Environmental corrosion assessment and pressure pipeline failure analysis method based on AI database

Abstract

The invention relates to the technical field of pipeline safety monitoring and integrity evaluation, and discloses an environmental corrosion evaluation and pressure pipeline failure analysis method based on an AI database, and the method comprises the steps: firstly constructing a high-dimensional phase space point cloud of monitoring data through a sliding time window, and generating a topological persistent landscape representing environmental steady state features through topological data analysis; constructing a Riemannian manifold dynamics database, and searching an optimal evolution path conforming to physical smoothness in a manifold space based on a Hessian energy minimization principle; and calculating a real-time topological entropy, dynamically driving the order of the variable-order fractional order differential equation by using the real-time topological entropy, and solving the equation in combination with an evolution path parameter to deduce the wall thickness attenuation process. Through fusion of algebraic topology and manifold geometry, deep coupling of data geometric features and a physical mechanism model is realized, the problems that a traditional model lacks interpretation and cannot adaptively describe a corrosion historical memory effect are solved, and the physical reliability of failure analysis is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of pipeline safety monitoring and integrity evaluation technology, specifically to a method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database. Background Technology

[0002] As the lifeline for energy transmission and industrial production, the operational safety of pressure pipelines is directly related to public safety and environmental protection. However, buried pressure pipelines are exposed to complex corrosive environments such as soil and water bodies for extended periods. They are affected by the coupling effects of multiple physical fields, including soil physicochemical properties, stray current interference, microbial activity, and pipeline stress, resulting in a highly nonlinear, random, and time-varying corrosion evolution process. Therefore, establishing accurate environmental corrosion assessment and failure analysis models is crucial for effectively managing the remaining life and failure risks of pipelines.

[0003] In the existing technological system, there are two main categories of analytical methods: physical mechanism-based models and data-driven methods. Physical mechanism-based methods typically rely on electrochemical kinetic equations, Faraday's law, or empirical / semi-empirical formulas, attempting to describe the relationship between corrosion rate and environmental factors through analytical formulas. However, these methods are often based on idealized experimental environment assumptions, making it difficult to fully encompass the complex multi-field coupling effects in actual working conditions. Furthermore, the model parameters are usually set as constants, failing to dynamically reflect the evolution of the corrosion mechanism over time, thus limiting their generalization ability and prediction accuracy in complex and variable environments.

[0004] With the development of sensor technology and artificial intelligence, data-driven methods based on neural networks, support vector machines, or random forests are increasingly being applied to corrosion prediction. While these methods address nonlinear fitting issues to some extent, they are essentially black-box models, lacking explicit physical interpretability. More critically, existing data-driven methods, when processing multi-source heterogeneous sensor data, mostly rely on calculating Euclidean distance based on numerical statistical features for similarity measurement or pattern recognition. This approach ignores the inherent geometric and topological structure of high-dimensional environmental data, making it difficult to effectively align data features with isomorphic mechanisms under different operating conditions, and is highly susceptible to environmental noise interference.

[0005] Furthermore, when using historical cases for failure attribution analysis, existing technologies typically rely solely on the similarity of geometric features for retrieval, neglecting the physical continuity and energy constraints in the state evolution process. Two state points that are geometrically close may have extremely high energy barriers between them, lacking a true physical evolution path. Simultaneously, corrosion processes exhibit significant historical memory and heredity; that is, current corrosion behavior depends not only on the current environmental conditions but also on historically accumulated damage paths. Traditional integer-order differential equation models or fixed-order fractional-order models cannot adjust the model's memory strength in real time according to the complexity of environmental conditions, making it difficult to accurately describe the dynamic behavior of phase transitions during the corrosion mechanism throughout the pipeline's entire life cycle. This often leads to significant deviations in failure prediction results, failing to meet the demands of practical engineering for high-confidence, interpretable failure analysis. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an environmental corrosion assessment and pressure pipeline failure analysis method based on an AI database. This method solves the problems in existing environmental corrosion assessment and pipeline failure analysis technologies, such as the difficulty of mechanism models in handling multi-field coupled environments, the lack of physical interpretability of traditional data-driven methods, and the neglect of material corrosion history memory effects.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database, comprising the following steps:

[0008] Collect multi-source environmental parameters and pipeline body monitoring data of pressure pipelines, and construct a high-dimensional phase space point cloud set using a sliding time window;

[0009] Topological data analysis is performed on the high-dimensional phase space point cloud set to generate a persistent topological landscape that can characterize the steady-state features of the corrosive environment.

[0010] A Riemannian manifold dynamics database containing historical corrosion cases was constructed, mapping the topologically persistent landscape to coordinate points on the Riemannian manifold space;

[0011] In the Riemannian manifold space, based on the Hessian energy minimization principle, the optimal evolution path from the historical state point to the current state point and pointing to the future failure state is searched.

[0012] The real-time topological entropy is calculated based on the persistent topological landscape, and the fractional order of the variable-order fractional differential equation is dynamically determined using the real-time topological entropy.

[0013] By combining the dynamic parameters extracted from the optimal evolution path with the fractional order, the variable-order fractional differential equation is solved to deduce the pipe wall thickness decay process and determine the failure risk.

[0014] Furthermore, when constructing a high-dimensional phase space point cloud set, a monitoring vector containing multi-dimensional sensor readings is defined. Based on Takens' embedding theorem, a time delay parameter and embedding dimension are set. The monitoring vectors at the current moment and historical moments are arranged in a time sequence to form a time-delay embedding matrix that can characterize the instantaneous dynamic state of the system. The column vectors of this matrix constitute the high-dimensional phase space point cloud set.

[0015] Furthermore, generating a topologically persistent landscape addresses the alignment and feature extraction challenges of heterogeneous data. Specifically, based on preset filtering parameters, a Vietoris-Rips simplistic complex sequence is constructed from the high-dimensional phase space point cloud set.

[0016] The homology groups of simplicial complex sequences in different dimensions are calculated to obtain the temporal parameters of topological feature generation and decay, generating a persistent graph. To map the topological features to a linear vector space, the persistent graph is transformed into a piecewise linear persistent landscape function. For the first Layered landscape, its function is defined as:

[0017] ;

[0018] in, Indicates taking the first Large values, For feature generation time and the time of extinction Constructed trigonometric auxiliary functions.

[0019] Furthermore, this invention introduces a Riemannian manifold geometry to store and retrieve corrosion cases. Each historical corrosion case record in the Riemannian manifold dynamics database includes: the coordinate position of the topological persistent landscape corresponding to the case in the manifold space, the corrosion dynamics parameter set corresponding to the case, and the local curvature tensor of the manifold at the coordinate position.

[0020] Furthermore, to ensure the continuity and rationality of the retrieved reference cases in terms of physical evolution, this invention employs the Hessian energy minimization principle when searching for the optimal evolution path in the manifold space. Specifically, this involves constructing a description of the evolution path. Energy functional This functional is composed of a weighted sum of the kinetic energy term and the Hessian energy term:

[0021] ;

[0022] in, The norm square of the path tangent vector under the manifold metric is used to constrain the path length; The norm square of the covariant derivative of the path tangent vector along the curve under the manifold metric is the Hessian energy term, used to penalize second-order mutations in the evolutionary path.

[0023] The weighting coefficients are used. By solving for the minimum value of the energy functional using the variational method, the fourth-order spline curve on the manifold is obtained as the optimal evolution path, thereby eliminating pseudo-similarity cases that are geometrically close but have excessively high physical evolution energy.

[0024] Furthermore, by establishing a correlation between geometric topology and physical memory, this invention solves the problem that fixed-order models cannot describe the dynamic changes in corrosion mechanisms.

[0025] Specifically, this involves calculating the real-time topological entropy. First, the function values ​​corresponding to the persistent topological landscape are normalized to obtain probability distribution characteristics. Then, the Shannon entropy is calculated as the real-time topological entropy that measures the complexity of the erosion morphology. .

[0026] Subsequently, the topological entropy and fractional order are established. Nonlinear mapping function between:

[0027] ;

[0028] As the real-time topological entropy value increases, it indicates that the corrosion morphology is becoming more complex. The fractional order is adjusted by the mapping function to deviate from the standard diffusion order, thereby enhancing the physical model's memory weight of historical corrosion states.

[0029] Furthermore, the deduction of the pipe wall thickness attenuation process is based on Caputo-type variable-order fractional differential equations.

[0030] The equation includes not only a time-varying fractional derivative operator driven by the real-time topological entropy, but also an effective corrosion rate coefficient determined by the optimal evolution path. and environmental impact modification items Pipe wall thickness The evolution follows the following dynamic equation:

[0031] ;

[0032] By numerically solving this equation, a predicted value for the future wall thickness, including the effects of historical cumulative damage, can be obtained.

[0033] A second aspect of the present invention provides an environmental corrosion assessment and pressure pipeline failure analysis system based on an AI database, comprising:

[0034] The data acquisition and reconstruction module is used to collect environmental and pipeline data and construct a high-dimensional phase space point cloud set.

[0035] The topological feature extraction module is used to extract the topological persistent landscape from point clouds using persistent cohomology techniques; the manifold evolution analysis module is used to maintain the Riemannian manifold dynamics database and search for the optimal evolution path in the manifold space based on the Hessian energy minimization principle.

[0036] The dynamics derivation module is used to calculate real-time topological entropy, dynamically set the order of variable-order fractional differential equations using topological entropy, and solve the equations in combination with the optimal evolution path parameters to output failure analysis results.

[0037] This invention provides a method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database. It offers the following advantages:

[0038] 1. This invention generates a persistent topological landscape by performing topological data analysis on a high-dimensional phase space point cloud set, transforming the numerical characteristics of environmental and pipeline monitoring data into topological geometric features. This effectively shields the interference caused by sensor noise and environmental parameter fluctuations, and solves the difficulties of traditional statistical methods in aligning multi-source heterogeneous data. This enables the system to accurately identify corrosion modes with isomorphic mechanisms in complex environments with different regions and working conditions.

[0039] 2. This invention establishes a nonlinear mapping relationship between real-time topological entropy and the order of variable-order fractional differential equations, and utilizes the geometric complexity of data to drive the memory characteristics of the physical model in real time. This enables the dynamic adaptive adjustment of the corrosion mechanism during the transition from uniform corrosion to local pitting or stress cracking, overcoming the shortcomings of traditional fixed-order models that cannot accurately describe the cumulative damage and historical memory effect of pipelines throughout their entire life cycle.

[0040] 3. This invention achieves deep intrinsic coupling between data geometry and physical evolution equations by using the topological persistent landscape as the boundary condition or parameter driving source of the differential equation. This enables the method to perform effective generalization inference based on the topological similarity of small sample data even in the absence of a large number of failure negative samples. This solves the technical bottleneck that pipeline failure, as a low-probability event, is difficult to train using conventional big data methods. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention;

[0042] Figure 2 This is a system architecture diagram of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0044] Example:

[0045] Please see the appendix Figure 1 This invention provides a method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database, comprising the following steps:

[0046] Step S1: Collect multi-source environmental parameters and pipeline body monitoring data of the pressure pipeline, and construct a high-dimensional phase space point cloud set using a sliding time window.

[0047] In this embodiment, step S1, which utilizes a sliding time window to construct a high-dimensional phase space point cloud set, aims to address the technical problem that numerical snapshots of a single time section in traditional pipeline monitoring methods are insufficient to reflect the nonlinear dynamic evolution trend of the corrosion system. Since soil corrosion and pressure pipeline failure is a typical multi-physics coupled time-varying process, its current corrosion rate depends not only on current environmental parameters but also significantly influenced by historical state evolution paths. Therefore, it is necessary to reconstruct one-dimensional or low-dimensional time series data into a high-dimensional phase space to reveal the system's implicit dynamic attractor structure.

[0048] Specifically, the first step is to collect data using a multi-source sensor array deployed around the pressure pipeline. The collected data covers various heterogeneous environmental factors and pipeline condition parameters that affect the pipeline corrosion rate.

[0049] As a preferred implementation, the parameters collected may include, but are not limited to: soil resistivity, soil pH, redox potential, stray current density, concentration of key anions in the soil, pipeline spontaneous potential, and local stress-strain data of the pipeline. These data are simultaneously sampled and preprocessed by the data acquisition unit to ensure consistency of the time reference.

[0050] After acquiring the raw monitoring data, this embodiment defines... Moment Dimensional monitoring vector Its mathematical expression is as follows:

[0051] ;

[0052] in, This represents the total dimension of the monitored parameters. Indicates the first One sensor in Normalized readings at time points. To eliminate the order-of-magnitude differences between different physical dimensions, it is preferable to standardize the data of each component before constructing the vector.

[0053] Subsequently, based on Takens' embedding theorem in nonlinear dynamics theory, the phase space of the aforementioned multidimensional time series data was reconstructed. This theorem states that for an infinite-dimensional dynamical system, the dynamic trajectory that is differentially homeomorphic to the original system can be recovered in the reconstructed phase space from the observed low-dimensional time series.

[0054] In this embodiment, the specific process of phase space reconstruction involves the determination of two key parameters: the time delay parameter. and embedding dimension .

[0055] For the current moment The system defines a sliding time window of a certain length, which includes the current and historical monitoring status at several past moments. Based on this, a phase space point cloud set is constructed. This set consists of a series of phase space vectors Composition, specifically defined as follows:

[0056] ;

[0057] in, This represents the total number of sample points contained within the sliding time window, i.e., the size of the point cloud.

[0058] Each phase space vector in the set By monitoring vectors It is generated by time delay extension, and its construction formula is:

[0059] ;

[0060] The above formula shows that each Not only includes The status information at any given moment was also traced back along a timeline. The step size allows for a complete description of the system's evolution trajectory within that local time segment in a high-dimensional space.

[0061] Regarding the selection of parameters, as a preferred implementation method, the time delay parameter... The embedding dimension can be determined by calculating the first minimum point of the autocorrelation function or mutual information function to ensure the independence between the components of the reconstructed vector; The pseudo-nearest neighbor method can be used to determine the phase space, ensuring that the reconstructed phase space can fully unfold the dynamic structure of the system and avoid spurious intersections of trajectories.

[0062] The point cloud set constructed through the above steps Essentially, it is a geometric representation of the instantaneous dynamic state of a pipeline corrosion system in a high-dimensional phase space.

[0063] This point cloud collection It is no longer a set of isolated numerical values, but a geometric object with a specific topological structure. Its distribution pattern is directly related to the stability type of the corrosion system. This also provides the necessary geometric data foundation for extracting persistent landscape features using algebraic topology methods in subsequent steps.

[0064] Step S2: Perform topological data analysis on the high-dimensional phase space point cloud set to generate a topological persistent landscape that can characterize the steady-state features of the corrosive environment.

[0065] In this embodiment, step S2 performs topological data analysis on the high-dimensional phase space point cloud set to generate a persistent topological landscape that can characterize the steady-state features of the corrosive environment. This aims to solve the technical problems of traditional Euclidean distance-based feature extraction methods, which struggle to handle multi-source heterogeneous data and resist environmental noise interference. Because the phase space point cloud set constructed in the aforementioned steps... These are discrete data points embedded in a high-dimensional space, and their inherent dynamic characteristics are often reflected in the overall geometry of the point cloud rather than the numerical value of individual points. Therefore, using the continuous homology technique in algebraic topology to extract these topological features that are invariant to coordinate transformations and local deformations can more fundamentally characterize the intrinsic mechanism of corrosive environments.

[0066] Specifically, the discrete point cloud data first needs to be transformed into continuous topological spatial objects. This embodiment preferably uses the Vietoris-Rips simplex construction algorithm. For a given set of filtering parameters... This parameter represents the spatial scale radius of the observed data. The system calculates the point cloud set. Any two data points Distance between This distance is usually measured using Euclidean distance or other metrics suitable for Riemannian manifolds.

[0067] During the construction process, when the distance between any two points in the point cloud subset is less than the given filtering parameters... At this point, the subset forms a simplex. Specifically, two points form an edge, three points form a triangle, four points form a tetrahedron, and so on. This varies with the filtering parameters. from As the value is gradually increased to a preset upper limit, the system generates a series of nested simple complex sequences; this process is called filtering. This dynamic process simulates the changes in the shape of observed data at different scales, from microscopic to macroscopic, thereby capturing steady-state characteristics across scales.

[0068] Subsequently, based on the aforementioned simple complex sequence, the system calculates different dimensions. Homogroups .in, A homology group corresponds to a connected component. A one-dimensional homology group corresponds to a one-dimensional hole or cycle. A 2D homology group corresponds to a two-dimensional cavity. During the filtering process, each topological feature will be in a specific... Value generated at, and in larger The place where it was located has disappeared.

[0069] For each dimension The system records the parameter values ​​at the time of generation of all topological features. and the parameter values ​​at the time of extinction Thus generating the first A persistent graph of dimension, denoted as :

[0070]

[0071] In this persistence plot, the horizontal axis represents the generation time, and the vertical axis represents the decay time. Points farther from the diagonal indicate that the feature is more stable across multiple scales, usually corresponding to the true corrosion kinetics; while points closer to the diagonal are usually considered as environmental noise.

[0072] To map the persistent graph composed of discrete point pairs into a vector space form capable of algebraic operations and statistical analysis, facilitating subsequent storage and retrieval in the Riemannian manifold database, this embodiment further transforms the persistent graph into a persistent landscape. A persistent landscape is a series of piecewise linear functions defined over the real number domain, which can preserve all topological information in the persistent graph and possesses good stability.

[0073] The specific conversion process is as follows: First, define the transformation process for each feature point. Auxiliary trigonometric functions The function is on the real number line. The upper part forms an isosceles triangle, defined by the following equation:

[0074] ;

[0075] This function intuitively represents the interval in which the feature exists and its significance.

[0076] Based on the above auxiliary functions, the first The first dimension Layered persistent landscape function Defined at any position At that point, the th in the set of all characteristic function values Larger values, i.e.:

[0077] ;

[0078] in, Indicates taking the first Large-value operators. Typically, the first layer of landscape... The topological features contained at the highest level, while higher-level landscapes contain nested or less salient feature structures.

[0079] Through the above processing, the originally disordered and difficult-to-measure environmental point cloud data is transformed into a deterministic function sequence. This sequence of functions constitutes the topological fingerprint of the current corrosive environment. This representation method not only standardizes heterogeneous data, but also, through the properties of topological invariants, enables the system to identify working conditions that, even with different environmental parameter values, share the same corrosion evolution mechanism, laying the foundation for accurate matching in the manifold space.

[0080] Step S3: Construct a Riemannian manifold dynamics database containing historical corrosion cases, mapping the topologically persistent landscape to coordinate points on the Riemannian manifold space.

[0081] In this embodiment, step S3 constructs a Riemannian manifold dynamics database containing historical corrosion cases and maps the topological persistent landscape to coordinate points on the Riemannian manifold space. This aims to overcome the limitation of traditional relational databases, which can only perform retrieval based on Euclidean distance when processing complex functional data, thus ignoring the inherent nonlinear geometric structure of the data. Since the persistent landscape function generated in step S2 is essentially an element in an infinite-dimensional function space, the simple linear space assumption is insufficient to describe the continuity and tortuous characteristics of the state evolution of the corrosion system. Therefore, introducing the Riemannian manifold as the geometric base to carry historical data can provide a more accurate measurement space for subsequent physical evolution analysis.

[0082] Specifically, the system first needs to establish a mapping from the persistent landscape function space to the Riemannian manifold M. Because the persistent landscape... Possess good Spatial properties are preferably mapped to a point on a Grassmann manifold or statistical manifold. In this process, the state of the corrosive environment at each historical moment is no longer treated as an isolated database record, but rather parameterized as a coordinate point in the manifold space. This manifold space is assigned a specific Riemannian metric. This makes the distance between two points in space no longer a straight line distance, but the geodesic length along the surface of the manifold, which conforms to the principle of minimum action in the evolution of physical states.

[0083] Based on this, each historical case record is stored in the Riemannian manifold dynamics database. Each is designed as a triple containing geometric coordinates, physical parameters, and local structural features, and its mathematical expression is defined as:

[0084] ;

[0085] in, Indicates the first The coordinates of a historical case on a Riemannian manifold are determined by manifold embedding of the persistent landscape of the historical environmental data corresponding to the case.

[0086] Furthermore, This represents the set of corrosion kinetic parameters that have been experimentally verified or confirmed through long-term field monitoring for this historical case. These parameters serve as the baseline true values ​​for subsequent physical model derivations and preferably include: corrosion rate constant, pitting depth propagation coefficient, characteristic parameters of the electrochemical impedance spectroscopy of the material surface, and the diffusion coefficient in the reaction-diffusion equation. These parameters are correlated with coordinates... This establishes a one-to-one mapping relationship, meaning that a specific environmental topology corresponds to specific physical and dynamic parameters.

[0087] It is worth noting that, in order to support energy-based path search in subsequent steps, this embodiment also specifically introduces the manifold local curvature tensor into the database records. This tensor describes the manifold at coordinate points. The degree of curvature in the vicinity. In Riemannian geometry, the curvature tensor It is usually defined as:

[0088] ;

[0089] in, For the tangent vector field, For the Levi-Civita connection. In this embodiment, This is used to quantify the sensitivity or instability of a corrosion system under this condition. Physically, regions with high manifold curvature correspond to bifurcation points or abrupt changes in corrosion evolution, while regions with flat curvature correspond to stable, uniform corrosion stages.

[0090] Through the above construction method, the database not only stores what the data is, but also where the data is located and the spatial geometric characteristics of the data. This structure makes the subsequent retrieval process no longer a simple table lookup and matching, but rather a search for the most reasonable evolutionary trajectory in a physically constrained curved space. This ensures that even in the absence of perfectly matching historical data, reference parameters that conform to physical laws can be derived based on the geometric continuity of the manifold.

[0091] Step S4: In the Riemannian manifold space, based on the Hessian energy minimization principle, search for the optimal evolution path from the historical state point to the current state point and pointing to the future failure state.

[0092] In this embodiment, step S4, based on the Hessian energy minimization principle, searches for the optimal evolution path from historical state points to the current state point and pointing towards the future failure state within the Riemannian manifold space. This aims to address the technical problem that traditional case retrieval methods based on geometric distance neglect the physical continuity and energy constraints in the corrosion evolution process. In corrosion dynamics, the change of system state is an asymptotic process following the laws of thermodynamics. Two state points that are geometrically close, if the line connecting them requires traversing a manifold region with extremely high curvature, do not have a real evolutionary correlation. Therefore, this embodiment introduces an energy functional with second-order smoothness constraints to find an optimal trajectory on the manifold that conforms to physical inertia.

[0093] Specifically, the first step is to construct the Riemannian manifold mentioned above. Define a parameterized evolution curve. The curve Connected to the reference historical state points in the database Compared with the currently monitored state points And extend to the future state according to the direction of the tangent vector.

[0094] To evaluate the physical plausibility of this evolutionary path, this embodiment constructs an energy functional that includes velocity and Hessian acceleration terms. This functional measures not only the length of a path but also its curvature, and its mathematical expression is as follows:

[0095] ;

[0096] in, For path parameters; Indicates the curve in the parameter The tangent vector at that point, This constitutes the kinetic energy term, which is used to constrain the geometric length of the path on the manifold, ensuring that the retrieved reference case has a basic similarity in topological features to the current state.

[0097] In the formula This represents the covariant derivative of the tangent vector along the curve itself, i.e., the acceleration vector on the manifold. This constitutes the Hessian energy term. The core function of this term is to penalize drastic transitions or abrupt changes in the evolutionary path. In a physical sense, corrosion is an energy dissipation process, and its state evolution should be smooth; minimizing the Hessian energy essentially requires the evolutionary path to be as close as possible to a geodesic or spline curve, avoiding jumps that violate the asymptotic laws of physics.

[0098] In the formula These are regularization weight coefficients used to balance the weights between path length matching and evolutionary smoothness. As a preferred implementation, The value can be adaptively set according to the average curvature modulus of the local manifold.

[0099] To determine the optimal evolution path In this embodiment, the variational method is used to solve the above-mentioned minimum problem of the energy functional, that is, to let the first-order variation of the functional be... The Euler-Lagrange equation corresponding to this variational problem is expressed as a fourth-order differential equation on a Riemannian manifold:

[0100] ;

[0101] in, For the Riemann curvature tensor, This represents a cubic covariant differential. The equation explicitly couples the geometry of the manifold with the dynamics of the path.

[0102] The system solves the equation numerically, thereby selecting a path from the candidate historical case set that maximizes the total energy. Minimum path This path is considered the most likely evolutionary source and future destination of the current corrosion state.

[0103] Finally, the system does not directly copy the parameters from historical cases, but extracts the optimal path. At the current point The tangent vector in the tangent space at the point and the gradient of the dynamic parameters along the path are derived. These derived parameters contain trend information along the evolution direction and will be used as effective physical constraints input into the subsequent variable-order fractional differential equations, thereby ensuring the physical and logical rigor of the failure prediction.

[0104] Step S5: Calculate the real-time topological entropy based on the persistent topological landscape, and use the real-time topological entropy to dynamically determine the fractional order of the variable-order fractional differential equation.

[0105] In this embodiment, step S5 calculates the real-time topological entropy based on the persistent topological landscape and uses the real-time topological entropy to dynamically determine the fractional order of the variable-order fractional differential equation. This aims to solve the technical problem that the order in traditional fractional corrosion models is usually set as a constant, thus failing to accurately reflect the phase transition of the corrosion mechanism over time. The core advantage of fractional calculus lies in its ability to describe the historical memory and nonlocality of the process through non-integer order derivative operators. This embodiment innovatively proposes to use the geometric topological complexity of the data to drive the adjustment of this physical memory characteristic in real time, achieving deep intrinsic coupling between data features and mechanism parameters.

[0106] Specifically, the first step is to process the topological persistent landscape function generated in the aforementioned steps. A quantitative evaluation is performed. Although the persistent landscape function preserves the shape information of point cloud data at multiple scales and is a sequence of functions defined in the real number domain, it must be compressed into an index that can measure the disorder or complexity of the system in order to integrate it into a scalar-driven physical model. To this end, this embodiment introduces the concept of Shannon entropy from information theory.

[0107] During the calculation process, the persistent landscape function of the selected dimensions and levels is first... Normalization is performed to transform it into a generalized probability density distribution function. The normalization process follows the constraint that the integral of the total probability is 1, that is... Subsequently, the Shannon entropy of this probability distribution is calculated and defined as the real-time topological entropy. The integral form of the formula is as follows:

[0108] ;

[0109] Alternatively, a discretized form can be used in numerical computation:

[0110] ;

[0111] in, This represents the normalized landscape value within a discrete interval. This is the real-time topological entropy. It has clear physical implications: when the environment is in a stable and uniform corrosion stage, the data structure is simple, the feature points in the persistence graph are concentrated, the landscape function peaks are few and narrow, and the corresponding topological entropy is low; however, when the environment experiences multi-scale local corrosion, pitting corrosion clusters, or microcrack propagation caused by stress concentration, the feature points in the persistence graph are discrete and abundant, resulting in complex and varied landscape function morphology and a significant increase in the corresponding topological entropy value.

[0112] After obtaining the real-time topological entropy, this embodiment establishes the relationship between the topological entropy and the order of the variable-order fractional differential equation. The nonlinear mapping relationship between them. In fractional-order dynamics theory, the order... The diffusion properties and memory strength of the system are determined when When the value is close to 1, the system exhibits a standard Fick diffusion or Markov process, meaning that the current corrosion rate mainly depends on the current state, with historical influences being relatively weak; when... When the distance from 1 is reduced, the system exhibits anomalous diffusion, which means that the weight of historical states on the current evolution increases significantly, i.e., it exhibits strong memory.

[0113] Based on the above physical mechanism, since a more complex corrosion morphology often implies a stronger nonlinear autocatalytic effect and a more significant historical dependence of the system, this embodiment constructs a mapping function. Transforming real-time topological entropy into time-varying fractional order. :

[0114] ;

[0115] To ensure the smoothness of the order change and to conform to the physical range of values, a variant of the Sigmoid function is preferred as the specific mapping model:

[0116] ;

[0117] in, It is usually set to 1.0, which represents an ideal uniform corrosion state; Set as the lower bound of experience, representing the order of strong memory under severe localized corrosion conditions; It serves as a reference threshold for entropy, used to determine the critical point where the order changes significantly; To adjust the sensitivity coefficient, the rate at which the order changes with the entropy value is controlled.

[0118] Through this mapping mechanism, when the topological entropy of the monitoring data increases due to environmental degradation or changes in corrosion mechanisms, the system automatically reduces the fractional order. This enhances the memory weight of past accumulated damage paths in the subsequent solution of the dynamic equations, enabling the prediction model to adaptively capture the dynamic characteristics from quantitative change to qualitative change.

[0119] Step S6: Combine the dynamic parameters extracted from the optimal evolution path with the fractional order to solve the variable-order fractional differential equation, deduce the pipeline wall thickness decay process, and determine the failure risk.

[0120] In this embodiment, step S6 combines the dynamic parameters extracted from the optimal evolution path with the fractional order to solve a variable-order fractional differential equation, deduce the pipe wall thickness decay process, and determine the failure risk. The aim is to integrate the geometric evolution constraints and topological memory characteristics obtained in the preceding steps into a unified physical-mathematical model. Traditional corrosion prediction often uses empirical formulas or simple linear extrapolations, making it difficult to quantify the cumulative damage effect in nonlinear processes. This embodiment, by constructing and solving a variable-order fractional differential equation, can accurately describe the nonlocal evolution of the remaining pipe wall thickness over time based on physical mechanisms.

[0121] Specifically, the system first establishes a description of the remaining wall thickness of the pressure pipeline. The time-varying kinetic governing equations. Considering the heritability and memory of the corrosion process, these equations are constructed as Caputo-type variable-order fractional differential equations, with the following mathematical expression:

[0122]

[0123] The left side of the equation for The time order is The Caputo fractional derivative operator. The fundamental reason for choosing the Caputo definition is that its derivative with respect to constants is zero, and the initial conditions have clear physical meaning, which makes the model more suitable for solving practical engineering problems.

[0124] Furthermore, in order to fully disclose the technical details of this embodiment, the specific definition of the Caputo fractional derivative operator is as follows:

[0125] ;

[0126] in, For the Gamma function, This is the integral variable, representing a historical time point. This integral term clearly demonstrates the core mechanism of the model: the current time... The rate of change of wall thickness depends not only on the current instantaneous state, but also on... to The entire historical evolution process Weighted convolution. Weight function. This is called the memory kernel, and its time-varying order is determined by step S5. Dynamic control. When When changes occur, the decay rate of the memory core changes accordingly, thus simulating the process of the corrosion mechanism transforming from uniform corrosion without memory to local pitting corrosion with strong memory.

[0127] The right-hand side of the equation contains physical parameters that drive corrosion evolution. Among them, This is the effective corrosion rate coefficient. It's worth noting that this coefficient is not a fixed constant, but rather the optimal evolution path found in the Riemannian manifold space through step S4. It is certain. The system extracts the dynamic parameters at the corresponding moment on the path, maps them to the current tangent space through parallel movement on the manifold, thus ensuring that the parameter values ​​conform to the physical inertia of historical evolution.

[0128] At the same time, in the equation This is an environmental impact correction item, used to reflect current instantaneous environmental parameters. Modulation effect on the basic corrosion rate. Preferably, this correction term can be in the form of Arrhenius or polynomial regression to quantify the effect of environmental stress on the activation energy of the corrosion reaction.

[0129] After constructing the above dynamic equations, since variable-order fractional differential equations are usually difficult to solve analytically, this embodiment preferably uses a numerical discretization method for solving them. Specifically, a predictor-corrector algorithm is used. This algorithm discretizes the continuous time domain into time steps of... The grid is first used to predict the initial wall thickness at the next moment using historical information. Then, the trapezoidal rule is used to correct the integral equation, thereby obtaining a high-precision numerical solution sequence. .

[0130] Finally, the system determines the failure risk based on the projected future wall thickness sequence. The safe operating wall thickness threshold for the pipeline is set as follows: The system calculates and predicts the wall thickness. Down to The required time is the remaining service life. If this time is less than the preset maintenance cycle, or if the probability of failure exceeds the confidence threshold within a specific prediction time window, the system will generate a failure warning signal and output a corresponding corrosion evolution path diagram and key influencing factor analysis report, providing direct quantitative basis for pipeline maintenance decisions.

[0131] Please see the appendix Figure 2 An environmental corrosion assessment and pressure pipeline failure analysis system based on an AI database includes:

[0132] The data acquisition and reconstruction module is used to collect environmental and pipeline data and construct a high-dimensional phase space point cloud set.

[0133] The topology feature extraction module is used to extract persistent topological landscapes from point clouds using persistent cohomology techniques.

[0134] The manifold evolution analysis module is used to maintain the Riemannian manifold dynamics database and search for the optimal evolution path in the manifold space based on the Hessian energy minimization principle.

[0135] The dynamics derivation module is used to calculate real-time topological entropy, dynamically set the order of variable-order fractional differential equations using topological entropy, and solve the equations in combination with the optimal evolution path parameters to output failure analysis results.

[0136] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database, characterized in that, Includes the following steps: Step S1: Collect multi-source environmental parameters and pipeline body monitoring data of the pressure pipeline, and construct a high-dimensional phase space point cloud set using a sliding time window; Step S2: Perform topological data analysis on the high-dimensional phase space point cloud set to generate a topological persistent landscape that can characterize the steady-state features of the corrosive environment. Step S3: Construct a Riemannian manifold dynamics database containing historical corrosion cases, and map the topological persistent landscape to coordinate points on the Riemannian manifold space; Step S4: In the Riemannian manifold space, based on the Hessian energy minimization principle, search for the optimal evolution path from the historical state point to the current state point and pointing to the future failure state. Step S5: Calculate the real-time topological entropy based on the topological persistent landscape, and use the real-time topological entropy to dynamically determine the fractional order of the variable-order fractional differential equation. Step S6: Combine the dynamic parameters extracted from the optimal evolution path with the fractional order to solve the variable-order fractional differential equation, deduce the pipeline wall thickness decay process, and determine the failure risk.

2. The method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database according to claim 1, characterized in that, In step S1, constructing the high-dimensional phase space point cloud set specifically includes: Define a monitoring vector that includes multidimensional sensor readings; Based on Takens' embedding theorem, set the time delay parameter and the embedding dimension; The monitoring vectors of the current moment and historical moments are arranged in time sequence to form a time-delay embedding matrix that can characterize the instantaneous dynamic state of the system. The column vectors of this matrix constitute the high-dimensional phase space point cloud set.

3. The method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database according to claim 1, characterized in that, In step S2, generating the topologically persistent landscape specifically includes: Based on preset filtering parameters, a Vietoris-Rips simple complex sequence is constructed from the high-dimensional phase space point cloud set; Calculate the homology groups of the simple complex sequence in different dimensions, obtain the time parameters of topological feature generation and destruction, and generate a persistent graph. The persistent graph is transformed into a piecewise linear persistent landscape function, and the sequence of functions serves as the topological persistent landscape.

4. The method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database according to claim 1, characterized in that, In step S3, each historical corrosion case record in the Riemannian manifold dynamics database contains: The coordinates of the topological persistent landscape in the manifold space corresponding to this case. The corrosion kinetic parameter set corresponding to this case; The local curvature tensor of the manifold at this coordinate location.

5. The method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database according to claim 1, characterized in that, In step S4, the Hessian energy minimization principle specifically refers to: Construct an energy functional that describes the evolutionary path, the energy functional being a weighted sum of kinetic energy terms and Hessian energy terms; The kinetic energy term is the squared norm of the path tangent vector under the manifold metric, used to constrain the path length; The Hessian energy term is the norm square of the covariant derivative of the path tangent vector along the curve under the manifold metric, used to penalize second-order mutations in the evolutionary path. By solving for the minimum value of the energy functional, the optimal evolution path that satisfies the physical smoothness constraint is determined.

6. The method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database according to claim 5, characterized in that, The search for the optimal evolution path involves solving the Euler-Lagrange equation corresponding to the energy functional using the variational method, and obtaining a fourth-order spline curve on the manifold as the optimal evolution path.

7. The method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database according to claim 1, characterized in that, In step S5, calculating the real-time topological entropy specifically includes: The function values ​​corresponding to the topological persistent landscape are normalized to obtain the probability distribution characteristics; Calculate the Shannon entropy of the probability distribution characteristics and define it as the real-time topological entropy that measures the complexity of the erosion morphology.

8. The method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database according to claim 7, characterized in that, In step S5, dynamically determining the fractional order specifically includes: Establish a nonlinear mapping function between topological entropy and fractional order; As the real-time topological entropy value increases, the fractional order is adjusted through the nonlinear mapping function to deviate from the standard diffusion order, thereby enhancing the physical model's memory weight of historical corrosion states.

9. The method for environmental corrosion assessment and pressure pipeline failure analysis based on an AI database according to claim 1, characterized in that, In step S6, the variable-order fractional differential equation is a Caputo-type differential equation, which not only includes the time-varying fractional derivative operator driven by the real-time topological entropy, but also includes the effective corrosion rate coefficient and environmental impact correction term determined by the optimal evolution path.

10. An environmental corrosion assessment and pressure pipeline failure analysis system based on an AI database, comprising the environmental corrosion assessment and pressure pipeline failure analysis method based on an AI database according to claims 1-9, characterized in that, include: The data acquisition and reconstruction module is used to collect environmental and pipeline data and construct a high-dimensional phase space point cloud set. The topology feature extraction module is used to extract persistent topological landscapes from point clouds using persistent cohomology techniques. The manifold evolution analysis module is used to maintain the Riemannian manifold dynamics database and search for the optimal evolution path in the manifold space based on the Hessian energy minimization principle. The dynamics derivation module is used to calculate real-time topological entropy, dynamically set the order of variable-order fractional differential equations using topological entropy, and solve the equations in combination with the optimal evolution path parameters to output failure analysis results.

Images