperature of the test point j; T ref represents the reference temperature; β j represents the water flow velocity correction coefficient; V j represents the water flow velocity of the test point j; γ j represents the depth influence coefficient; D j represents the depth of the test point j, that is, the measurement depth; D ref represents the reference depth.

[0105] It should be noted that by introducing factors such as ambient temperature, water flow speed and measurement depth, the original wall thickness data can be corrected as needed, making the corrected wall thickness data closer to the real situation. Because environmental factors are very important in the operation state of underwater pipelines, they directly affect the wall thickness performance of the pipeline. Temperature rise can cause thermal expansion of the pipeline material, while water flow speed can affect the accuracy of data acquisition during measurement. Through appropriate correction, the true wall thickness of the pipeline can be more accurately reflected. Through a unified correction formula, data from different measurement points can be compared under the same standard. For data obtained under different environmental conditions, the effects of environmental differences can be eliminated after correction, enhancing the comparability between data. Measurements taken in environments with fast and slow water flow speeds can be directly compared after correction, effectively evaluating the health status of each measurement point. The temperature, water flow speed and depth of the underwater environment often have variability, and by monitoring these environmental parameters in real time and correcting the data, different underwater conditions can be adapted to. This enables efficient operation under various complex conditions. For example, in the deep sea or river rapids, environmental changes are frequent, and real-time correction can ensure the stability of the monitoring data and improve the safety monitoring capability of the pipeline. 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 indicates that the wall thickness of a measurement point is decreasing rapidly, the relevant management department can take timely maintenance measures to prevent potential pipeline leaks or ruptures. In multiple measurements and environmental changes, correction based on environmental parameters can help reduce measurement errors caused by environmental fluctuations, making monitoring data more consistent and enhancing stability. For example, in the case of long-term monitoring, real-time correction of environmental parameters can ensure the continuous reliability of the monitoring results, allowing maintenance teams 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, the risk assessment capability can be enhanced, and the reliability in complex underwater environments can be ensured. These beneficial effects not only improve the quality of monitoring data, but also provide important protection for the safe operation of the pipeline.

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

[0108]

[0109] Wherein, a0 is the linear expansion coefficient of the pipeline at the reference depth, the linear expansion coefficient is the elongation per degree of temperature rise, m is a constant, 0.1≤m≤0.3, u represents the pressure change value per unit increase in water depth, and P represents the pressure value at the test point.

[0110] Wherein, represents the adjustment of the linear expansion coefficient a0, represents the adjustment of the depth to the linear expansion coefficient; log(u·P) represents the adjustment of the pressure to the linear expansion coefficient, specific analysis, u represents the pressure change value per unit increase in water depth, and the pressure change value is known from P=ρgh / h=ρg, u is related to the density of water, u represents the adjustment of the density of water to the linear expansion coefficient, and P represents the adjustment of the pressure to the elongation.

[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, 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, and the reliability of the data can be enhanced, providing a more solid foundation for subsequent data analysis. The calculation method of the temperature correction coefficient allows adaptive adjustment under different environmental conditions, ensuring stable measurement results in the variable underwater environment. Accurate corrected wall thickness data provides a more reliable basis for subsequent trend analysis and risk assessment, helping to identify potential safety hazards in advance and improve the efficiency of pipeline management and maintenance. The correction considering temperature and pressure factors can prevent pipeline damage risks caused by environmental condition changes to some extent, ensuring the safety and stability of the pipeline during operation. The introduction of the temperature correction coefficient and its related parameter calculation makes the monitoring more intelligent, which 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 velocity correction coefficient is obtained by the following formula:

[0113]

[0114] Wherein, max(P) represents the maximum pressure value in the k measurement values at the measurement point j, min(P) represents the minimum pressure value in the multiple measurement values at the measurement point j, max(V j ) represents the maximum water flow velocity in the k measurement values at the measurement point j, min(V j ) represents the minimum water flow velocity in the k measurement values at the measurement point j, V ref represents the reference water flow velocity, and V j represents the current water flow velocity at the measurement point j.

[0115] It should be noted that the water flow rate correction coefficient corrects the original wall thickness data, which is:

[0116] Improving measurement accuracy: The water flow rate correction coefficient ensures that the corrected data can truly reflect the actual wall thickness of the pipeline by considering the influence of flow rate on wall thickness measurement. This improvement in accuracy ensures high reliability under different water flow conditions, effectively avoiding measurement errors caused by flow rate changes.

[0117] Reducing environmental interference: In underwater environments, flow rate changes may cause fluctuations in measurement signals. By introducing the water flow rate correction coefficient, the interference of flow rate on the original wall thickness data can be effectively filtered out, significantly reducing the impact of environmental noise on monitoring results, making the data more stable and reliable.

[0118] Enhancing adaptability: The application of water flow rate correction coefficient makes it more adaptable to complex underwater environments. Whether it is still water or strong flow conditions, real-time correction can maintain data consistency, thereby improving its versatility in practical applications.

[0119] Ensuring pipeline safety: Precise water flow rate correction can detect potential pipeline abnormalities in advance, allowing for timely maintenance measures. This effectively prevents pipeline damage or leakage risks caused by inaccurate measurements, ensuring the safety and reliability of pipelines in various underwater environments.

[0120] Improving credibility: Accurate water flow rate correction improves the credibility of monitoring. This is crucial for promoting new technologies and gaining user recognition, contributing to the widespread application and popularization of technology.

[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 corrected wall thickness data, a depth correction coefficient is also obtained through the following formula:

[0122] γ j =(1+f·(D j -D ref )·ρ D );

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

[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 the data deviation caused by the change of depth is eliminated. f is a constant (0.03): the constant is used to quantify the influence of depth change on wall thickness data, and 0.03 represents the order of magnitude of the influence of per 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. Different depths of water pressure and density may cause errors in the measured 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 differences with the depths of each measurement point, and to ensure that the correction is based on the relative change of depth. The water density at depth: the water density changes at different depths, and 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 the change of depth can be further accurately corrected.

[0125] Through the correction of the depth, the influence of different measurement depths on the wall thickness data can be effectively reduced, and the data of each measurement point is ensured to be comparable under the condition of inconsistent depth. Since the depth correction coefficient considers the influence of depth difference on measurement accuracy, more accurate wall thickness data can be obtained under different depth measurement conditions. The change of depth has a cumulative effect on wall thickness measurement, and the cumulative error can be minimized by the depth correction coefficient to ensure the consistency of the 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 helps the subsequent analysis and processing of data. 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 measurement point is fused, specifically including:

[0127] The average value of the corrected wall thickness data obtained by multiple detections of the same measurement point is calculated to obtain the fused wall thickness data, and the variance of the corrected wall thickness data is calculated according to the following formula:

[0128]

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

[0130] The historical wall thickness data is obtained, the variance of the historical wall thickness data at the measurement point is calculated, and the variance threshold is obtained;

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

[0132] It should be noted that through multiple detection and variance calculation, the influence of individual abnormal measurement data can be eliminated, and the stability and precision of the corrected wall thickness data can be improved. According to the historical data, the variance threshold is dynamically adjusted to ensure that different measurement points are processed differently according to their actual historical fluctuation, and errors caused by fixed threshold are avoided. When the variance exceeds the set threshold, timely re-measurement can effectively prevent the interference of abnormal data and ensure the quality of the finally obtained data. Through reasonable control of the variance, the data precision 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 the wall thickness trend of the pipeline wall thickness is analyzed based on the historical wall thickness data, it includes:

[0134] Obtain the historical wall thickness data of the same measurement point, establish the time data set of the historical wall thickness data based on the historical wall thickness data and the corresponding measurement time, and obtain the predicted wall thickness data by substituting the measurement time into the wall thickness trend function.

[0135] It should be noted that when the wall thickness trend of the pipeline wall thickness is analyzed based on the historical wall thickness data, it can be specifically:

[0136] Obtain the historical wall thickness data:

[0137] Collect and store the historical wall thickness data of each measurement point of the pipeline, and the data should include the measurement time, the measurement point number, and the measured corrected wall thickness value. The measured corrected wall thickness value can be the value obtained by correcting and fusing multiple measurement values of the measurement point, i.e. the fused wall thickness data value of the measurement point, or the corrected wall thickness value obtained by correcting the single measurement value of the measurement point. Data format: (time, measurement point number, corrected wall thickness value).

[0138] Construct the time data set:

[0139] For each measurement point, extract the corresponding historical wall thickness data and measurement time 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 )}; wherein, wherein, t n n represents the time of the nth measurement, T j (t n ) represents the corrected wall thickness data of measurement point j at time t n .

[0140] Linear regression fitting:

[0141] Using linear regression method, the measurement time t n of the time data set is taken as the independent variable, and the corrected wall thickness value T j is taken as the dependent variable, to fit the wall thickness trend function of measurement point j. n

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

[0143] Where T j (t n ) is the trend function of wall thickness change over time, a j and b j are regression coefficients obtained by linear regression fitting, representing the speed of wall thickness change and the initial wall thickness value respectively.

[0144] Generate wall thickness trend function:

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

[0146] Predict 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 predicted wall thickness value of measurement point j at future time t future .

[0148] Results output and analysis:

[0149] Output the wall thickness trend function and predicted wall thickness data of each measurement point for analysis of the future wall thickness change trend of the pipeline.

[0150] According to the predicted wall thickness data, evaluate whether the pipeline has the risk of wall thickness thinning to the critical value in the future, so as to take preventive measures in advance.

[0151] ​It can be understood that through the analysis of historical data and trend prediction, the change of the pipeline wall thickness can be predicted in advance to avoid unexpected leakage and damage. According to the trend function, the maintenance cycle of the pipeline is dynamically adjusted to reduce unnecessary detection and maintenance and improve the maintenance efficiency. Through the data-driven wall thickness trend analysis, a scientific basis can be provided for the management and maintenance decision of the pipeline to reduce the failure risk. 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 size range of the second deviation threshold is 20%-30% of the nominal wall thickness data.

[0154] Referring to Figure 2 The embodiment of the present application also provides an underwater pipeline wall thickness monitoring processing system, which comprises:

[0155] A WAND sensor configured to collect original wall thickness data at a set measurement point;

[0156] A data collector configured to collect environmental parameters obtained by the sensor in real time;

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

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

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

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

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

[0162] An abnormality judgment module 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 the environmental parameters are collected:

[0164] Thermocouples or RTD sensors can be used to measure water temperature, ultrasonic or electromagnetic flow sensors can be used to accurately measure flow velocity in underwater environments, and pressure sensors or ultrasonic depth sounders can be used to measure water depth in underwater environments.

[0165] Install the sensors near or outside the pipe, ensuring they are in contact with the environmental medium. Connect the data output interfaces of the temperature, flow rate, and depth sensors to the data acquisition unit. Use a microcontroller or data logger to acquire and store environmental parameters in real time. Transmit the acquired environmental parameters wirelessly (e.g., Wi-Fi, Bluetooth) or via wired means (e.g., 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 and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

[0167] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... 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 Taking a processor 410 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 Taking the example of a connection between China and Israel via a bus.

[0168] The memory 420, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the underwater pipeline wall thickness monitoring and processing method in this embodiment of the 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 can include a program storage area and a data storage area, where the program storage area can store an operating system, application programs required for at least one function, and the data storage area can store data created according to the use of the terminal, etc. In addition, the memory 420 can include a high-speed random access memory, and can further include a non-volatile memory such as at least one of a disk storage device, a flash memory device, or other non-volatile solid state storage device. In some examples, the memory 420 can further include a memory disposed remotely with respect to the processor 410, which can be connected to the device through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0170] The input device 430 can be used to receive input digital or character information, and to generate signal input related to user settings and function control of the device. The output device 440 can include a display device such as a display screen.

[0171] The embodiment of the present application also provides a storage medium containing computer executable instructions, which are used to execute an underwater pipeline wall thickness monitoring processing method when executed by a computer processor, the method comprising: setting a plurality of measurement points according to the length of the pipeline, each measurement point being measured N times by a WAND sensor, collecting the measurement results of the WAND sensor and recording as original wall thickness data, the original wall thickness data being 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 noise processing of the original wall thickness data, collecting environmental parameters, correcting the original wall thickness data according to the environmental parameters to obtain corrected wall thickness data, the corrected wall thickness data being in the form of The environmental parameters include water flow rate, temperature and depth; data fusion is performed on the corrected wall thickness data of each measurement point to obtain fused wall thickness data, the fused wall thickness data being in the form of AvgWANDTH j The variance of the corrected wall thickness data of each measurement point is calculated and compared, if the variance of the corrected wall thickness data is greater than or equal to a set variance threshold, re-measurement is performed; 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 in the historical wall thickness data is recorded as a first difference value, and the difference between the fused wall thickness data and the nominal wall thickness data is recorded as a second difference value, when the first difference value is greater than or equal to a pre-set first deviation threshold or the second difference value is greater than or equal to a pre-set second deviation threshold, the corresponding measurement point is marked as an abnormal point; wall thickness trend analysis is performed on the pipeline wall thickness based on the historical wall thickness data to obtain predicted wall thickness data, the predicted wall thickness data being in the form of According to the historical wall thickness data, a wall thickness change rate of the pipeline is acquired, when the wall thickness change rate is greater than or equal to a preset wall thickness change safety threshold, it is determined that the measurement point is in a high-risk area; when the predicted wall thickness data is lower than a preset pipeline wall thickness threshold, it is determined that the area of the measurement point has a damage risk; wherein t represents time, represents predicted wall thickness data of the j measurement point at a set time t.

[0172] The computer storage medium of the embodiment of the application 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 may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, 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, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0173] The computer readable signal medium can include a data signal propagated in a baseband or as a part of a carrier wave, in which computer readable program code is carried. Such a propagated data signal can take on many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can transmit, propagate or transport program for use by or in connection with an instruction execution system, device or component.

[0174] The program code contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0175] Computer program code for carrying out operations of embodiments of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0176] It is to be understood that the above description is merely a preferred embodiment of the application and the applied technical principles. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, modifications and substitutions can be made without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for monitoring and processing the wall thickness of underwater pipelines, characterized in that, include: Several measurement points are set according to the length of the pipeline. Each measurement point is measured N times using a WAND sensor. The measurement results from 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 k-th measurement, k = 1, 2, ..., N; After noise processing of the original wall thickness data, environmental parameters are collected, and the original wall thickness data is corrected based on the environmental parameters to obtain corrected wall thickness data. The corrected wall thickness data is in the form of... The environmental parameters include water flow rate, temperature, and depth; The corrected wall thickness data at each measurement point are fused to obtain fused wall thickness data, which is in the form of AvgWANDTH. j Calculate and compare the variance of the corrected wall thickness data at each measurement point. If the variance of the corrected wall thickness data is greater than or equal to the set variance threshold, then remeasure. If the variance of the corrected wall thickness data is less than the set variance threshold, then the historical wall thickness data and the nominal wall thickness data of the pipeline are obtained. The difference between the minimum value of the merged wall thickness data and the historical wall thickness data is recorded as the first difference, and the difference between the merged 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 anomaly point. Based on the historical wall thickness data, a wall thickness trend analysis is performed on the pipe wall thickness to obtain predicted wall thickness data. The predicted wall thickness data is in the following format: The pipe 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 a preset wall thickness change safety threshold, the measurement 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 area of ​​the measurement point is determined to have a risk of damage; where t represents time. This represents the predicted wall thickness data for measurement point j at a set time t.

2. The underwater pipeline wall thickness monitoring and processing method according to claim 1, characterized in that, When performing noise processing on the original wall thickness data, the following are included: The original wall thickness data was obtained, and obvious outliers were removed using the three-standard-deviation method: Where, μ j σ represents the average value of the original wall thickness data at measurement point j. j The standard deviation of the original wall thickness data at measurement point j is represented by N, and the number of measurements taken at measurement point j is represented by N. When satisfied At that time, the original wall thickness data at measurement point j is discarded, and the data at the discarded position is completed using linear interpolation. The completed original wall thickness data is calculated using the following formula: in, This represents the original wall thickness data after completion; The filtered data is normalized using the min-max normalization method according to the following formula. The normalized original wall thickness data is then used to replace the original wall thickness data before noise processing, and the normalization is calculated using the following formula: in, This 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]; This is the original wall thickness data for the k-th measurement point j.

3. The underwater pipeline wall thickness monitoring and processing method according to claim 2, characterized in that, When collecting environmental parameters and correcting the original wall thickness data based on these parameters to obtain corrected wall thickness data, the process includes: While collecting the measurement results from the WAND sensor, the ambient temperature, water flow velocity, and measurement depth of the area where the WAND sensor is set are also collected, and the corrected wall thickness data is calculated using the following formula: Where, α j T represents the temperature correction factor; j T represents the ambient temperature at test point j; ref Indicates the reference temperature; β j V represents the water flow velocity correction factor; j γ represents the water flow velocity at test point j; j D represents the depth of influence coefficient. j D represents the depth of test point j, i.e., the measurement depth; ref Indicates the reference depth.

4. The underwater pipeline wall thickness monitoring and processing method according to claim 3, characterized in that, When collecting environmental parameters and correcting the original wall thickness data based on these parameters to obtain corrected wall thickness data, the temperature correction coefficient is also obtained using the following formula: Where 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 per 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, characterized in that, When collecting environmental parameters and correcting the original wall thickness data based on these parameters to obtain corrected wall thickness data, the water flow velocity correction coefficient is also obtained using the following formula: Where max(P) represents the maximum pressure value among k measured values ​​at measurement point j, min(P) represents the minimum pressure value among multiple measured values ​​at measurement point j, and max(V) represents the maximum pressure value among k measured values. j () represents the maximum water flow velocity among the k measurements at measurement point j, min(V) j V represents the minimum water flow velocity among the k measured values ​​at measurement point j. ref V represents the reference water flow velocity. j This represents the water flow velocity at test point j.

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

7. The underwater pipeline wall thickness monitoring and processing method according to claim 6, characterized in that, After data fusion of the corrected wall thickness data for each measurement point, the specific process includes: The average value of the corrected wall thickness data obtained from multiple measurements at the same measurement point is calculated to obtain the fused wall thickness data. The variance of the corrected wall thickness data is then calculated 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 measurement point, and use it as the variance threshold; The historical wall thickness data refers to corrected wall thickness data collected in the past.

8. The underwater pipeline wall thickness monitoring and processing method according to claim 7, characterized in that, When performing wall thickness trend analysis on the pipe wall thickness based on the historical wall thickness data, it includes: Historical wall thickness data of the same measurement point is obtained. A time dataset of 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 based on the measurement time and historical wall thickness data. The measurement time is substituted into the wall thickness trend function to obtain the 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 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 1-9, characterized in that, The system includes: The WAND sensor is configured to acquire raw wall thickness data at set measurement points; The data acquisition unit is configured to collect environmental parameters acquired by sensors in real time; The noise processing module is configured to process the noise in the raw wall thickness data; The environmental correction module is configured to correct the original wall thickness data based on the collected environmental parameters and calculate the corrected wall thickness data. The data fusion module is configured to fuse multiple measurements of corrected wall thickness data and calculate the mean and variance of the corrected wall thickness data. The trend analysis module is configured to perform wall thickness trend analysis based on historical wall thickness data, establish a time dataset of historical wall thickness data, and obtain the wall thickness trend function through linear regression fitting. The prediction module is configured to substitute the measurement time into the wall thickness trend function to obtain the predicted wall thickness data; The anomaly detection module is configured to determine whether a measurement point is an anomaly based on 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 includes: One or more processors; 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 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 executed by the processor, the program implements the underwater pipeline wall thickness monitoring and processing method as described in any one of claims 1-9.

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