Pipeline integrity intelligent diagnosis and life prediction system based on time series modeling

Abstract

The application discloses a pipeline integrity intelligent diagnosis and life prediction system based on time sequence modeling, and relates to the technical field of pipeline health monitoring.The system realizes the synchronous acquisition, unified sampling and standardized processing of multi-source monitoring data by arranging multiple online sensors such as pressure, temperature, vibration, acoustic emission and electrochemical corrosion along the pipeline.A double-flow structure of short-time window and long-term window is constructed to simultaneously extract sudden abnormal features and long-term degradation features, and the importance adaptive fusion of different sensing channels is realized through a cross-modal attention mechanism to obtain comprehensive time sequence features representing the pipeline operation state.The system further constructs a health index model and realizes the continuous characterization of the health state and the dynamic prediction of the remaining life based on an exponential degradation equation.Combining the health index change rate and the life threshold, the system forms a multi-level early warning mechanism, and the actual maintenance records and operation data can be fed back to the model to realize online parameter updating and self-learning.

Description

Technical Field

[0001] This invention relates to the field of pipeline health monitoring technology, and more specifically, to a pipeline integrity intelligent diagnosis and life prediction system based on time-series modeling. Background Technology

[0002] As the most crucial transportation unit in long-distance oil and gas pipeline networks, urban gas pipeline networks, and chemical plants, the integrity of pipelines directly impacts personal safety, environmental protection, and the stable operation of the facilities. Traditional pipeline integrity management primarily relies on periodic offline monitoring methods, such as internal detectors ("intelligent pipeline cleaning"), magnetic particle testing, ultrasonic testing, and ground patrols. These methods can detect defects such as moderate to severe corrosion, deformation, and mechanical damage to a certain extent. However, these methods often rely on "intermittent inspections," resulting in long inspection cycles, high costs, and difficulty in timely detecting rapid deterioration or sudden failures occurring between inspections. This presents a significant lag for long-distance pipelines, large-scale pipeline networks, and high-risk areas. Once a leak or instability accident occurs within the inspection cycle, it often exceeds the early warning capabilities of traditional offline monitoring.

[0003] With the development of online monitoring technology, the industry has gradually introduced various online sensors, such as those for pressure, flow, temperature, vibration, acoustic emission, and electrochemical corrosion, and combined them with SCADA systems or simple data acquisition terminals to achieve remote monitoring of pipelines. However, most existing online monitoring methods still remain at the level of "single-variable threshold alarm" or "simple rule triggering," that is, setting upper and lower limits for a single sensor value, and triggering an alarm when the value exceeds the limit. These methods are difficult to comprehensively consider the coupling relationship between multiple physical quantities and cannot effectively identify early weak anomalies and latent degradation processes. At the same time, the complex on-site operating conditions and frequent switching of operating conditions result in a large amount of noise and non-stationary components in the signal, making the simple threshold method prone to false alarms and missed alarms. Some studies have begun to try to introduce statistical methods or traditional time series models to perform trend analysis and anomaly detection for a certain type of signal, but they often only target single-channel or a few-channel data, failing to fully utilize multi-source sensor information, and lacking the ability to uniformly model multimodal time series data under complex operating conditions.

[0004] In terms of lifespan prediction, current engineering practices still commonly employ simplified methods based on empirical formulas, safety factors, or a limited number of historical failure samples. Examples include estimating remaining wall thickness based on empirical corrosion rates or discounting the design life to estimate remaining life. These methods typically treat pipelines as static objects, rarely explicitly incorporating real-time operational data and online monitoring results. They fail to adequately consider the impact of load fluctuations, changes in the transported medium, and variations in environmental conditions, making it difficult to provide dynamic and refined remaining life estimates for specific pipe sections. While some literature has proposed using machine learning models for fault diagnosis or lifespan prediction in recent years, most work remains at the level of offline modeling and single data sources. There is a lack of effective fusion mechanisms for multi-source time-series data such as pressure, vibration, acoustic emission, and electrochemical corrosion. Diagnostic results are difficult to integrate with lifespan prediction results to form a unified and interpretable health index system, and a complete solution that can operate and adaptively update over long periods in the engineering field is also lacking.

[0005] Therefore, there is an urgent need for a pipeline integrity intelligent diagnosis and life prediction system based on time-series modeling to solve these problems. Summary of the Invention

[0006] The purpose of this invention is to solve the technical problems mentioned in the background art and to provide a pipeline integrity intelligent diagnosis and life prediction system.

[0007] The above-mentioned objective of the present invention is achieved through the following technical solution:

[0008] A pipeline integrity intelligent diagnosis and life prediction system based on time-series modeling includes:

[0009] The multi-source sensor acquisition module is used to collect raw time-series data of pressure, temperature, vibration, acoustic emission, electrochemical corrosion signals, flow rate, and environmental parameters during pipeline operation.

[0010] The data synchronization and processing module is used to perform timestamp alignment, frequency resampling, noise filtering, missing data completion, and data normalization on the original time-series data to generate a fused input sequence with a unified time base.

[0011] The temporal feature modeling and anomaly diagnosis module is used to construct a temporal coding model based on the fused sequence, jointly model short-term mutation features and long-term degradation features, and output diagnostic results reflecting changes in pipeline health.

[0012] The pipeline health index generation module is used to generate a health index based on the degree of deviation of the time series characteristics, in order to characterize the health status of the pipeline at different time points.

[0013] The lifespan prediction module is used to construct a dynamic degradation model based on the health index and historical degradation trajectory to predict the remaining lifespan of the pipeline.

[0014] The early warning and visualization module is used to generate multi-level early warnings based on the life prediction results and provide pipeline maintenance decision suggestions.

[0015] Furthermore, the multi-source sensing acquisition module includes a pressure sensor for detecting medium pressure, a temperature sensor for detecting medium and ambient temperature, an acceleration sensor for detecting pipeline structure vibration, an acoustic emission sensor for capturing acoustic emission signals from microcracks, an electrochemical corrosion probe for monitoring the corrosion rate of the test piece, and a flow meter for monitoring changes in operating flow rate.

[0016] Furthermore, the data synchronization and processing module resamples sensor data at different sampling frequencies by establishing a unified time-series baseline and completes missing data using a weighted time window interpolation method to ensure strict alignment of multi-source data in the time dimension.

[0017] Furthermore, the temporal feature modeling and anomaly diagnosis module adopts a cross-modal attention mechanism to perform weighted fusion of temporal features from different sensor channels, enabling the model to automatically highlight the sensor signals with the most diagnostic value under the current operating conditions.

[0018] Furthermore, the time-series feature modeling and anomaly diagnosis module includes a short-term mutation feature extraction sub-model and a long-term degradation feature extraction sub-model. The former is used to capture transient anomalies such as leakage, impact, and fatigue crack initiation, while the latter is used to identify slow degradation processes such as corrosion deepening, wall thickness reduction, and material fatigue.

[0019] Furthermore, the pipeline health index generation module calculates the degree of deviation between the current time-series characteristics and the reference health state, and generates a health index based on the deviation magnitude, deviation direction, and continuity. This index is used to quantify the pipeline health state and serve as the basic input for the life prediction model.

[0020] Furthermore, the lifespan prediction module fits the changing trend of the health index based on a nonlinear degradation model and adopts a dynamic parameter update strategy to continuously correct the coefficients of the degradation model based on newly collected monitoring data, thereby achieving adaptive adjustment of the remaining lifespan prediction.

[0021] Furthermore, the early warning and visualization module sets multi-level early warning thresholds, which are determined comprehensively based on remaining lifespan, rate of change of health index, and abnormal diagnosis results, in order to achieve early medium-level early warning, trend early warning, and high-risk early warning before failure.

[0022] Furthermore, the maintenance suggestions provided by the early warning and visualization module include: recommended maintenance time windows, influencing factor analysis, pipeline segment priority ranking, and maintenance plan recommendations, enabling maintenance personnel to formulate refined maintenance strategies based on the actual condition of the pipeline.

[0023] Furthermore, the system includes a model self-learning module, which is used to periodically update the time-series feature model and life prediction model based on newly input historical monitoring data, on-site maintenance records and failure samples, so as to achieve self-evolutionary performance improvement of the entire system.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This invention constructs a unified modeling framework for multi-source time-series data, enabling simultaneous processing and multi-scale feature extraction of various monitoring signals, including pipeline pressure, temperature, vibration, acoustic emission, and electrochemical corrosion. Unlike traditional alarm methods based on single-sensor thresholds, this invention comprehensively characterizes both sudden anomalies and gradual degradation trends in pipelines through a dual-flow structure of short-term and long-term windows. This allows the system to simultaneously identify transient impact events (such as initial leaks and mechanical impacts) and long-term latent degradation processes (such as deepening corrosion and fatigue accumulation). This multi-scale modeling approach significantly improves the sensitivity and stability of anomaly detection, ensuring reliable diagnostic capabilities even under complex operating conditions and high-noise environments.

[0026] 2. Regarding feature fusion, the cross-modal attention mechanism proposed in this invention can automatically allocate the contribution weights of each sensing channel according to different operating conditions. It explicitly expresses the potential correlations and coupling relationships between physical quantities such as pressure, vibration, acoustic emission, and electrochemical signals as continuous weights, thereby overcoming the information loss caused by traditional multi-channel averaging or simple splicing. This mechanism enables the system to automatically focus on key channels based on actual operating conditions. For example, it automatically increases the weights of acoustic emission and pressure fluctuations during the leak initiation stage and increases the contribution of electrochemical signals during the corrosion propagation stage, achieving adaptive identification of different degradation modes, resulting in more accurate and interpretable diagnostic results.

[0027] 3. In terms of health status assessment and lifespan prediction, this invention constructs a complete prediction chain from multi-source fusion features to the Health Index (HI) and then to the exponential degradation model, realizing unified ontological modeling of health diagnosis and remaining life assessment. Through health index curve fitting and online update mechanisms based on real monitoring data, this invention can dynamically track the actual degradation rate of pipelines, obtain the remaining life (RUL) prediction results over time, and establish a three-level early warning system (light, medium, and severe), providing pipeline maintenance personnel with clear maintenance priorities and decision-making basis. Furthermore, the self-learning update mechanism of this invention can continuously feed back on-site maintenance records, fault samples, and new operating condition data to the model, forming a continuously evolving intelligent diagnostic system. This enables the system to maintain adaptability to new fault modes and high prediction accuracy during long-term operation. In summary, this invention has significant advantages in intelligent diagnostic accuracy, early warning timeliness, lifespan prediction reliability, and engineering feasibility. Attached Figure Description

[0028] Fig. 1 This is a system block diagram of the intelligent pipeline integrity diagnosis and life prediction system proposed in this invention;

[0029] Fig. 2 This is a system flowchart of the intelligent pipeline integrity diagnosis and life prediction system proposed in this invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the following description is provided in conjunction with embodiments and appendices. Figs. 1-2 The present invention will be further described in detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0031] The present invention will be further described in detail below with reference to specific embodiments, but the present invention is not limited to these embodiments. Equivalent modifications made by those skilled in the art without departing from the principles of the present invention should fall within the protection scope of the present invention.

[0032] Example: This embodiment of the invention provides a pipeline integrity intelligent diagnosis and life prediction system. The system is deployed along a 50km long pipeline, with various types of sensors laid along the pipeline, including: pressure sensors for collecting internal pipe pressure, temperature sensors for collecting medium temperature, pipe wall temperature, and ambient temperature, vibration acceleration sensors for collecting pipeline structural vibration response, acoustic emission sensors for capturing high-frequency acoustic emission signals from microcracks and leaks, electrochemical corrosion probes for collecting corrosion current or corrosion potential, flow meters for measuring instantaneous flow in each pipe section, and environmental sensors for measuring external environmental conditions such as wind speed and humidity. All sensors are connected to a data acquisition terminal via fieldbus or industrial Ethernet, and then uploaded to the intelligent analysis platform of this invention via wired or wireless networks. The raw outputs of various sensors are time series with different sampling frequencies and start times. To achieve unified modeling, the system first resamples and synchronizes the data from each channel.

[0033] With the first Taking one sensor channel as an example, its original continuous-time signal is denoted as: The system selects a uniform sampling period. By mapping it to a unified time baseline using a resampling function, a discrete sequence is obtained: ;

[0034] in, Indicates the first The channel is in Resampled values ​​at each time step Represents the resampling function. The original continuous-time signal, To standardize the sampling time interval, For discrete-time indexes under a unified time baseline This is the sensor channel index. If there is missing data at certain time steps, the system uses an interpolation function to complete it. ;

[0035] in, For interpolation functions, and This function represents the resampled values ​​of the nearest valid time step. The values ​​of missing points can be estimated using this function, thus obtaining a continuous time series.

[0036] To eliminate differences in dimensions and magnitudes between different sensors, the data synchronization and processing module further standardizes the data from each channel. For the first... The channel calculates its average value within the selected historical reference time interval. with standard deviation Resampled values Mapped to dimensionless normalized features: ;

[0037] in, Indicates the first Channel at time step Standardized eigenvalues Indicates channel The statistical average over the reference time interval. Indicates channel The statistical standard deviation over the same time interval. Through the above standardization, and Difference divided by The result obtained later As a dimensionless quantity, it eliminates the differences in units and numerical scales between different sensors, facilitating subsequent unified modeling.

[0038] After obtaining the normalized sequence of all channels Subsequently, the system simultaneously characterizes short-term abrupt change behavior and long-term degradation behavior, introducing a two-stream structure to construct short-term and long-term window feature matrices respectively. Assume that a total of [number missing] [units missing] are deployed along the pipeline. One sensing channel, short-time window length is Then at time step The short-time window matrix on is defined as: ;

[0039] in, For size The matrix, This represents the total number of sensor channels and the corresponding row number in the matrix. This indicates the number of time steps contained in the short-time window, corresponding to the number of columns in the matrix. Each element is derived from the normalized features of the corresponding channel at the corresponding time step. .therefore This can be understood as "multi-channel local feature fragments of all channels within a relatively short time range".

[0040] Similarly, the system sets the long-term window length to... And satisfy In time step Construct the long-term window matrix above:

[0041] in, For size The matrix, This also represents the total number of sensor channels. This indicates the length of the long-term window, which covers a longer historical time range than the short-term window. The elements of the matrix also have standardized features. Composition. From this, we can see that... , and The three form a hierarchical relationship from "single-channel single-point characteristics" to "full-channel short-term window characteristics" and then to "full-channel long-term window characteristics".

[0042] The temporal feature modeling and anomaly diagnosis module encodes the short-time window matrix and the long-time window matrix respectively, extracting the corresponding short-time feature vectors and long-time feature vectors. Let the short-time encoding function be... The long-term encoding function is Then at time step The encoding result is: ;

[0043] in, Indicates at time step The short-time feature vector obtained by encoding the short-time window matrix Indicates at time step The long-term feature vector is obtained by encoding the long-term window matrix. and This can be implemented using temporal neural networks such as one-dimensional convolutional neural networks, recurrent neural networks, or Transformer encoders. Subsequently, the system concatenates the two to obtain a comprehensive temporal feature vector: ;

[0044] in, Indicates at time step The comprehensive feature vector is composed of short-time feature vectors. With long-term eigenvectors It is formed by connecting the links.

[0045] To further highlight the contribution of different sensing channels to diagnosis under different operating conditions, this invention introduces a cross-modal attention mechanism based on comprehensive features. It is assumed that the feature extraction network can obtain the value of each channel at each time step... intermediate feature vector The system constructs query vectors. For example, based on comprehensive features It is obtained through linear transformation, and then a scoring function is used. Calculate the first Channel attention score: ;

[0046] in, Indicates at time step No. The channel attention scoring scalar, This represents a query vector used to measure the current overall state. Indicates the first The feature vector of the channel, The scoring function can be a vector inner product, a bilinear function, or a nonlinear function implemented by a feedforward neural network. To ensure that the sum of the channel weights is 1, the system uses the softmax function to normalize the scores. ;

[0047] in, Indicates at time step No. The attention weights for each channel are calculated by dividing the sum of the exponential values ​​of all channel scores by the denominator. This is a dimensionless quantity between 0 and 1, satisfying the condition that the summation over all channels equals 1. Finally, the system performs a weighted summation of the features from each channel based on the attention weights to obtain the fused temporal features: ;

[0048] in, Indicates at time step The final fused feature vector is obtained by analyzing the intrinsic features of all channels. According to weight The linear combination yields a feature vector with the same dimension as a single channel feature vector.

[0049] To quantify the current health status of pipelines, this invention introduces a health index. In the initial stage of system debugging, a segment of historical monitoring data that was confirmed to be fault-free and without significant corrosion was selected. The corresponding fusion features were extracted and averaged to serve as a reference health feature vector. At any time step in actual operation The degree of deviation between the fused features and the reference features is defined as: ;

[0050] in, Indicates at time step Feature deviation distance, This represents the vector norm, such as the 2-norm. Based on historical experience or offline analysis, this invention selects a maximum reference distance. When the deviation distance reaches or exceeds At that time, it was considered to be approaching a severely abnormal state. Therefore, a dimensionless health index can be constructed: ;

[0051] in, Indicates time step Health index, This indicates the deviation distance at that time step. This represents the maximum reference deviation used for normalization, since and Since they have the same dimension of distance, their ratio is a dimensionless quantity. The value typically ranges from 0 to 1, with values ​​closer to 1 indicating better health and closer to 0 indicating a closer state of failure. This is achieved through continuous calculation. This allows us to obtain a health index curve that changes over time, which can be used to observe the overall degradation trend of pipelines.

[0052] Based on the health index, the lifespan prediction module establishes a health index degradation model over time. This embodiment uses an exponential degradation model: ;

[0053] in, Representing continuous time Health index at any time This represents the initial health magnitude parameter, reflecting the difference between the health index at the reference starting point and the long-term limit value. This represents the degradation rate parameter, with the dimension of the reciprocal of time (1 / time). A larger value indicates faster degradation. This represents a long-term limiting parameter, reflecting the lower limit to which the health index approaches under extreme aging. This represents the service time, measured in units of time, from the reference start time. Due to the exponential part of the exponential function... It must be a dimensionless quantity, therefore The dimension is 1 / time, ensuring the autonomy of the model's dimensions.

[0054] The system is based on a historical health index sequence { For parameters Perform fitting, for example, by minimizing the error between the model's predicted values ​​and the observed values ​​using the least squares method. Set a health index failure threshold. ,when If the pipeline is considered to have reached a state requiring shutdown or maintenance, then according to the degradation model: ;

[0055] in, Let be the predicted failure time point. Solving the above equation yields: ;

[0056] in, The parentheses represent the natural logarithm function. It is a dimensionless ratio. It has a time dimension, therefore The dimension of is time.

[0057] Let the current time be Then the remaining lifespan is: ;

[0058] in, Indicates the current moment The corresponding predicted remaining useful life, in units of time. Indicates the predicted failure time. This indicates the current service time, thus ensuring that the calculation process from health index to degradation model to remaining lifespan is completely consistent in terms of dimensions.

[0059] To adapt to the impact of changing operating conditions and maintenance operations on the degradation trajectory, this invention further introduces a dynamic parameter update mechanism. The lifespan prediction module re-aggregates the latest health index observation data in each update cycle and constructs a loss function. ;

[0060] in, Represents the loss function. Indicates at a point in time The observed real health index Indicates based on the current parameter The predicted health index calculated by the degradation model. For any parameter... The update is performed using gradient descent: ;

[0061] in, The parameter values ​​before the update. For the updated parameter values, The learning rate hyperparameter is controlled by adjusting the step size. For the loss function with respect to parameters The partial derivatives. By repeatedly performing the above process, the degradation model parameters can be adaptively corrected based on the latest monitoring data, making the remaining life prediction results closer to reality.

[0062] The early warning and visualization module implements multi-level early warning strategies based on health index and remaining life expectancy results. The system pre-sets health index thresholds. , and remaining lifetime threshold , And introduce the rate of change of health index: ;

[0063] in, This represents the rate of change of the health index between adjacent time steps, with the dimension 1 / time. and These are the health indices for the current time step and the previous time step, respectively. If Continuously large negative values ​​and Still higher The system issues a trend warning; when or When the system issues a medium-level warning; when or The system will issue a high-risk warning at that time. The aforementioned thresholds can be set by experts based on pipeline grade, media hazard, and the company's risk appetite.

[0064] In the visualization interface, the system divides the entire pipeline into multiple sections, displaying a health index curve, remaining life prediction curve, and warning level for each section in real time. The system can also... The numerical values ​​and health index trends prioritize pipe sections and generate a maintenance priority list. For pipe sections with warnings, the system combines the abnormal diagnosis results to provide a fault type prediction, such as "suspected external corrosion aggravation", "suspected fatigue crack initiation", "suspected small flow leakage", etc., and recommends corresponding maintenance solutions, such as online cathodic protection enhancement, local welding repair, pipe section replacement or internal inspection, thus providing decision support for operation and maintenance personnel.

[0065] To continuously improve diagnostic and predictive capabilities, this invention incorporates a model self-learning module that feeds back on on-site maintenance records and failure instances into the model. When actual maintenance reveals severe corrosion, perforation, or cracks in a section of pipeline, the system labels the multi-source sensor data, health index curves, and final diagnostic conclusions for that time period as training samples with ground truth labels, incorporating them into the retraining process of the anomaly type classification model and the degradation model.

[0066] The model self-learning module jointly optimizes the temporal feature extraction network, attention weight allocation mechanism, and lifetime prediction model based on new samples, so that the system’s ability to identify similar failure modes is continuously improved in long-term operation, and its ability to fit degradation trajectories under different working conditions is gradually enhanced.

[0067] In terms of engineering implementation, the modules of this invention can be centrally deployed on a cloud platform server, or the data acquisition, preprocessing and some preliminary diagnostic functions can be deployed on edge computing terminals along the line to reduce communication bandwidth usage, while lifetime prediction and global optimization decision-making can be deployed on the central platform.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A pipeline integrity intelligent diagnosis and life prediction system based on time-series modeling, characterized in that, include: The multi-source sensor acquisition module is used to collect raw time-series data of pressure, temperature, vibration, acoustic emission, electrochemical corrosion signals, flow rate, and environmental parameters during pipeline operation. The data synchronization and processing module is used to perform timestamp alignment, frequency resampling, noise filtering, missing data completion, and data normalization on the original time-series data to generate a fused input sequence with a unified time base. The temporal feature modeling and anomaly diagnosis module is used to construct a temporal coding model based on the fused sequence, jointly model short-term mutation features and long-term degradation features, and output diagnostic results reflecting changes in pipeline health. The pipeline health index generation module is used to generate a health index based on the degree of deviation of time-series characteristics, which is used to characterize the health status of the pipeline at different time points. Specifically: Introducing a health index In the initial stage of system debugging, a segment of historical monitoring data that was confirmed to be free of faults and significant corrosion was selected. The corresponding fusion features were extracted and averaged to serve as a reference health feature vector. In actual operation, the degree of deviation between the fused features and the reference features at any time step k is defined as: ； in, The distance represents the feature deviation at time step k, and ∥⋅∥ represents the vector norm; a maximum reference distance is selected based on historical experience or offline analysis. When the deviation distance reaches or exceeds At that time, it was considered to be approaching a severely abnormal state; therefore, a dimensionless health index was constructed: ； in, This represents the health index at time step k. This indicates the deviation distance at that time step. This represents the maximum reference deviation used for normalization, since and Since they have the same dimension of distance, their ratio is a dimensionless quantity. The value typically ranges from 0 to 1, with values ​​closer to 1 indicating better health and closer to 0 indicating a closer state of failure. This is achieved through continuous calculation. This allows us to obtain a health index curve that changes over time, which can be used to observe the overall degradation trend of the pipeline. The lifespan prediction module is used to construct a dynamic degradation model based on the health index and historical degradation trajectory to predict the remaining lifespan of the pipeline. The early warning and visualization module is used to generate multi-level early warnings based on the life prediction results and provide pipeline maintenance decision suggestions.

2. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 1, characterized in that, The multi-source sensing acquisition module includes a pressure sensor for detecting medium pressure, a temperature sensor for detecting medium and ambient temperature, an acceleration sensor for detecting pipeline structure vibration, an acoustic emission sensor for capturing acoustic emission signals from microcracks, an electrochemical corrosion probe for monitoring the corrosion rate of test specimens, and a flow meter for monitoring changes in operating flow rate.

3. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 1, characterized in that, The data synchronization and processing module resamples sensor data at different sampling frequencies by establishing a unified time-series baseline and completes missing data by using a weighted time window interpolation method, so as to ensure strict alignment of multi-source data in the time dimension.

4. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 1, characterized in that, The temporal feature modeling and anomaly diagnosis module adopts a cross-modal attention mechanism to perform weighted fusion of temporal features from different sensor channels, so that the model automatically highlights the sensor signals with the most diagnostic value under the current operating conditions.

5. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 1, characterized in that, The time-series feature modeling and anomaly diagnosis module includes a short-term mutation feature extraction sub-model and a long-term degradation feature extraction sub-model. The former is used to capture transient anomalies such as leakage, impact, and fatigue crack initiation, while the latter is used to identify corrosion deepening, wall thickness reduction, and slow material fatigue degradation processes.

6. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 1, characterized in that, The pipeline health index generation module calculates the degree of deviation between the current time-series characteristics and the reference health status, and generates a health index based on the deviation magnitude, deviation direction and continuity. This index is used to quantify the pipeline health status and serve as the basic input for the life prediction model.

7. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 1, characterized in that, The lifespan prediction module fits the changing trend of the health index based on a nonlinear degradation model and adopts a dynamic parameter update strategy to continuously correct the coefficients of the degradation model based on newly collected monitoring data, thereby achieving adaptive adjustment of the remaining lifespan prediction.

8. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 1, characterized in that, The early warning and visualization module sets up multi-level early warning thresholds, which are determined based on remaining lifespan, rate of change of health index, and abnormal diagnosis results, in order to achieve early medium-level early warning, trend early warning, and high-risk early warning before failure.

9. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 8, characterized in that, The maintenance suggestions provided by the early warning and visualization module include: recommended maintenance time windows, influencing factor analysis, pipeline segment priority ranking, and maintenance plan recommendations, enabling maintenance personnel to formulate refined maintenance strategies based on the actual condition of the pipeline.

10. The intelligent pipeline integrity diagnosis and life prediction system based on time-series modeling according to claim 1, characterized in that, The system further includes a model self-learning module, which is used to periodically update the time series feature model and life prediction model based on newly input historical monitoring data, on-site maintenance records and failure samples, so as to achieve self-evolutionary performance improvement of the entire system.

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