A method and system for analyzing pipeline defects based on corrosion mechanisms

Abstract

This invention discloses a pipeline defect analysis method and system based on corrosion mechanisms, relating to the fields of pipeline corrosion prediction and intelligent monitoring. The method first acquires pipeline corrosion data through multi-source corrosion data acquisition and spatiotemporal alignment. Then, it achieves feature representation based on multi-scale reconstruction of pipeline corrosion feature tokens. Spatial correlation is established using a windowed self-attention mechanism and periodic window translation interaction. Mechanism parameter interaction is achieved through decoupling embedding of pipeline corrosion mechanism parameters and axial selective attention. State modeling is performed using selective state space propagation driven by corrosion mechanism parameters, and on-chip memory optimization and parallel recursive evolution are implemented for pipeline corrosion state sequences. Finally, prediction results are output through cross-timescale corrosion prediction and uncertainty quantification, and the system achieves adaptive optimization based on a closed-loop optimization of the detection strategy using uncertainty feedback. This invention achieves high accuracy, high efficiency, and adaptive optimization in pipeline corrosion prediction.

Description

Technical Field

[0001] This invention relates to the field of pipeline corrosion prediction and intelligent monitoring technology, specifically a pipeline defect analysis method and system based on corrosion mechanisms. Background Technology

[0002] As a critical infrastructure for oil and gas transportation, pipelines endure multiple complex effects such as corrosion, fluid erosion, and mechanical stress over long periods. Accurate prediction and timely detection of pipeline corrosion defects are crucial for ensuring safe transportation. Traditional pipeline corrosion analysis methods primarily rely on periodic inspections and experience-based semi-quantitative assessments, making it difficult to achieve precise modeling of complex corrosion mechanisms and dynamic prediction across multiple time scales.

[0003] Existing technologies assess pipeline corrosion status using single or limited monitoring methods such as ultrasonic testing and electrochemical sensing, and predict trends based on simplified corrosion models. However, these technologies have limitations, such as difficulty in effectively fusing multi-source heterogeneous data, lack of deep coupling between corrosion mechanisms and data-driven models, insufficient accuracy in long-term series predictions, and the inability of detection strategies to adaptively optimize based on prediction uncertainty. Specifically, when dealing with multi-dimensional corrosion mechanism parameters such as wall thickness measurement, electrochemical potential, fluid shear stress, and material stress, existing methods often employ simple feature splicing or weighted fusion, ignoring the inherent correlation and independence requirements between different physical quantities. In the time-series modeling of corrosion status, traditional recurrent neural networks and attention mechanisms struggle to simultaneously balance long-term memory dependency and computational efficiency, and lack explicit constraints on the physical mechanisms of corrosion. Regarding the application of prediction results, detection strategies are typically formulated based on fixed time intervals or threshold judgments, failing to fully utilize prediction uncertainty information for intelligent decision-making.

[0004] Therefore, there is an urgent need for a pipeline corrosion prediction and intelligent monitoring technology that can deeply integrate multi-source corrosion data, organically combine corrosion physical mechanisms with data-driven modeling, achieve efficient long-sequence state propagation, and possess closed-loop adaptive optimization capabilities. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and propose a pipeline defect analysis method and system based on corrosion mechanism to solve the above-mentioned problems.

[0006] The objective of this invention is achieved through the following technical solution: a pipeline defect analysis method and system based on corrosion mechanism, comprising the following steps:

[0007] S1. Multi-source corrosion data acquisition and spatiotemporal alignment: Acquire multi-source corrosion data of pipelines, and perform spatiotemporal alignment and preprocessing of multimodal data;

[0008] S2. Multi-scale reconstruction based on pipeline corrosion feature tokens: Multi-source data are organized into a corrosion feature token matrix according to the axial position and radial depth of the pipeline. Each token in the corrosion feature token matrix contains corrosion mechanism parameters corresponding to the spatial position. The high-resolution pipeline corrosion field is mapped to the low-resolution feature space through hierarchical downsampling to capture the macroscopic corrosion evolution trend. Then, the original pipeline spatial resolution is restored through upsampling. Finally, the pipeline corrosion feature representations of different spatial scales are fused through cross-scale residual connection.

[0009] S3. Windowed self-attention mechanism and periodic window translation interaction: The erosion state field is divided into local windows and self-attention calculation is performed within the windows. Cross-window information exchange is achieved through periodic window translation operation;

[0010] S4. Decoupling and Embedding of Pipeline Corrosion Mechanism Parameters and Axial Selective Attention: Different corrosion mechanism parameters, including corrosion depth field, electrochemical potential distribution, fluid shear stress field, and material stress tensor field, are arranged into a data matrix according to feature fields and mapped to independent high-dimensional embedding spaces. An axial attention mechanism is constructed in the dimensional direction of corrosion mechanism parameters to achieve selective information interaction across corrosion mechanism parameters.

[0011] S5. Selective state-space propagation driven by corrosion mechanism parameters: Construct a discrete-time state-space model, in which some elements of the state transition matrix are calculated from corrosion mechanism physical quantities such as the electrochemical corrosion rate constant of the pipeline material, the ambient pH value, and the fluid shear stress, while the remaining elements are dynamically generated by the current corrosion feature input. The retention and forgetting of historical corrosion state information are controlled by a selective gating mechanism driven by the corrosion evolution stage.

[0012] S6. On-chip memory optimization of parallel recursive evolution for pipeline corrosion state sequences: The recursive calculation of the state space model is reconstructed into a parallel scanning algorithm adapted to GPU on-chip SRAM storage, and parallel recursive processing is performed on the corrosion state sequences sampled along the pipeline length direction.

[0013] S7. Corrosion Prediction and Uncertainty Quantification Across Time Scales: Based on multi-scale mechanism state representation, output short-term local corrosion rate prediction and long-term global corrosion morphology evolution prediction, and calculate the uncertainty distribution of the prediction;

[0014] S8. Closed-loop optimization of detection strategy based on uncertainty feedback: Taking the spatial distribution of predicted uncertainty as the objective function, an adaptive detection strategy is generated and fed back to the feature modeling and state propagation process in steps S2-S5 to form a closed-loop adaptive system.

[0015] The multi-source corrosion data in step S1 includes data acquired through wall thickness ultrasonic arrays, electrochemical probes, fluid dynamics sensors, stress-strain sensors, and environmental parameter monitors. The data is aligned based on timestamps and spatial coordinates, and preprocessed with missing data completion and noise filtering.

[0016] In step S2, the corrosion feature tokens contain wall thickness, potential, and stress corrosion mechanism parameters corresponding to the spatial location. Layered downsampling adopts a stride convolution sampling strategy, while upsampling adopts a transposed convolution method.

[0017] In step S3, the size of the local window is 8×8 to 32×32 spatial units, the displacement of the window translation is 1 / 4 to 1 / 2 of the window size, and the translation period is 3 to 8 time steps.

[0018] In step S4, the Query, Key, and Value matrices of the axial attention mechanism are generated through linear transformation of the embedding vectors of different corrosion mechanism parameters.

[0019] The selective gating mechanism in step S5 includes forget gate, selection gate, and reset gate. The state transition matrix, input mapping matrix, and output mapping matrix are generated by mapping the current erosion features through a multilayer perceptron. The gating activation function is the sigmoid function.

[0020] In step S6, the parallel scanning algorithm adopts a prefix sum calculation mode, and completes the state recursion of T time steps in log(T) time complexity through a binary tree structure. The GPU shared memory allocation strategy is optimized by block division according to the state vector dimension.

[0021] In step S7, uncertainty quantification is achieved through Monte Carlo Dropout. The uncertainty of short-term and long-term predictions is kept consistent through time-scale-related covariance propagation. In step S8, the detection strategy optimization adopts a gradient-based optimization algorithm.

[0022] A pipeline defect analysis system based on corrosion mechanism includes:

[0023] The multimodal data acquisition module includes a high-speed acquisition card for acquiring ultrasonic array wall thickness data, a multi-channel potential difference acquisition device for acquiring electrochemical probe signals, a flow and pressure sensing interface for acquiring fluid parameters, and a strain gauge signal conditioner for acquiring stress and strain data. It is used to acquire and spatiotemporally align multi-source data related to pipeline corrosion through multiple sensors.

[0024] The pipeline corrosion feature token multi-scale reconstruction module is used to perform downsampling, feature aggregation, upsampling, and cross-scale residual connection operations based on corrosion feature tokens.

[0025] The window-based attention interaction module is used to realize local window self-attention calculation and periodic window translation interaction;

[0026] The pipeline corrosion mechanism parameter decoupling modeling module is used to perform independent embedding and axial selective attention interaction of different corrosion mechanism parameters;

[0027] The selective state propagation module is used to implement input-conditional state-space modeling and selective memory propagation.

[0028] The on-chip storage optimization module includes a GPU processor with a shared memory area of ​​not less than 48KB, used to perform parallel recursive state evolution calculations optimized by GPU on-chip SRAM;

[0029] The cross-scale prediction module is used to output corrosion evolution predictions and uncertainty quantifications across multiple time scales.

[0030] The detection strategy closed-loop optimization module based on uncertainty feedback is used to optimize the detection strategy based on the uncertainty distribution and feed it back to the feature modeling and state propagation process.

[0031] The multi-scale reconstruction module for pipeline corrosion feature tokens includes configurable downsampling rate and upsampling strategy. The window size and translation parameters of the windowed attention interaction module are adaptively adjusted according to the input data resolution. The objective function weights of the detection strategy closed-loop optimization module based on uncertainty feedback are dynamically configured according to the application scenario.

[0032] The beneficial effects of this invention are:

[0033] By organically combining the following technical features—multi-source corrosion data acquisition and spatiotemporal alignment, multi-scale reconstruction based on pipeline corrosion feature tokens, windowed self-attention mechanism and periodic window translation interaction, decoupled embedding of pipeline corrosion mechanism parameters and axial selective attention, selective state space propagation driven by corrosion mechanism parameters, on-chip memory optimization and parallel recursive evolution for pipeline corrosion state sequences, cross-timescale corrosion prediction and uncertainty quantification, and closed-loop optimization of detection strategies based on uncertainty feedback—the following significant beneficial effects are achieved:

[0034] First, by employing multi-source corrosion data acquisition and spatiotemporal alignment technology, unified acquisition and processing of data from multiple sources, including wall thickness ultrasonic arrays, electrochemical probes, fluid dynamics sensors, stress-strain sensors, and environmental parameter monitors, was achieved, significantly improving the comprehensiveness and accuracy of corrosion state monitoring. Spatiotemporal alignment of multimodal data based on timestamps and spatial coordinates resolved the inconsistencies in time and space between data from different sensors, laying a solid foundation for subsequent multi-scale feature modeling.

[0035] Secondly, a multi-scale reconstruction technique based on pipeline corrosion feature tokens is employed. This technique organizes multi-source data into a corrosion feature token matrix according to the pipeline's axial position and radial depth, and uses hierarchical downsampling, upsampling, and cross-scale residual connections to achieve a multi-scale feature representation of the pipeline corrosion field, from macroscopic trends to microscopic details. This multi-scale feature reconstruction method effectively captures the corrosion evolution characteristics at different spatial scales, overcoming the technical problem that traditional single-scale modeling methods struggle to simultaneously capture macroscopic trends and local details.

[0036] Furthermore, the windowed self-attention mechanism and the periodic window translation interaction technique significantly reduce computational complexity by dividing the erosion state field into local windows and performing self-attention calculations within each window. The periodic window translation operation enables cross-window information exchange, achieving effective flow of global spatial information while maintaining computational efficiency, thus solving the technical problem of high computational overhead in traditional global attention mechanisms.

[0037] Furthermore, the decoupled embedding of pipeline corrosion mechanism parameters and the axial selective attention technique map different corrosion mechanism parameters, such as corrosion depth field, electrochemical potential distribution, fluid shear stress field, and material stress tensor field, into independent high-dimensional embedding spaces, and achieve selective information interaction across corrosion mechanism parameters through the axial attention mechanism. This decoupled embedding method effectively maintains the physical semantic independence of each corrosion mechanism parameter, avoiding the problem of reduced feature representation ability caused by the mixing of different physical quantities.

[0038] Meanwhile, based on the selective state-space propagation technology driven by corrosion mechanism parameters, a hybrid driving model is constructed, in which some elements of the state transition matrix are calculated from corrosion mechanism physical quantities and others are dynamically generated from current corrosion feature inputs. This achieves a deep integration of physical mechanisms and data-driven approaches. The selective gating mechanism adaptively controls the retention and forgetting of historical state information according to the stage characteristics of corrosion evolution, effectively improving the accuracy of long-term series prediction.

[0039] A parallel recursive evolution technique optimized for on-chip memory in pipeline corrosion state sequences is proposed. This technique reconstructs the recursive computation of the state-space model into a parallel scanning algorithm adapted to GPU on-chip SRAM storage. By unfolding the intermediate states of all time steps in GPU shared memory, the performance bottleneck of frequent access to high-bandwidth external GPU memory is avoided. This parallel recursive processing method significantly improves the computational efficiency of long-term corrosion state evolution, fulfilling the engineering requirement of real-time prediction.

[0040] A cross-timescale corrosion prediction and uncertainty quantification technique is developed. Based on multi-scale mechanistic state representation, it outputs short-term local corrosion rate predictions and long-term global corrosion morphology evolution predictions, and quantifies the prediction uncertainty through Monte Carlo Dropout. The uncertainties of short-term and long-term predictions are kept consistent through time-scale-related covariance propagation, providing a reliable confidence assessment of the prediction results.

[0041] Finally, a closed-loop optimization technique for the detection strategy based on uncertainty feedback is employed. Using the spatial distribution of prediction uncertainty as the objective function, an adaptive detection strategy is generated and fed back to the feature modeling and state propagation processes, forming a closed-loop adaptive system that synergistically improves both prediction accuracy and detection efficiency. This closed-loop optimization mechanism significantly improves the utilization efficiency of detection resources, reduces detection costs, and ensures continuous improvement in the performance of the prediction system. Attached Figure Description

[0042] Figure 1 The process of this invention Figure 1 ;

[0043] Figure 2 The process of this invention Figure 2 ;

[0044] Figure 3 This is a system architecture diagram of the present invention. Detailed Implementation

[0045] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be noted that the directional concepts of "left", "right", "up", "down", "front", "back", "inner", and "outer" in the following scheme are all relative directions, and will not be listed one by one here.

[0047] Example 1: A Pipeline Corrosion Prediction Method Based on Multi-Source, Multi-Scale Mechanism Modeling

[0048] like Figure 1 As shown in the figure, this embodiment describes in detail a multi-scale feature modeling implementation method in a pipeline corrosion prediction method based on multi-source multi-scale mechanism modeling and selective state propagation. This implementation method achieves accurate prediction and analysis of the corrosion evolution trend of large and long-distance pipelines through multi-source corrosion data acquisition and spatiotemporal alignment, multi-scale reconstruction based on pipeline corrosion feature tokens, and windowed self-attention mechanism and periodic window translation interaction.

[0049] This embodiment takes a pipeline system as an example, whose operating environment includes complex corrosive factors such as corrosive media and internal oil fluids. The entire prediction method implementation process includes key steps such as multi-source corrosion data acquisition and spatiotemporal alignment, multi-scale reconstruction based on pipeline corrosion feature tokens, windowed self-attention mechanism and periodic window translation interaction.

[0050] In the multi-source corrosion data acquisition and spatiotemporal alignment phase, multi-source corrosion data for pipelines is acquired through data obtained via ultrasonic wall thickness arrays, electrochemical probes, fluid dynamics sensors, stress-strain sensors, and environmental parameter monitors. Specifically, ultrasonic wall thickness arrays deployed every 10 meters along the pipeline length acquire pipeline wall thickness distribution data. Each sensor array contains 64 ultrasonic transducers, operates at a frequency of 5 MHz, and achieves a sampling depth resolution of 0.1 mm, enabling precise detection of corrosion depth changes on the inner and outer walls of the pipeline. Simultaneously, an electrochemical probe network deployed every 50 meters acquires electrochemical potential distribution data on the pipeline surface. Each probe can measure the potential value relative to a saturated calomel electrode with a measurement accuracy of ±1 mV and a measurement range of -2000 mV to +2000 mV. Fluid dynamics sensor arrays, including flow sensors, pressure sensors, and temperature sensors, are deployed at key locations inside the pipeline to monitor the velocity, pressure, and temperature fields of the fluid inside the pipeline. The flow sensor accuracy is ±0.5%, the pressure sensor accuracy is ±0.1% of full scale, and the temperature sensor accuracy is ±0.1℃. The stress-strain sensor chain adopts fiber optic grating technology and is distributed along the circumferential and axial direction of the pipe surface. A set of sensors is set every 5 meters, and each set contains 4 strain measurement points. The measurement accuracy reaches ±1 micro-strain, which can monitor the stress-strain state changes of the pipe structure in real time.

[0051] Multimodal data undergoes spatiotemporal alignment and preprocessing. Specifically, alignment is performed based on timestamps and spatial coordinates, followed by missing value completion and noise filtering preprocessing. The data acquisition system employs a distributed architecture, with each sensor node transmitting data to the central processing node. In the data preprocessing stage, multi-source data is spatiotemporally aligned based on timestamps and spatial coordinates. For missing data, an interpolation algorithm based on spatiotemporal correlation is used to complete the missing values. Adaptive filters are employed to remove sensor measurement noise during noise filtering.

[0052] In the multi-scale reconstruction stage based on pipeline corrosion feature tokens, multi-source data is organized into a corrosion feature token matrix according to the pipeline's axial position and radial depth. Each token in the corrosion feature token matrix contains corrosion mechanism parameters corresponding to its spatial location. Specifically, the corrosion feature tokens contain corrosion mechanism parameters such as wall thickness, potential, and stress corresponding to their spatial locations, forming a 64-dimensional feature vector. The entire pipeline's corrosion feature token matrix is ​​constructed as a 1024×8×64 three-dimensional tensor, where 1024 represents the number of axial positions, 8 represents the number of radial depth layers, and 64 represents the feature dimension of each token.

[0053] Hierarchical downsampling maps high-resolution pipeline corrosion fields to a low-resolution feature space to capture macroscopic corrosion evolution trends. The hierarchical downsampling employs a stride convolutional sampling strategy, using a 3×3 convolution kernel with a stride of 2, progressively reducing the spatial resolution layer by layer, while increasing the number of feature channels in each downsampling layer to maintain feature expressiveness.

[0054] Within the low-resolution feature space, local window feature aggregation is performed to extract corrosion correlation information between local regions of the pipeline. For each downsampling layer, the feature map is divided into overlapping local windows, with a window size of 16×16 spatial units and an overlap stride of 8 units, ensuring a 50% overlap between adjacent windows. Within each local window, depthwise separable convolution is used for feature aggregation. Specifically, channel-wise convolution is first used to extract spatial features independently for each feature channel, and then pointwise convolution is used for cross-channel feature fusion. The calculation formula for local window feature aggregation is as follows:

[0055] ;

[0056] in, This represents the aggregated feature at position (i,j). Let represent the input feature value of the c-th feature channel at position (i,j). This represents the channel-wise convolution kernel weights of the c-th channel. Let W represent the pointwise convolution weight of the c-th channel, W represent the local window region, and C represent the total number of feature channels. This represents the ReLU activation function.

[0057] After local window feature aggregation, upsampling is performed to restore the original pipeline spatial resolution. Upsampling uses transposed convolution, corresponding to the downsampling process, upsampling 128×1, 256×2, and 512×4 feature maps to spatial resolutions of 256×2, 512×4, and 1024×8 respectively. Each transposed convolution layer uses a 3×3 kernel with a stride of 2, padding of 1, and output padding of 1 to ensure accurate restoration of the spatial dimensions after upsampling. The formula for calculating transposed convolution is:

[0058]

[0059] in, This represents the feature value at position (i,j) after upsampling. This represents the feature value at position (i,j) in the downsampled feature map. The weights of the transposed convolution kernel are represented by , and s represents the stride of the transposed convolution. This indicates a round-down operation.

[0060] To maintain consistency in the representation of pipeline corrosion features across different spatial scales, a cross-scale residual connection fusion mechanism is employed. Specifically, the features obtained from upsampling at each layer are residually connected with the features from the corresponding downsampling layer, and the feature information from different scales is integrated through weighted fusion. The calculation formula for the cross-scale residual connection is as follows: ;

[0061] in, Indicates the characteristics after fusion. Indicates upsampling features, The same-scale feature representing skip connections. and These are the weight parameters for adaptive learning, satisfying... and The weight parameters are learned automatically through an attention mechanism. The formula for calculating the attention weights is:

[0062] ;in, and It is a learnable weight matrix. This indicates a feature splicing operation.

[0063] In the interaction phase between the windowed self-attention mechanism and the periodic window translation, the erosion state field is divided into local windows, and self-attention calculations are performed within each window. The size of the local window ranges from 8×8 to 32×32 spatial units. Within each local window, self-attention calculations are performed to model the erosion correlations between different locations within the window. The self-attention mechanism maps the input features to different representation spaces through three linear transformations: Query, Key, and Value. Then, attention weights are calculated and weighted sums are performed to obtain the output features. The formula for calculating self-attention within a window is: ;

[0064] in, These represent the Query, Key, and Value matrices, respectively, and X represents the input feature matrix within the window. They are learnable linear transformation weights. This represents the dimension of the Key vector, used to scale the attention score to avoid the vanishing gradient problem.

[0065] Cross-window information exchange is achieved through periodic window translation operations. The translation amount is 1 / 4 to 1 / 2 of the window size, and the translation period is 3 to 8 time steps. This translation operation allows adjacent regions that were originally located on different window boundaries to be located within the same window after translation, thereby establishing a cross-window information exchange channel through a self-attention mechanism within the window. The mathematical representation of window translation is:

[0066] ;

[0067] in, This indicates the window position at time step t. P represents the original window position, P represents the translation period, and D represents the translation amount.

[0068] Multi-head self-attention enhances the model's expressive power by computing multiple attention heads in parallel. Each attention head focuses on a different feature subspace, and the outputs of all attention heads are then concatenated and linearly transformed to obtain the final output. The formula for calculating multi-head self-attention is:

[0069] ;

[0070] ;

[0071] Where h represents the number of attention heads, set to 8. This represents the Query, Key, and Value weight matrix for the i-th attention head. This indicates the output linear transformation weight matrix.

[0072] Work process

[0073] The entire multi-scale feature modeling process unfolds in a time sequence, with each processing cycle comprising four main stages: data acquisition, feature tokenization, multi-scale reconstruction, and attention computation.

[0074] During the data acquisition phase, a distributed sensor network synchronously acquires multi-source corrosion data at a frequency of 100Hz. The data is transmitted in real time to the central processing node via wireless and wired communication networks. The data preprocessing system performs quality checks, spatiotemporal alignment, missing value completion, and noise filtering on the received raw data to ensure that the data used in subsequent processing has good quality and consistency.

[0075] In the feature tokenization stage, the preprocessed multi-source data is organized according to a predefined spatial grid, with each grid point corresponding to a corrosion feature token. The tokenization process includes three sub-steps: spatial interpolation, feature normalization, and dimensionality organization. Spatial interpolation interpolates measurement data from different sensors onto a unified spatial grid, feature normalization unifies the numerical range of different physical quantities to the [0,1] interval, and dimensionality organization combines various corrosion mechanism parameters into a feature vector of fixed dimensions.

[0076] In the multi-scale reconstruction stage, tokenized erosion features are used to extract feature representations at different scales through a hierarchical downsampling network. The downsampling process starts at the highest resolution, progressively reducing the spatial resolution and increasing the number of feature channels layer by layer. Each downsampling layer retains feature information from the current scale for subsequent cross-scale fusion. Local window feature aggregation is performed in parallel at each scale to extract spatial correlation information of local regions. The upsampling process restores low-resolution features to the original resolution layer by layer, while simultaneously fusing feature information from different scales through cross-scale residual connections, ultimately obtaining a comprehensive feature representation containing multi-scale information.

[0077] During the attention computation phase, the fused multi-scale features are spatially correlated and modeled using a windowed self-attention mechanism. Window partitioning divides the entire feature map into several non-overlapping local regions, with multi-head self-attention computation performed independently within each window. Periodic window translation operations ensure that information exchange channels can be established between adjacent windows, avoiding the negative impact of window boundary effects on feature learning. The results of attention computation provide feature representations for each spatial location that consider global contextual information, enabling these features to better reflect the spatial distribution patterns and evolution trends of pipeline corrosion.

[0078] The entire process employs a pipelined parallel processing architecture, allowing data acquisition, feature extraction, multi-scale reconstruction, and attention computation to be executed in parallel on different processing units, significantly improving the system's real-time processing capabilities. The system also features adaptive load balancing, dynamically adjusting the workload allocation among processing units based on computational load to ensure stable processing performance even under high load conditions.

[0079] Multi-source data fusion significantly improves the accuracy and reliability of corrosion prediction. By simultaneously collecting and fusing multiple types of corrosion-related data, such as wall thickness, electrochemistry, fluid dynamics, and stress-strain, the system can comprehensively capture various physical and chemical factors affecting pipeline corrosion. Compared to prediction methods based on a single data source, the prediction accuracy is improved by more than 25%. The complementarity of multi-source data also enhances the system's robustness; when one type of sensor malfunctions or its data quality deteriorates, data from other types of sensors can still maintain the system's basic predictive function.

[0080] Tokenization of corrosion features provides a unified and efficient mathematical framework for describing complex multiphysics corrosion phenomena. Organizing the spatial distribution information of different physical quantities into a token matrix not only preserves the spatial structure of the data but also facilitates subsequent processing by deep learning algorithms. The dimensional design of the tokenization representation fully considers the physical characteristics of pipeline corrosion, ensuring the integrity of feature representation while keeping computational complexity within an acceptable range.

[0081] The multi-scale reconstruction mechanism effectively solves the technical challenge of balancing long-range dependence and local details in pipeline corrosion prediction. Through a U-shaped network structure of hierarchical downsampling and upsampling, the system can simultaneously capture the overall macroscopic corrosion trend of the pipeline and the microscopic corrosion details of local areas. Cross-scale residual connections ensure the effective fusion of feature information at different scales, avoiding the problem in traditional methods where excessive focus on one scale leads to the loss of important information at other scales. Experimental results show that compared to single-scale methods, the multi-scale reconstruction method improves the accuracy of long-term corrosion trend prediction by 15% and the recall rate of local corrosion detection by 20%.

[0082] Windowed self-attention mechanisms significantly reduce the computational complexity of global attention while maintaining good feature learning performance. The computational complexity of traditional global self-attention mechanisms is... Where N is the sequence length, the computational cost is too high for large-scale pipeline systems. This embodiment uses windowed self-attention to reduce the complexity to [missing value]. Where W is the window size, which is typically much smaller than N, thus significantly improving computational efficiency. The periodic window translation operation cleverly solves the information isolation problem caused by window segmentation, ensuring that global information can still be effectively propagated.

[0083] The system boasts excellent scalability and practicality. Its modular design allows for flexible adaptation to pipeline systems of varying sizes and types; adjustments to sensor configurations, tokenization parameters, and network structure can make it suitable for different application scenarios. The system supports incremental deployment, enabling gradual expansion from small-scale pilot projects to full system coverage, reducing the technical risks and economic costs of engineering implementation.

[0084] The real-time processing capabilities meet the stringent requirements of industrial applications. Optimized through GPU parallel computing, pipelined processing, and load balancing, the system can complete a full feature extraction and prediction calculation within 100 milliseconds, fulfilling the needs of real-time monitoring and early warning. The system also features historical data backtracking analysis, enabling rapid processing of large amounts of historical data to uncover long-term corrosion patterns and anomalies.

[0085] The technical solution in this embodiment has been verified through actual engineering. It operated continuously in a pipeline system for six months, processing over 100TB of data, achieving a prediction accuracy of 92% and a false alarm rate below 5%, providing reliable technical support for pipeline safety operation and maintenance decisions. Compared to traditional periodic inspection methods, this system can detect potential corrosion risks 2-3 months in advance, gaining valuable time for pipeline maintenance.

[0086] Example 2:

[0087] like Figure 1 and Figure 2 As shown in the figure, this embodiment describes in detail a selective state propagation implementation method in a pipeline corrosion prediction method based on multi-source multi-scale mechanism modeling and selective state propagation. Based on the multi-scale feature modeling described in Embodiment 1, this embodiment achieves real-time evolution prediction and long-term sequence modeling of pipeline corrosion state under complex working conditions by decoupling and embedding pipeline corrosion mechanism parameters and axial selective attention, selective state space propagation driven by corrosion mechanism parameters, and on-chip memory optimization parallel recursive evolution for pipeline corrosion state sequences.

[0088] This embodiment takes a natural gas pipeline system as an example, and the transport medium is natural gas containing hydrogen sulfide. Based on the multi-source corrosion data acquisition and multi-scale feature reconstruction results described in Embodiment 1, this embodiment focuses on realizing the deep decoupling modeling of corrosion mechanism parameters and the intelligent propagation mechanism of state space.

[0089] In the decoupling and axial selective attention stage of pipeline corrosion mechanism parameters, different corrosion mechanism parameters, including corrosion depth field, electrochemical potential distribution, fluid shear stress field, and material stress tensor field, are arranged into a data matrix according to their characteristic fields and mapped to independent high-dimensional embedding spaces. Based on the corrosion characteristics of subsea pipelines, the corrosion mechanism parameters are divided into four main categories: corrosion depth field, electrochemical potential distribution, fluid shear stress field, and material stress tensor field. The corrosion depth field reflects the spatiotemporal variation of the pipeline wall thickness, the electrochemical potential distribution describes the electrochemical corrosion activity of the pipeline surface, the fluid shear stress field characterizes the scouring effect of the internal fluid on the pipe wall, and the material stress tensor field describes the stress state of the pipeline structure.

[0090] Each corrosion mechanism parameter is mapped to a 128-dimensional high-dimensional embedding space through an independent embedding network, maintaining the physical semantic independence of each parameter. The embedding vectors for the four corrosion mechanism parameters are denoted as follows: These constitute the overall corrosion mechanism parameter embedding matrix. .

[0091] An axial attention mechanism is constructed along the corrosion mechanism parameter dimension to achieve selective information interaction across corrosion mechanism parameters. The core idea of ​​the axial attention mechanism is to calculate attention weights along the corrosion mechanism parameter dimension, enabling the model to automatically learn the interaction relationships between different corrosion mechanisms. Specifically, the embedding matrix... The Query matrix is ​​obtained by linear transformation. Key matrix Value matrix The formula for calculating axial attention is:

[0092] ;

[0093] ;

[0094] in, It is a learnable linear transformation weight matrix. It is the dimension of the key vector in the attention mechanism. It is the output of axial attention. The axial attention mechanism can capture the synergistic effects between different corrosion mechanisms, such as the coupling effect of electrochemical corrosion and stress corrosion, the accelerating effect of fluid erosion on electrochemical corrosion, and other complex physicochemical processes.

[0095] In the selective state-space propagation stage driven by corrosion mechanism parameters, a discrete-time state-space model is constructed to describe the temporal evolution of pipeline corrosion state. The mathematical form of the state-space model is:

[0096] ; .

[0097] in, This represents the hidden state vector at time t. This represents the input feature vector at time t. This represents the output prediction vector at time t. These are the state transition matrix, input mapping matrix, and output mapping matrix, respectively.

[0098] State transition matrix The state transition matrix is ​​constructed by calculating some elements from corrosion mechanism physical quantities such as the electrochemical corrosion rate constant of the pipeline material, the ambient pH value, and the fluid shear stress, while the remaining elements are dynamically generated from the current corrosion characteristics. Specifically, the state transition matrix can be decomposed into a physical driving part. and data-driven part :

[0099] ;

[0100] in, These are balancing parameters used to control the relative weights of physical mechanisms and data-driven aspects. (Physics-driven part) Constructed based on corrosion electrochemistry theory, wherein the electrochemical corrosion rate constant Calculated according to Tafel's equation: ;

[0101] in, It is the standard reaction rate constant. It is the Tafel slope, and F is the Faraday constant. It is an overpotential, R is the gas constant, and T is the absolute temperature. Fluid shear stress. The effect on the corrosion rate is described by an empirical formula: ;

[0102] in, and These are material-related constants obtained by fitting experimental data. This represents the multiplication factor of shear stress on corrosion rate.

[0103] Data-driven part Learn from the current input features using a multilayer perceptron:

[0104]

[0105] MLP is a multilayer perceptron with three hidden layers, containing 1024, 512, and 512 neurons respectively, and an output layer dimension of [missing information]. To meet the dimensional requirements of the matching state transition matrix.

[0106] The selective gating mechanism includes a forget gate, a selection gate, and a reset gate. The state transition matrix, input mapping matrix, and output mapping matrix are generated by mapping the current erosion features through a multilayer perceptron. The sigmoid function is used as the gating activation function. The gating mechanism includes a forget gate. Select door and reset door Their calculation formulas are: ; ;

[0107] ;in, It is a weight matrix. It is a bias vector. It is the sigmoid activation function. It is a physical constraint vector extracted from corrosion mechanism parameters. This represents a vector concatenation operation. Physical constraint vectors. It includes key corrosion mechanism indicators at the current moment, such as corrosion rate, stress level, and environmental corrosivity, to guide the decision-making process of the gating mechanism.

[0108] The selective state update process driven by corrosion evolution stages combines traditional gated updates with corrosion physics constraints:

[0109] ;

[0110] in, This represents element-wise multiplication. These are the weighting coefficients of the physical constraints. It is a constraint function based on the physical laws of corrosion, ensuring that the state evolution conforms to the basic physical laws of the corrosion process.

[0111] In the on-chip memory optimization of the parallel recursive evolution stage for pipeline corrosion state sequences, the recursive computation of the state space model is reconstructed into a parallel scanning algorithm adapted to GPU on-chip SRAM storage. Traditional recursive computation has severe sequence dependencies and cannot fully utilize the parallel computing capabilities of the GPU. Especially when dealing with long-term corrosion state evolution sequences, computational efficiency becomes the bottleneck of system performance.

[0112] The parallel scan algorithm employs a prefix sum computation mode, transforming the recursive relation into a prefix operation that can be computed in parallel. For the state-space model... This can be computed in parallel using prefix sums in matrix form. Specifically, we define the composite transformation. Then the calculation of the state sequence can be expressed as: ;

[0113] in, This represents the composition operation of composite transformations. Parallel computation of prefix sums is implemented using a binary tree structure, reducing the time complexity from the original... Reduce to , where T is the length of the time series.

[0114] The specific implementation process of parallel scanning of binary trees is as follows: First, the time series of length T is decomposed into... The binary tree is layered, and each layer performs a parallel pairwise merge operation. During the upsweep phase, adjacent transform pairs are merged from the leaf nodes towards the root node: ;

[0115] in, This indicates that the k-th layer starts from position i and has a length of... The interval transformation, the up-scan process requires The process is completed in one step. In the next scan phase, the accumulated results are propagated from the root node to the leaf nodes, eventually yielding all prefix sum results.

[0116] The GPU shared memory allocation strategy is optimized by partitioning the state vector dimension, fully utilizing the GPU's storage hierarchy. Specifically, the 512-dimensional state vector is decomposed into eight 64-dimensional sub-blocks, each allocated 6KB of shared memory space, using a total of 48KB of on-chip SRAM. This partitioning strategy ensures that all intermediate computation results are stored in high-speed shared memory, avoiding performance penalties caused by frequent access to global memory. The memory access mode for block-parallel computation is carefully designed to ensure no memory access conflicts between different thread blocks, while maximizing memory bandwidth utilization.

[0117] For the corrosion state sequence sampled along the pipeline length, a spatial block parallel strategy is adopted. The 50-kilometer pipeline is divided into 100 spatial segments, each 500 meters long. The corrosion state evolution of each segment can be processed in parallel in different GPU pipelines. Boundary information between spatial segments is exchanged through a dedicated boundary synchronization mechanism to ensure the continuity and consistency of the overall corrosion state evolution.

[0118] Work process

[0119] The selective state propagation process adopts a multi-stage pipeline architecture, which includes five main stages: corrosion mechanism parameter decoupling, axial attention calculation, state space modeling, selective gating, and parallel recursive evolution.

[0120] In the corrosion mechanism parameter decoupling stage, the system receives multi-scale corrosion features from Example 1 and decomposes the fused features into independent representations of four basic corrosion mechanism parameters through a feature separation network. The feature separation network employs gated linear units and attention pooling mechanisms, effectively extracting the feature components of each corrosion mechanism while suppressing interference information from other mechanisms. The separated corrosion mechanism parameters are mapped to a unified high-dimensional representation space through independent embedding networks, preparing for subsequent axial attention calculations.

[0121] In the axial attention calculation phase, the system calculates attention weights along the corrosion mechanism parameter dimensions to learn the interaction patterns between different corrosion mechanisms. The calculation process employs a parallel multi-head attention architecture, with each attention head focusing on different mechanism interaction patterns. For example, one attention head specifically studies the coupling relationship between electrochemical corrosion and stress corrosion, while another attention head focuses on the influence of fluid erosion on various corrosion mechanisms. The results of multi-head attention are weighted and fused to obtain a comprehensive corrosion mechanism interaction feature, which includes the synergistic and competitive relationships between various mechanisms in complex corrosion processes.

[0122] In the state-space modeling phase, the system constructs a hybrid-driven state transition matrix, organically combining corrosion physics mechanisms and data learning. The physics mechanism component is based on corrosion electrochemistry, materials mechanics, and fluid mechanics theories, ensuring that state evolution conforms to fundamental physical laws. The data-driven component learns complex nonlinear relationships from historical data through deep neural networks, supplementing complex factors that the physical model cannot accurately describe. The dynamic balance between the two components is automatically adjusted through reinforcement learning algorithms, allowing the system to automatically adjust the weight ratio of physics-driven and data-driven approaches based on current prediction accuracy and physical consistency.

[0123] During the selective gating phase, the system dynamically controls the retention and forgetting of historical state information based on the stage-specific characteristics of corrosion evolution. The decision-making process of the gating mechanism comprehensively considers current input characteristics, historical state information, and corrosion physical constraints, enabling it to identify key events and turning points in the corrosion process. For example, when a sudden increase in the corrosion rate is detected, the forgetting gate reduces the forgetting weight, retaining more historical information to analyze the causes of accelerated corrosion; when the corrosion process enters a stable phase, the selective gate enhances the selectivity for new information, focusing on new factors that may lead to state changes.

[0124] During the parallel recursive evolution phase, the system employs a GPU cluster for large-scale parallel computation to achieve efficient evolution of long-term erosion states. The computational task is decomposed in two dimensions: time and space. The time dimension is implemented using a prefix sum algorithm. The system employs parallel recursion, utilizing domain decomposition to achieve multi-segment parallel processing in the spatial dimension. Data exchange within the GPU cluster is facilitated through a high-speed interconnect network, ensuring the efficiency and accuracy of parallel computing. Furthermore, the system implements a dynamic load balancing mechanism, which dynamically adjusts GPU resource allocation based on the computational complexity of different spatial segments, preventing computational bottlenecks.

[0125] The entire workflow employs asynchronous pipeline scheduling, allowing computations at each stage to overlap and significantly improving the overall system throughput. The system also features fault tolerance and recovery mechanisms; when a computing node fails, tasks can be quickly migrated to other nodes to continue execution, ensuring system reliability and robustness.

[0126] The decoupling and embedding of corrosion mechanism parameters significantly improves the modeling accuracy of multiphysics corrosion processes. By decomposing complex corrosion phenomena into basic physicochemical processes, the system can more accurately capture the independent contributions and interactions of different corrosion mechanisms. The axial selective attention mechanism enables the model to automatically learn the complex relationships between corrosion mechanisms, improving accuracy by more than 30% in predicting multi-mechanism coupled corrosion compared to traditional linear superposition methods. In particular, for the synergistic effect of electrochemical corrosion and stress corrosion commonly found in subsea pipeline systems, the system can accurately identify and quantify this coupling effect, providing important basis for the formulation of corrosion protection strategies.

[0127] The state-space model driven by corrosion mechanism parameters achieves a deep integration of physical mechanisms and data-driven approaches. The hybrid-driven state transition matrix ensures the physical rationality of the corrosion evolution process while also possessing the ability to handle complex nonlinear relationships. The physical-driven part ensures that the prediction results conform to the fundamental laws of corrosion electrochemistry and materials mechanics, while the data-driven part can capture various complex factors and random disturbances in actual engineering. This fusion strategy gives the model better generalization ability when facing new operating conditions, achieving a performance improvement of over 40% in small-sample prediction tasks compared to purely data-driven methods.

[0128] The selective gating mechanism effectively solves the memory capacity and selectivity problems in long-term corrosion prediction. Traditional recurrent neural networks are prone to gradient vanishing and information loss when processing long sequences, while the selective gating mechanism in this embodiment can intelligently retain key information and forget irrelevant noise based on the stage characteristics of corrosion evolution. Experimental results show that in modeling corrosion history data of up to 10 years, the system can accurately identify and retain key historical events affecting the current corrosion state, with a key event retention rate of over 95%, while reducing noise interference to below 5%, significantly improving the stability and reliability of long-term prediction.

[0129] The GPU-on-chip memory-optimized parallel recursive algorithm significantly improves computational efficiency. Through a prefix sum parallel scan algorithm, the system reduces the original O(T) sequence computation complexity to [missing value]. When processing corrosion state evolution over 1000 time steps, the computation time was reduced from 10 seconds to 100 milliseconds using traditional methods, achieving a 100-fold efficiency improvement. The GPU shared memory block optimization strategy reduced memory access latency by 80%, enabling the system to process high-frequency corrosion monitoring data in real time. This computational optimization provides the technological foundation for real-time corrosion early warning and online decision support.

[0130] The system boasts excellent scalability and adaptability. Its modular state-space design allows for flexible adaptation to different types of piping systems and corrosive environments; by adjusting the types and number of corrosion mechanism parameters, it can be suited to various application scenarios. The hybrid-driven state transition matrix can be customized according to specific piping materials and environmental conditions. Physical parameters can be set based on material manuals and environmental test data, while data-driven parameters are continuously optimized through online learning.

[0131] Practical engineering applications have validated the system's practical value. The technical solution of this embodiment has been successfully applied in a deep-sea natural gas pipeline system, operating continuously for eight months, processing over 50TB of corrosion monitoring data, successfully predicting three potential corrosion failure events with a prediction accuracy of 94% and a false alarm rate below 3%. The system detected corrosion risk areas 4-6 months in advance, providing ample preparation time for pipeline maintenance and repair, and avoiding potential environmental pollution and economic losses. Compared to traditional periodic inspection methods, this system reduces inspection costs by 60% while improving the temporal resolution of corrosion monitoring from quarterly to hourly levels, significantly enhancing the proactiveness and accuracy of pipeline safety management.

[0132] Long-term operational data of the system show that the selective state propagation method can effectively adapt to seasonal changes in the marine environment, adjustments to pipeline operating parameters, and updates to corrosion protection measures. The performance of the prediction model continues to improve with the extension of operating time, demonstrating good adaptive learning ability and engineering practicality.

[0133] Example 3:

[0134] like Figures 1 to 3 As shown in the figure, this embodiment describes in detail a closed-loop detection optimization implementation method in a pipeline corrosion prediction method based on multi-source multi-scale mechanism modeling and selective state propagation. Based on the multi-scale feature modeling described in Embodiment 1 and the selective state propagation described in Embodiment 2, this implementation method achieves intelligent allocation of maintenance optimization and detection resources for aging pipelines through cross-timescale corrosion prediction and uncertainty quantification, and closed-loop optimization of detection strategies based on uncertainty feedback.

[0135] This embodiment takes a long-distance pipeline system that has been in service for many years as an example. Having undergone complex changes in the corrosive environment and multiple maintenance and renovations, the corrosion state of different sections of the pipeline varies significantly, requiring differentiated detection strategies and maintenance plans. Based on the multi-scale feature modeling of Embodiment 1 and the selective state propagation results of Embodiment 2, this embodiment focuses on achieving accurate quantification of prediction uncertainty and closed-loop optimization of the detection strategy.

[0136] In the cross-timescale corrosion prediction and uncertainty quantification stage, based on multi-scale mechanism state representation, short-term local corrosion rate predictions and long-term global corrosion morphology evolution predictions are output, and the uncertainty distribution of the predictions is calculated. Short-term predictions cover a time range of 1 hour to 1 week, mainly targeting the instantaneous changes and short-term fluctuations in local corrosion rates, with a prediction time resolution of 1 hour and a spatial resolution of 10 meters of pipe segment. Medium-term predictions cover a time range of 1 week to 3 months, focusing on the development and expansion of regional corrosion patterns, with a prediction time resolution of 1 day and a spatial resolution of 100 meters of pipe segment. Long-term predictions cover a time range of 3 months to 2 years, analyzing the evolution trend of the overall corrosion morphology and the spatial migration of corrosion risk, with a prediction time resolution of 1 week and a spatial resolution of 1 kilometer of pipe segment.

[0137] The multi-timescale prediction is implemented using a hierarchical prediction network architecture, with each timescale corresponding to a dedicated prediction branch. The short-term prediction branch is based on high-frequency state features and employs a temporal convolutional network structure to capture rapidly changing erosion dynamics; the medium-term prediction branch is based on mid-frequency state features and employs a recurrent neural network structure to model periodic erosion patterns; the long-term prediction branch is based on low-frequency state features and employs an attention mechanism to focus on key inflection points in long-term trends. The three prediction branches exchange information through a feature-sharing layer, ensuring consistency and complementarity between predictions at different timescales.

[0138] Uncertainty quantification is achieved through Monte Carlo Dropout, a method that estimates the cognitive uncertainty of the model by maintaining the randomness of the Dropout layer during inference. The formula for calculating the uncertainty is:

[0139] ; ; ;

[0140] in, The variance of the predicted values ​​reflects cognitive uncertainty. The mean of the model output variance represents the random uncertainty. This represents the total uncertainty, where N=100 is the number of Monte Carlo samplings. It is the predicted value of the i-th sample. It is the mean of the predicted values. It is the variance of the model output at the i-th sampling.

[0141] Uncertainty in short-term and long-term forecasts remains consistent through time-scale-related covariance propagation. The mathematical model of covariance propagation is based on the propagation patterns of uncertainty in time and space dimensions:

[0142]

[0143]

[0144] in, This represents the uncertainty covariance matrix at time t. The Jacobian matrix represents the state transition. Represents the process noise covariance matrix. Indicates the time of short-term forecast and long-term prediction time Cross covariance between It is a time-scale coupling function used to describe the correlation between predictions at different time scales.

[0145] In the closed-loop optimization stage of the detection strategy based on uncertainty feedback, an adaptive detection strategy is generated with the prediction of the spatial distribution of uncertainty as the objective function. This strategy is then fed back to the feature modeling and state propagation processes in steps S2-S5, forming a closed-loop adaptive system. The detection strategy optimization problem can be formulated as a multi-objective constrained optimization problem:

[0146]

[0147] ; ;

[0148]

[0149] in, The binary decision variable representing the detection location. This indicates that the testing equipment is deployed in the i-th pipe segment. This indicates the inspection frequency for each pipe section. This indicates the selection of detection methods, where 1 represents ultrasonic testing, 2 represents magnetic particle testing, 3 represents X-ray testing, and 4 represents comprehensive testing. Objective function. It consists of three parts: Indicates the total uncertainty of the system. This represents the total testing cost. Indicates the benefit of improved detection accuracy. These are weighting coefficients. Constraints include the total budget constraint. Upper and lower limits of detection frequency constraints .

[0150] The detection strategy optimization employs a gradient-based optimization algorithm. Specifically, a mixed-integer programming method is used to decompose the original problem into a continuous optimization subproblem and a discrete choice subproblem. The continuous optimization subproblem is solved using the gradient descent algorithm to find the optimal detection frequency. ;

[0151] in, It's the learning rate. This is the gradient of the objective function with respect to the detection frequency. The discrete selection subproblem is solved by an improved genetic algorithm to find the optimal combination of detection location and detection method. The fitness function of the genetic algorithm is designed based on the objective function value, and the selection, crossover, and mutation operations are specialized for the hybrid coding chromosome structure.

[0152] The system dynamically adjusts and optimizes parameters based on real-time collected detection data, achieving adaptive evolution of the detection strategy. When new detection data indicates a significant change in the corrosion state of a certain area, the system reassesses the uncertainty distribution of that area and adjusts the configuration of detection resources accordingly. Triggering conditions for adaptive adjustment include: prediction error exceeding a preset threshold, a significant increase in uncertainty, and the detection of a new corrosion mode. The adjustment process employs an incremental optimization strategy, recalculating only the affected pipe sections, avoiding the enormous computational overhead of global re-optimization.

[0153] The results of the detection strategy optimization are fed back to the feature modeling process in Example 1 and the state propagation process in Example 2 through a feedback mechanism, forming a complete closed-loop adaptive system. Feedback information includes the reliability assessment of the detection data, suggestions for adjusting feature weights, and the direction of correction for the state propagation parameters. The feature modeling process adjusts the weight allocation of different features based on the feedback information, highlighting feature dimensions that contribute significantly to the current prediction accuracy. The state propagation process adjusts the parameters of the gating mechanism based on the feedback information, optimizing the strategy for retaining historical information and the strategy for selecting new information.

[0154] In this embodiment, the multimodal data acquisition module further integrates the automatic control functions of the intelligent detection equipment. The high-speed acquisition card not only acquires ultrasonic array wall thickness data but also controls the scanning path and detection parameters of the ultrasonic probe. The multi-channel potential difference acquisition unit expands the dynamic configuration function of the reference electrode, enabling automatic adjustment of the electrode's measurement frequency and accuracy based on optimization strategies. The flow and pressure sensing interfaces add fluid sampling and composition analysis functions, providing more comprehensive data support for corrosion mechanism analysis. The strain gauge signal conditioner integrates vibration monitoring and structural health assessment functions, enabling the detection of changes in the pipeline's structural integrity.

[0155] In this embodiment, the multi-scale reconstruction module for pipeline corrosion feature tokens adds configurable downsampling rates and upsampling strategies, dynamically adjusting the spatial and temporal resolution of feature extraction based on the results of detection strategy optimization. When a region is identified as a high-risk area, the system increases the feature extraction resolution for that region, enhancing the ability to capture local corrosion details. When a region is identified as a low-risk area, the system decreases the feature extraction resolution for that region, allocating computational resources to areas requiring greater attention.

[0156] In this embodiment, the windowed attention interaction module implements the function of adaptively adjusting the window size and translation parameters according to the input data resolution. The system automatically selects the optimal window configuration based on the spatial and temporal resolution of the current data to ensure the efficiency and effectiveness of attention calculation. The adaptive adjustment algorithm is based on the principles of information theory, aiming to maximize mutual information to determine the optimal values ​​of the window parameters.

[0157] In this embodiment, the closed-loop optimization module for the detection strategy based on uncertainty feedback implements the function of dynamically configuring the objective function weights according to the application scenario. Different application scenarios correspond to different risk preferences and cost constraints, and the system can automatically adjust the weight coefficients according to the user-defined preference parameters. For example, in application scenarios with high security requirements, the system increases the weight for minimizing uncertainty to ensure the reliability of the prediction; in cost-sensitive application scenarios, the system increases the weight for minimizing cost to achieve economical resource allocation.

[0158] Work process

[0159] The closed-loop detection optimization process adopts a distributed multi-level control architecture, including four main levels: data layer, analysis layer, decision layer, and execution layer, to achieve fully closed-loop automated control from data acquisition to detection execution.

[0160] At the data layer, the system receives multi-scale features and state prediction results from Examples 1 and 2, while simultaneously collecting real-time detection data from intelligent detection devices. The data fusion module performs unified format conversion and quality assessment on the multi-source heterogeneous data to ensure that the data used in subsequent analyses adheres to consistent quality standards. Data storage employs a distributed time-series database, supporting efficient storage and rapid retrieval of massive amounts of historical data, providing a data foundation for long-term trend analysis and pattern recognition.

[0161] At the analysis layer, the uncertainty quantification module calculates the uncertainty distribution of multi-timescale predictions through Monte Carlo sampling and Bayesian inference. Spatial interpolation algorithms extend the uncertainty values ​​of discrete measurement points to the entire pipeline network, generating a continuous uncertainty field. The time series analysis module identifies dynamic patterns of uncertainty change, including periodic fluctuations, trend changes, and sudden anomalies. The statistical learning module extracts a relationship model between detection effectiveness and uncertainty reduction from historical detection data, providing empirical evidence for optimizing detection strategies.

[0162] At the decision-making level, the multi-objective optimization engine calculates the optimal detection strategy based on the current uncertainty distribution and constraints. The optimization process employs a hierarchical, progressive strategy: first, coarse-grained global optimization determines the macro-level allocation of detection resources; then, fine-grained local optimization determines specific detection parameters. The risk assessment module evaluates the safety and feasibility of the optimization results, ensuring that the optimization strategy does not pose security risks or exceed the system's capabilities. The decision support system provides operations and maintenance personnel with a visual decision-making interface, supporting manual intervention and strategy adjustments.

[0163] At the execution layer, the automated control system drives various testing devices to perform testing tasks according to optimization strategies. The task scheduling module coordinates the working sequence of different testing devices to avoid resource conflicts and duplicate testing. The equipment monitoring module monitors the working status and testing quality of the testing devices in real time, promptly identifying and handling equipment failures. The feedback collection module transmits testing results and equipment status information back to the decision-making layer, providing data support for the next round of optimization.

[0164] The entire process employs an event-driven response mechanism, enabling the system to respond rapidly to emergencies and anomalies. Upon detecting an urgent corrosion risk, the system immediately initiates an emergency detection process, allocating the nearest detection resources for a rapid response. When prediction uncertainty increases abnormally, the system automatically increases the detection frequency and accuracy in that area. If detection equipment malfunctions, the system automatically adjusts the detection plan, utilizing other available equipment to maintain detection coverage.

[0165] The system also implements a multi-layered learning and adaptation mechanism. The short-term learning mechanism adjusts the parameters of the prediction model based on recent detection results, improving the accuracy of short-term predictions. The medium-term learning mechanism adjusts the parameters of the corrosion model based on seasonal environmental changes, adapting to cyclical changes in environmental conditions. The long-term learning mechanism adjusts the overall risk assessment strategy based on the pipeline's aging trend, achieving synchronous evolution with the pipeline's life cycle.

[0166] Cross-timescale corrosion prediction and uncertainty quantification significantly improve the reliability and practicality of the prediction system. By decomposing the prediction task into short-term, medium-term, and long-term timescales, the system can simultaneously meet the different needs of real-time monitoring and long-term planning. Short-term predictions provide timely and accurate information support for daily operation and maintenance decisions, medium-term predictions provide a scientific basis for maintenance plan formulation, and long-term predictions provide strategic guidance for asset management and upgrades. The collaborative work of multi-timescale predictions avoids the limitations of single-timescale predictions, and the prediction accuracy is significantly improved across all timescales: short-term prediction accuracy reaches 96%, medium-term prediction accuracy reaches 89%, and long-term prediction accuracy reaches 82%, representing improvements of 8%, 12%, and 15% respectively compared to traditional single-scale methods.

[0167] The uncertainty quantification method based on Monte Carlo Dropout provides a scientific confidence assessment of the prediction results. Traditional deterministic prediction methods can only provide point estimates and cannot assess the reliability of the prediction. However, the uncertainty quantification method in this embodiment can provide both the predicted value and the confidence interval, providing a quantitative basis for risk assessment in decision-making. Practical application shows that the coverage of the 95% confidence interval reaches 93%, and the correlation coefficient between the prediction uncertainty and the actual prediction error reaches 0.87, proving the effectiveness and accuracy of uncertainty quantification. This quantified uncertainty information enables operations and maintenance personnel to formulate differentiated response strategies based on specific risk tolerance, avoiding overly conservative or overly aggressive decision-making biases.

[0168] The automatic configuration of detection strategies based on gradient optimization significantly improves the utilization efficiency of detection resources. Traditional detection strategies are usually based on experience and lack scientific optimization basis, which can easily lead to resource waste or insufficient coverage. The optimization method in this embodiment can automatically determine the optimal configuration of detection resources according to the spatial distribution of prediction uncertainty, increasing the uncertainty reduction by 35% under the same budget constraints. Specifically, the detection frequency in high uncertainty regions is reasonably increased, and the detection frequency in low uncertainty regions is appropriately reduced, making the allocation of detection resources more accurate and efficient.

[0169] The closed-loop feedback mechanism enables continuous improvement and adaptive evolution of the prediction system. Traditional prediction systems are open-loop, unable to adjust model parameters based on actual results, leading to a decline in prediction accuracy over time. The closed-loop feedback mechanism in this embodiment continuously optimizes the prediction model based on detection results, giving the system self-learning and self-improvement capabilities. Long-term operational data shows that the system's prediction accuracy exhibits a stable upward trend, with an average annual improvement rate of 5%, effectively avoiding model performance degradation. Closed-loop feedback also enhances the system's adaptability to environmental changes; when pipeline operating conditions or environmental factors change, the system can quickly adjust its prediction strategy, maintaining the stability of prediction accuracy.

[0170] The automated control of intelligent inspection equipment significantly improves the efficiency and quality of inspection operations. Traditional pipeline inspection requires a large amount of manual operation, which is not only inefficient but also prone to human error. The automated control system integrated in this embodiment can automatically drive the inspection equipment to perform inspection tasks according to optimization strategies, reducing the need for manual intervention. The consistency and reproducibility of automated inspection are far superior to manual inspection, with the standard deviation of inspection results reduced by 60% and inspection efficiency increased by 3 times. At the same time, the automated system can work around the clock, unaffected by personnel shifts and fatigue, greatly improving the timeliness and continuity of inspection coverage.

[0171] A multi-layered learning and adaptation mechanism endows the system with excellent robustness and scalability. Learning mechanisms at different time scales enable the system to adapt to environmental changes and system evolution over varying time spans. Short-term learning ensures rapid response to sudden changes, while long-term learning ensures effective tracking of trend changes. When faced with new pipe types, new corrosive environments, or new detection technologies, the system can quickly adapt through the learning mechanism without requiring a complete redesign of the system architecture. The scalable design allows the system to be easily scaled to larger-scale pipe networks, supporting various application scenarios from single pipelines to complex pipe networks.

[0172] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be modified within the scope of the concept described herein by means of the above teachings or the technology or knowledge in related fields.

Claims

1. A pipeline defect analysis method based on corrosion mechanism, characterized in that, Includes the following steps: S1. Multi-source corrosion data acquisition and spatiotemporal alignment: Acquire multi-source corrosion data of pipelines, and perform spatiotemporal alignment and preprocessing of multimodal data; S2. Multi-scale reconstruction based on pipeline corrosion feature tokens: Multi-source data are organized into a corrosion feature token matrix according to the axial position and radial depth of the pipeline. Each token in the corrosion feature token matrix contains corrosion mechanism parameters corresponding to the spatial position. The high-resolution pipeline corrosion field is mapped to the low-resolution feature space through hierarchical downsampling to capture the macroscopic corrosion evolution trend. Then, the original pipeline spatial resolution is restored through upsampling. Finally, the pipeline corrosion feature representations of different spatial scales are fused through cross-scale residual connection. S3. Windowed self-attention mechanism and periodic window translation interaction: The erosion state field is divided into local windows and self-attention calculation is performed within the windows. Cross-window information exchange is achieved through periodic window translation operation; S4. Decoupling and Embedding of Pipeline Corrosion Mechanism Parameters and Axial Selective Attention: Different corrosion mechanism parameters, including corrosion depth field, electrochemical potential distribution, fluid shear stress field, and material stress tensor field, are arranged into a data matrix according to feature fields and mapped to independent high-dimensional embedding spaces. An axial attention mechanism is constructed in the dimensional direction of corrosion mechanism parameters to achieve selective information interaction across corrosion mechanism parameters. S5. Selective state-space propagation driven by corrosion mechanism parameters: Construct a discrete-time state-space model, in which some elements of the state transition matrix are calculated from the electrochemical corrosion rate constant of the pipeline material, the ambient pH value, and the physical quantities of the fluid shear stress corrosion mechanism, while the remaining elements are dynamically generated by the current corrosion feature input. The retention and forgetting of historical corrosion state information are controlled by a selective gating mechanism driven by the corrosion evolution stage. S6. On-chip memory optimization of parallel recursive evolution for pipeline corrosion state sequences: The recursive calculation of the state space model is reconstructed into a parallel scanning algorithm adapted to GPU on-chip SRAM storage, and parallel recursive processing is performed on the corrosion state sequences sampled along the pipeline length direction. S7. Corrosion Prediction and Uncertainty Quantification Across Time Scales: Based on multi-scale mechanism state representation, output short-term local corrosion rate prediction and long-term global corrosion morphology evolution prediction, and calculate the uncertainty distribution of the prediction; S8. Closed-loop optimization of detection strategy based on uncertainty feedback: Taking the spatial distribution of predicted uncertainty as the objective function, an adaptive detection strategy is generated and fed back to the feature modeling and state propagation process in steps S2-S5 to form a closed-loop adaptive system.

2. The method according to claim 1, characterized in that, The multi-source corrosion data in step S1 includes data acquired through wall thickness ultrasonic arrays, electrochemical probes, fluid dynamics sensors, stress and strain sensors, and environmental parameter monitors. The data is aligned based on timestamps and spatial coordinates, and preprocessed with missing data completion and noise filtering.

3. The method according to claim 1, characterized in that, In step S2, the corrosion feature tokens contain wall thickness, potential, and stress corrosion mechanism parameters corresponding to the spatial location. Layered downsampling adopts a stride convolution sampling strategy, while upsampling adopts a transposed convolution method.

4. The method according to claim 1, characterized in that, In step S3, the size of the local window is 8×8 to 32×32 spatial units, the displacement of the window translation is 1 / 4 to 1 / 2 of the window size, and the translation period is 3 to 8 time steps.

5. The method according to claim 1, characterized in that, In step S4, the Query, Key, and Value matrices of the axial attention mechanism are generated through a linear transformation of the embedding vectors of different corrosion mechanism parameters.

6. The method according to claim 1, characterized in that, The selective gating mechanism in step S5 includes a forget gate, a selection gate, and a reset gate. The state transition matrix, input mapping matrix, and output mapping matrix are generated by mapping the current erosion features through a multilayer perceptron. The gating activation function is the sigmoid function.

7. The method according to claim 1, characterized in that, In step S6, the parallel scanning algorithm adopts a prefix sum calculation mode and completes the state recursion of T time steps in log(T) time complexity through a binary tree structure. The GPU shared memory allocation strategy is optimized by block division according to the state vector dimension.

8. The method according to claim 1, characterized in that, In step S7, uncertainty quantification is achieved through Monte Carlo Dropout. The uncertainty of short-term and long-term predictions is kept consistent through time-scale-related covariance propagation. In step S8, the detection strategy optimization adopts a gradient-based optimization algorithm.

9. A pipeline defect analysis system based on corrosion mechanism, characterized in that, include: The multimodal data acquisition module includes a high-speed acquisition card for acquiring ultrasonic array wall thickness data, a multi-channel potential difference acquisition device for acquiring electrochemical probe signals, a flow and pressure sensing interface for acquiring fluid parameters, and a strain gauge signal conditioner for acquiring stress and strain data. It is used to acquire and spatiotemporally align multi-source data related to pipeline corrosion through multiple sensors. The multi-scale reconstruction module for pipeline corrosion feature tokens is used to perform downsampling, feature aggregation, upsampling, and cross-scale residual connection operations based on corrosion feature tokens. It organizes multi-source data into a corrosion feature token matrix according to the axial position and radial depth of the pipeline. Each token in the corrosion feature token matrix contains corrosion mechanism parameters corresponding to the spatial position. The high-resolution pipeline corrosion field is mapped to a low-resolution feature space through hierarchical downsampling to capture the macroscopic corrosion evolution trend. Then, the original pipeline spatial resolution is restored through upsampling. Finally, the pipeline corrosion feature representations of different spatial scales are fused through cross-scale residual connection. The windowed attention interaction module is used to realize the self-attention calculation of local windows and the interaction of periodic window translation; the erosion state field is divided into local windows and self-attention calculation is performed within the window, and cross-window information exchange is realized through periodic window translation operation; The pipeline corrosion mechanism parameter decoupling modeling module is used to perform independent embedding and axial selective attention interaction of different corrosion mechanism parameters. Different corrosion mechanism parameters, including corrosion depth field, electrochemical potential distribution, fluid shear stress field, and material stress tensor field, are arranged into a data matrix according to feature fields and mapped to independent high-dimensional embedding spaces. An axial attention mechanism is constructed in the dimensional direction of corrosion mechanism parameters to realize selective information interaction across corrosion mechanism parameters. The selective state propagation module is used to implement input-conditional state-space modeling and selective memory propagation. The on-chip storage optimization module includes a GPU processor with a shared memory area of ​​not less than 48KB, used to perform parallel recursive state evolution calculations optimized by GPU on-chip SRAM; The cross-scale prediction module is used to output corrosion evolution predictions and uncertainty quantifications across multiple time scales. The detection strategy closed-loop optimization module based on uncertainty feedback is used to optimize the detection strategy based on the uncertainty distribution and feed it back to the feature modeling and state propagation process.

10. The system according to claim 9, characterized in that, The pipeline corrosion feature token multi-scale reconstruction module includes configurable downsampling rate and upsampling strategy; the window size and translation parameters of the windowed attention interaction module are adaptively adjusted according to the input data resolution; and the objective function weight of the detection strategy closed-loop optimization module based on uncertainty feedback is dynamically configured according to the application scenario.

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