A method and system for intelligent early warning of multi-dimensional corrosion risk of a semi-buried pipeline

Abstract

This invention discloses a multi-dimensional intelligent early warning method and system for corrosion risks in semi-buried pipelines. The intelligent early warning method includes the following steps: dividing the semi-buried pipeline into typical corrosion evolution scenarios based on corrosion environment gradient characteristics; deploying monitoring nodes in each scenario to synchronously and continuously collect multi-dimensional data on environmental parameters and corrosion responses; processing the collected environmental parameters and corrosion response data to construct a standardized spatiotemporal sequence feature dataset; storing the standardized spatiotemporal sequence feature dataset in a cloud-edge collaborative hybrid time-series database, and achieving dynamic association storage of environment-material-corrosion multi-dimensional data through spatiotemporal indexing; obtaining a corrosion risk cognitive map based on the standardized spatiotemporal sequence feature dataset using an intelligent prediction model; and conducting risk assessment and triggering corresponding early warnings based on the corrosion risk cognitive map. This invention achieves real-time monitoring, early prediction, and accurate early warning of corrosion risks, improving the safety and scientific nature of pipeline operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of pipeline monitoring and early warning technology, specifically to a multi-dimensional intelligent early warning method and system for corrosion risk of semi-buried pipelines. Background Technology

[0002] In the transition section from sea to land, semi-buried crude oil pipelines successively traverse various corrosive environments, including the marine atmosphere zone, wave splash zone, tidal zone, soil interface zone, and fully buried zone. They are subjected to multiple stress coupling effects such as high salt spray, alternating wet and dry conditions, stray currents, and microorganisms over a long period of time. Corrosion behavior exhibits characteristics of multi-scenario differentiation, dynamic time-varying nature, and coupled inducing factors. Pipeline corrosion failure can easily lead to serious consequences such as media leakage, safety accidents, and environmental damage. Therefore, it is necessary to monitor and provide early warning of corrosion risks of semi-buried crude oil pipelines to achieve safe operation and maintenance.

[0003] Currently, corrosion monitoring and risk control for semi-buried pipelines mainly rely on regular manual inspections, offline electrochemical detection, local point sensor monitoring, and simple threshold alarms. However, these methods have the following problems: 1. Monitoring (e.g., wall thickness sampling, cathodic protection potential, etc.) usually only obtains single-point, instantaneous physical or electrochemical parameters, lacking synchronous and continuous acquisition of multi-dimensional information such as corrosion environment, material response, and damage evolution, resulting in the absence of a dynamic correlation data chain of "environment-material-corrosion".

[0004] 2. Existing research on corrosion mechanisms mostly relies on accelerated laboratory tests. The test conditions differ from actual complex working conditions, which limits our understanding of the actual corrosion evolution.

[0005] 3. Analytical methods are mostly limited to post-event statistics and simple threshold alarms, making it difficult to predict corrosion trends in advance and diagnose root causes. This leads to corrosion prevention and maintenance strategies often being based on fixed cycles or post-event repairs, lacking accurate decision support based on real-time status and forward-looking predictions, resulting in insufficient proactive prevention and control capabilities.

[0006] In summary, existing technologies are insufficient to integrate the mechanisms of actual complex operating conditions to achieve early prediction and accurate diagnosis of corrosion trends in semi-buried pipelines. This results in delayed corrosion risk warnings, a lack of scientific basis for maintenance decisions, and difficulty in shifting from passive response to proactive prevention and control. Summary of the Invention

[0007] The purpose of this invention is to provide a multi-dimensional intelligent early warning method and system for corrosion risk of semi-buried pipelines, so as to solve the problem that existing technologies are difficult to integrate the actual complex working conditions and mechanisms to predict and accurately diagnose corrosion risks of semi-buried pipelines in advance.

[0008] The technical solution of this invention is: A multi-dimensional intelligent early warning method for corrosion risk of semi-buried pipelines includes the following steps: Typical corrosion evolution scenarios are divided along the semi-buried pipeline according to the characteristics of corrosion environment gradient. Monitoring nodes are deployed in each scenario to collect environmental parameters and multi-dimensional data of corrosion response simultaneously and continuously. The collected raw environmental parameters and corrosion response multidimensional data are assigned metadata tags containing data source identifiers and timestamps. After edge preprocessing and feature extraction, the data is uploaded to the cloud and then processed in the cloud to form a standardized spatiotemporal sequence feature dataset. Standardized spatiotemporal sequence feature datasets are stored in a cloud-edge collaborative hybrid time series database. The cloud-edge collaborative hybrid time series database also stores pipeline attributes, corrosion scenario definitions, and monitoring metadata. The two types of data are linked through a spatiotemporal index to achieve dynamic association and storage of multidimensional environmental-material-corrosion data. Input the stored standardized spatiotemporal sequence feature dataset into the trained intelligent prediction model to obtain a corrosion risk cognitive map; Real-time monitoring data is collected, and risk level assessment is performed by combining the corrosion risk perception map, historical baseline, and corrosion mechanism rules. This triggers graded early warnings and outputs the main risk factors and maintenance decision recommendations.

[0009] Preferably, as a further improvement of the present invention, the formation of the standardized spatiotemporal sequence feature dataset includes the following steps: The received tagged data is cleaned and repaired in the cloud, and then the heterogeneous data from different monitoring nodes are time-aligned and spatially interpolated and fused using the corrosion evolution scenario and timestamp as the core index to form a standardized spatiotemporal sequence feature dataset.

[0010] Preferably, as a further improvement of the present invention, the cloud-edge collaborative hybrid time-series database includes a time-series database and a relational database associated through a spatiotemporal index. The time-series database is used to store streaming sensor data, which includes environmental parameters, corrosion response-related dynamic data, and standardized spatiotemporal sequence feature datasets after edge preprocessing and cloud cleaning. The relational database is used to store pipeline attribute data, corrosion scenario definition data, and monitoring metadata. The pipeline attribute data includes pipeline material and anti-corrosion coating type. The corrosion scenario definition data includes the classification criteria and corrosion characteristics of each typical scenario. The monitoring metadata includes metadata tags of data source identifier and timestamp. The two are linked for cross-database query through a unified query service.

[0011] Preferably, as a further improvement of the present invention, the construction process of the intelligent prediction model includes the following steps: Based on historical and real-time monitoring data stored in a cloud-edge collaborative hybrid time-series database, time-delay cross-correlation analysis and principal component analysis are used to analyze the dynamic coupling relationship between environmental variable sequences and corrosion response signals, and to identify key environmental factor combinations and their contribution weights under different corrosion evolution scenarios. Based on the combination of key environmental factors and the field environmental load spectrum, the multi-stress coupled accelerated corrosion environment was reproduced in the laboratory and tests were carried out on samples of the same material. The corrosion mechanism was revealed by combining microscopic characterization methods to calibrate and correct the corrosion rate mechanism model. A mechanism-data fusion intelligent prediction model is constructed based on the calibrated corrosion rate mechanism model. The mechanism-data fusion intelligent prediction model is trained using historical data. The normalized standardized spatiotemporal sequence feature dataset is input into the trained mechanism-data fusion intelligent prediction model, and a corrosion risk cognitive map is output.

[0012] Preferably, as a further improvement of the present invention, the mechanism-data fusion intelligent prediction model is a multi-task-physical information network architecture. This model takes a standardized spatiotemporal sequence feature dataset as input and performs normalization processing on the input data. The mechanism-data fusion intelligent prediction model includes a shared feature encoder and parallel task heads. The shared feature encoder adopts a temporal convolutional network or a Transformer structure. The parallel task heads include at least a regression task head for predicting corrosion depth, a classification task head for outputting the probability of multiple failure modes, and a survival analysis task head for generating the probability distribution of the pipeline's remaining life. During model training, an electrochemical kinetic equation is embedded in the loss function as a physical constraint term. At the same time, Monte Carlo Dropout technology is used to achieve Bayesian inference, so that the probability distribution and confidence interval of the predicted values ​​are output after model training. Finally, a corrosion risk cognitive map containing corrosion depth distribution, multiple failure mode risk, contribution of dominant inducing factors, and remaining life confidence interval is output.

[0013] Preferably, as a further improvement of the present invention, the method for performing risk assessment and triggering corresponding early warning includes the following steps: Establish an early warning and judgment engine based on fuzzy reasoning and evidence theory; The real-time monitoring data, the corrosion risk perception map, historical baseline data, and preset mechanism rules are input into the early warning judgment engine to calculate the comprehensive risk confidence level. Based on the range of the comprehensive risk confidence level, the probability of the predicted indicators exceeding the safety threshold, the changes in risk trends, and the extent of exceeding limits in real-time monitoring data, corresponding level 1, level 2, and level 3 warnings are triggered. After an early warning is triggered, the system performs a risk root cause diagnosis based on the contribution of risk characteristics, corrosion mechanism rules, and historical cases. It also conducts cost-benefit simulations to compare different maintenance strategies and outputs anti-corrosion maintenance decision recommendations.

[0014] Preferably, as a further improvement of the present invention, the first-level warning is triggered when the comprehensive risk confidence level is 30%~60% or the risk prediction trend is unfavorable, and interface marking and enhanced monitoring measures are taken; the second-level warning is triggered when the comprehensive risk confidence level is 60%~85% or the probability of exceeding the safety threshold is >80%, and pre-maintenance work orders and special inspection measures are taken; the third-level warning is triggered when the comprehensive risk confidence level is >85% or the real-time data seriously exceeds the limit, and audible and visual alarms and emergency plan activation measures are taken.

[0015] Preferably, as a further improvement of the present invention, the plurality of the typical corrosion evolution scenarios include at least three of the following: marine atmosphere zone, splash zone, tidal zone, soil-atmosphere interface zone, fully buried zone, and key heterogeneous connection point.

[0016] Based on the same inventive concept, this invention also discloses a multi-dimensional corrosion risk intelligent early warning system for semi-buried pipelines, used to implement the aforementioned multi-dimensional corrosion risk intelligent early warning method for semi-buried pipelines, comprising: A distributed intelligent sensing network is deployed at multiple monitoring nodes in various corrosion evolution scenarios to collect multidimensional data on corrosion environment parameters and pipeline corrosion response, and to complete edge preprocessing and feature extraction of the raw data. The cloud-edge collaborative data governance platform communicates with the distributed intelligent sensing network and is used to clean, spatiotemporally fuse and standardize the received data to construct a standardized spatiotemporal sequence feature dataset. A cloud-edge collaborative hybrid time-series database is connected to the cloud-edge collaborative data governance platform to store standardized spatiotemporal sequence feature data, pipeline assets and scene metadata after governance, and to provide a unified association query service based on spatiotemporal index. The mechanism-data fusion intelligent modeling platform communicates with the cloud-edge collaborative data governance platform to build and train an intelligent prediction model based on mechanism-data fusion, and generate a corrosion risk cognitive map. The intelligent early warning and decision support application platform communicates with the mechanism-data fusion intelligent modeling platform to receive corrosion risk cognitive maps, integrate real-time monitoring data, historical baselines and mechanism rules, trigger graded early warnings through a multi-level early warning judgment engine, and provide risk root cause diagnosis and maintenance decision suggestions.

[0017] Preferably, as a further improvement of the present invention, the monitoring node includes an environmental sensing unit, a corrosion response sensing unit, and an edge intelligent processing unit. The environmental sensing unit is used to collect multi-dimensional corrosion environment parameters under pipeline service scenarios; the corrosion response sensing unit is used to capture electrochemical and physical corrosion response signals of pipeline materials; and the edge intelligent processing unit is used to perform local filtering, feature extraction, cache storage, and preliminary anomaly diagnosis based on preset rules on the collected raw data.

[0018] Compared with the prior art, the beneficial effects of the present invention are: 1. By deploying intelligent monitoring nodes in various typical corrosion evolution scenarios, the system can synchronously and continuously collect multi-dimensional information such as corrosion environment, material response, and corrosion evolution, thus completing the dynamic correlation data chain of "environment-material-corrosion" and facilitating on-site dynamic correlation analysis.

[0019] 2. By adopting a three-step progressive mechanism-data fusion method of "on-site dynamic correlation analysis + laboratory mechanism mapping calibration + physical information neural network modeling", the experimental conditions are made closer to the complex working conditions in reality, which improves the physical interpretability and extrapolation reliability of the intelligent prediction model.

[0020] 3. Upgrade the early warning mode from a reactive, threshold-based alarm to a proactive, probabilistic trend early warning. Attached Figure Description

[0021] Figure 1 A schematic diagram of the logical framework of a multi-dimensional intelligent early warning method for corrosion risks in semi-buried pipelines. Detailed Implementation

[0022] The following is combined Figure 1 The specific embodiments of the present invention will be described in detail below. In the description of the invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0023] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of the invention, unless otherwise stated, "a plurality of" means two or more.

[0024] Example like Figure 1 As shown in the figure, this invention provides a multi-dimensional intelligent early warning method for corrosion risk of semi-buried pipelines, which may include: S1, along the pipeline from the ocean end to the land end, is divided into multiple typical corrosion evolution scenarios based on the characteristics of the corrosion environment. Distributed intelligent monitoring nodes are deployed in each scenario to synchronously and continuously collect and obtain multi-dimensional data on environmental parameters and corrosion response through distributed intelligent monitoring nodes.

[0025] It should be noted that the typical corrosion evolution scenarios described in this embodiment are divided based on the typical laying method of pipelines in coastal areas, the location of the soil / atmosphere interface and the range of waterline variation, and according to the dominant type of corrosion kinetic mechanism and environmental gradient, the pipeline is discretized into several continuous "corrosion evolution scenarios".

[0026] Furthermore, typical corrosion evolution scenarios include at least three of the following: fully immersed zone, splash zone, marine atmosphere zone, soil-atmosphere interface zone, fully buried zone, and key heterogeneous connection point.

[0027] Fully submerged area (seabed section): continuously submerged in seawater, characterized by uniform corrosion and biological corrosion.

[0028] Tidal zone (splash zone): This area experiences periodic wet and dry cycles and is subjected to the combined effects of mechanical impact and high salt concentration. It is the most severely corroded area, mainly characterized by localized corrosion (pitting corrosion, crevice corrosion).

[0029] Marine Atmosphere Zone: Exposed to an atmosphere rich in salt spray, affected by solar radiation, humidity, and salt deposition, and primarily characterized by atmospheric corrosion.

[0030] Soil-atmosphere interface zone (semi-buried area): The pipeline is partially buried and partially exposed, and there are potential pathways for oxygen concentration cells and stray currents, resulting in complex and uneven corrosion behavior.

[0031] Fully buried areas: affected by soil resistivity, moisture content, pH value, and microbial activity, the main causes are soil corrosion and possible external corrosion.

[0032] Key heterogeneous connection points, such as valves, flanges, joints, and connections of different materials, are prone to galvanic corrosion.

[0033] Specifically, the intelligent monitoring node includes an environmental sensing unit, a corrosion response sensing unit, and an edge intelligent processing unit.

[0034] The environmental sensing unit is used to collect multi-dimensional corrosion environment parameters under pipeline service scenarios.

[0035] Environmental sensing unit: ①Atmospheric / Surface Environment: Temperature and humidity sensor, salt spray deposition rate meter (dry film method or conductivity method), chloride ion concentration sensor (solid-state ion selective electrode), solar radiation / ultraviolet intensity meter.

[0036] ② Soil environment: Multi-parameter soil sensing probe to measure pH, redox potential, moisture content, resistivity and temperature.

[0037] ③Water Environment: Multi-parameter water quality monitor, suitable for tidal zones, measures dissolved oxygen, conductivity, and temperature.

[0038] The corrosion response sensing unit is used to capture electrochemical and physical corrosion response signals of pipeline materials.

[0039] Electrochemical sensor array: ① Three-electrode array (working electrode material is the same as the pipe): used for linear polarization resistance measurement of instantaneous corrosion rate and electrochemical impedance spectroscopy intermittent measurement to evaluate coating / rust layer status.

[0040] ② Electrochemical noise sensor: monitors current / potential noise and identifies the nascent stage of localized corrosion (pitting corrosion, stress corrosion cracking).

[0041] ③ Zero-resistance galvanometer: Connects the pipe body to a representative dissimilar metal (such as flange bolts) to directly monitor galvanic corrosion current.

[0042] Physical sensor group: ① Resistance probe: Provides an absolute measurement of cumulative metal loss, used to calibrate electrochemical data.

[0043] ② Acoustic emission sensor: monitors transient elastic waves generated by coating peeling, matrix crack propagation, and stress corrosion cracking.

[0044] ③ Magnetic memory / weak magnetic field detection unit: monitors magnetic field distortion caused by stress concentration, serving as an indirect indicator of early stress corrosion risk.

[0045] ④ Fiber Bragg grating sensor: Attached to the surface of the pipe, it monitors strain and temperature, and can indirectly assess the health status of the coating by the changes in the strain field caused by coating degradation.

[0046] The edge intelligent processing unit is used to perform local filtering, feature extraction, cache storage, and preliminary anomaly diagnosis based on preset rules on the collected raw data.

[0047] The core of the edge intelligence processing unit: a low-power, high-performance microprocessor (such as the ARM Cortex-A series) or an edge AI chip.

[0048] Functions of the edge intelligent processing unit: ① Data preprocessing: Filtering (such as wavelet denoising), amplification, and analog-to-digital conversion of the original signal.

[0049] ②Feature extraction: Real-time calculation of key features at the edge, such as LPR value, standard deviation and kurtosis of EN, energy and count rate of AE events, and center wavelength drift of FBG.

[0050] ③ Rule Engine: Runs pre-defined lightweight rules (such as "if three consecutive LPR values ​​are below threshold A and EN kurtosis is greater than B, then mark it as 'suspected pitting'") to achieve local anomaly diagnosis.

[0051] ④ Data buffering and communication: Local storage (SD card / Flash), and according to the policy, the feature data or compressed raw data is uploaded to the cloud via 4G / 5G / NB-IoT / LoRa or wired network.

[0052] Therefore, the collected environmental parameters and corrosion response multidimensional data are preprocessed and feature extracted by the edge intelligent processing unit.

[0053] It should be noted that intelligent monitoring nodes need to be deployed in layers: Primary nodes (dense deployment in critical scenarios): In high-risk scenarios such as splash zones, soil-atmosphere interfaces, and heterogeneous junctions, deploy an all-purpose node (integrating all or most sensors) every 50-100 meters.

[0054] Secondary nodes (intra-scenario gradient deployment): Within a single scenario (e.g., ocean and atmosphere zone), simplified nodes are deployed along elevation or wind direction gradients to focus on monitoring the spatial distribution of environmental parameters.

[0055] Level 3 nodes (mobile / temporary nodes): Deployed on drones or inspection robots, they are used to conduct supplementary inspections and data collection in blind spots or areas of sudden risk that are difficult to cover by Level 1 and Level 2 nodes.

[0056] Level 4 nodes (reference / calibration nodes): Long-term stability testing nodes are set up in a laboratory or controlled field environment for periodic calibration of field sensor drift.

[0057] Power supply and protection for intelligent monitoring nodes: They adopt a solar-battery hybrid power supply and are equipped with an explosion-proof and corrosion-resistant shell that meets IP68 / NEMA6P standards, making them suitable for harsh marine environments.

[0058] It should be noted that the intelligent monitoring node deployment strategy adopts a hybrid star and mesh topology. Edge nodes aggregate data to the gateway node within the scene via a wireless mesh, and the gateway node connects to the cloud via a faster and more stable backhaul link (such as fiber optic or 5G). This enhances network robustness. A collaborative triggering mechanism between nodes is also defined. (For example, when the acoustic emission sensor of a node detects a suspected crack event, it can automatically wake up the high-frequency LPR or EN sensors of neighboring nodes to perform synchronous intensive data acquisition to capture related signals).

[0059] Step S2: Assign metadata tags containing data source identifiers and timestamps to the collected raw environmental parameters and corrosion response multidimensional data. After edge preprocessing and feature extraction, upload to the cloud and then process in the cloud to form a standardized spatiotemporal sequence feature dataset.

[0060] Specifically, using "scene-location-timestamp" as the core spatiotemporal index, the preprocessed and feature-extracted environmental parameters and corrosion response multidimensional data are processed to obtain a standardized spatiotemporal sequence feature dataset. Based on the cloud-edge collaborative architecture, the standardized spatiotemporal sequence feature dataset is processed hierarchically and transmitted in an optimized manner. This solves the problem of "aggregation, management, and use" of massive, heterogeneous, and spatiotemporally correlated monitoring data, and transforms the raw data stream into high-quality, correlated, and easily analyzable data assets.

[0061] The implementation process is as follows: S21: Building a unified metadata model and foundation for data traceability: Based on the corrosion evolution scenario, monitoring nodes, and sensor deployment information defined in step S1, a structured core metadata tag is assigned to all collected raw data streams. This tag includes at least the data source identifier (scene_id, node_id, sensor_id), the collection timestamp, the data type and unit (data_type, unit), and the initial quality flag (quality_flag), forming a data lineage index that runs through all subsequent processing stages, ensuring full-chain traceability.

[0062] S22: Implement real-time streaming processing and optimized transmission at the edge: Within the edge intelligent processing unit deployed on-site, the raw data generated by various sensors in step S1 is processed in real time. For high-frequency, high-volume waveform data (such as acoustic emission and electrochemical noise), real-time filtering and time-frequency domain feature extraction are performed to convert the raw waveforms into low-dimensional feature vectors. For mid-to-low frequency scalar data (such as temperature, potential, and LPR values), validity verification, jump filtering, and calculation of statistical features (such as mean and standard deviation) based on sliding windows are performed. The processed feature data and some key sample data are then losslessly or lossily compressed and uploaded to the cloud via a communication network according to a strategy, thereby significantly optimizing network bandwidth and cloud storage consumption while ensuring that key information is not lost.

[0063] S23: Implement cloud-based multi-source data fusion and standardized governance: After receiving data from various edge nodes in the cloud, in-depth processing is performed. First, the data undergoes secondary cleaning and repair, using statistical models or cross-sensor consistency checks to identify and process outliers not filtered at the edge in step S22. Then, using the spatiotemporal index defined in step S21 as the core, heterogeneous data streams from different nodes and with different sampling frequencies are fused through time alignment and spatial interpolation techniques to form a standardized multidimensional data cube with "scene-time" as the coordinate system. Finally, derived features (such as cumulative corrosion and environmental factor exposure intensity indices) are calculated and added, and correlated with external data sources (such as meteorology and tides), outputting a high-quality, structured dataset that can directly serve database storage in step S3 and model training in step S4.

[0064] It should be noted that this step requires a unified data model and spatiotemporal index definition.

[0065] Unified data model: Defines the core metadata tags that all data must carry, forming the basis of data lineage: `scene_id`: A unique code for the erosion evolution scene (e.g., "C2-3A" represents point A in splash zone 3).

[0066] `pipeline_segment_id`: Pipeline segment identifier, associated with the GIS system.

[0067] `node_id`: The physical ID of the smart node.

[0068] `sensor_id`: The specific sensor number within the node.

[0069] `timestamp`: UTC timestamp of data acquisition, accurate to milliseconds.

[0070] `data_type`: Data type (such as `ENV_TEMP`, `EC_LPR`, `PHY_AE_EVENT`).

[0071] `data_quality_flag`: Data quality flag (0-good, 1-questionable, 2-invalid, and reason code) Definition of spatiotemporal index: At the database level, a composite index is created with `(scene_id, timestamp)` as the primary key and `(pipeline_segment_id, node_id)` as the secondary key. This allows all queries to be efficiently located and correlated based on "when, in what scenario, and at what location".

[0072] (1) Edge-side processing (lightweight, real-time): Protocol standardization: All sensor driver outputs are standardized to an internal intermediate data format (such as JSON or Protocol Buffers).

[0073] Streaming engine: Runs a lightweight streaming framework (such as ApacheEdgent or a custom state machine) and executes: Verification and cleaning: range verification, jump filtering.

[0074] Feature calculation: Real-time calculation of predefined time-domain / frequency-domain features.

[0075] Event detection: Generate primary events based on the rule engine (such as "high salt spray event" and "pitting activity event").

[0076] Adaptive sampling: The sampling rate is dynamically adjusted according to rules (reduced during quiet periods and increased during abnormal periods).

[0077] Data compression: Lossy compression (such as MP3 for audio, specific encoding for waveform) is used for waveform data (such as AE), and lossless or low-loss compression (such as Delta encoding) is used for numerical data.

[0078] (2) Cloud-based data aggregation and governance: Data access layer: Message queues (such as Apache Kafka) are used to receive edge data from various gateways at high concurrency, achieving decoupling and buffering.

[0079] ① Data cleaning and fusion layer: Deep cleaning: Utilizing cloud computing power for more complex cleaning, such as outlier detection based on statistical models (isolated forests) and conflict resolution based on multi-sensor consistency.

[0080] Spatiotemporal alignment: Data with different sampling frequencies are aligned to a unified time axis through interpolation or resampling, forming a multidimensional data cube indexed by `scene_id` and `timestamp`.

[0081] Missing value imputation: using spatiotemporal kriging interpolation of adjacent node data within the scene or prediction based on machine learning models to imput missing values.

[0082] ② Data standardization and enrichment: Convert all data units to International Standard Units (SI).

[0083] Add derived feature fields, such as "cumulative salt spray deposition", "number of wet and dry cycles", and "corrosion current integral".

[0084] Connect to external data sources (such as weather data from meteorological bureaus and tide tables) to enrich data dimensions.

[0085] (3) Specific implementation of cloud-edge collaborative hybrid time-series database system ① Storage architecture: Hot storage layer (time-series database): Employs a database specifically optimized for time series processing (such as InfluxDB or TimescaleDB) to store all timestamped raw data streams, cleaned data streams, and edge computing features. Its time-sharded and highly efficient compression features are ideal for handling massive amounts of monitoring data.

[0086] Warm storage layer (relational database): Uses PostgreSQL / MySQL to store all metadata, scenario definitions, pipeline static attributes, model parameters, early warning event records, maintenance work orders and other relational data.

[0087] Cold storage / data lake: Using Hadoop HDFS or object storage (such as Amazon S3) to archive unstructured or extremely large-scale data such as raw waveform data, high-definition inspection images, and microscopic characterization images for a long time.

[0088] ② Related query service: Develop a unified data query service (DataQueryService) that provides RESTful APIs or SQL-like query languages ​​to external users.

[0089] The service handles cross-database queries transparently at the underlying level. For example, if a user queries "get all synchronous corrosion current and acoustic emission events where salt spray concentration > threshold in a certain scenario over the past week", the service will:

[0090] Retrieve a list of `node_id` from the relational database.

[0091] Query the time series database for the corresponding `node_id`'s environmental data and corrosion response data.

[0092] The results are returned after time alignment and conditional filtering in memory or the compute engine.

[0093] Establish data version management to record the history of data cleaning and correction, ensuring that the analysis is reproducible.

[0094] S3, the standardized spatiotemporal sequence feature dataset is stored in the cloud-edge collaborative hybrid time series database. The cloud-edge collaborative hybrid time series database also stores pipeline attributes, corrosion scenario definitions and monitoring metadata. The two types of data are linked through spatiotemporal indexes to realize dynamic association storage of multidimensional data of environment-material-corrosion. The cloud-edge collaborative hybrid time-series database is an infrastructure capable of efficiently managing massive, multimodal, and strongly spatiotemporally correlated corrosion monitoring data. It addresses the core challenges faced by traditional databases in this field, such as high write throughput, complex query patterns, and sensitivity to storage costs. Standardized spatiotemporal sequence feature datasets are stored in the cloud-edge collaborative hybrid time-series database, which includes a time-series database and a relational database linked by a spatiotemporal index. The time-series database uses a time-partitioned supertable structure to store time-series monitoring data and performs columnar compression on historical partitions. The relational database stores pipeline attributes, corrosion scenario definitions, and monitoring metadata. The two databases achieve cross-database related queries through a unified query service.

[0095] Specifically, the cloud-edge collaborative hybrid time-series database adopts a layered, heterogeneous hybrid storage architecture to achieve a balance between performance, cost, and functionality.

[0096] (1) Edge caching and preprocessing layer (At theEdge): Technology selection: Industrial-grade SD cards or eMMC storage built into the nodes, and local time-series databases on the edge gateways (such as lightweight InfluxDB or SQLite with Timeseries extensions).

[0097] Data buffer: In the event of a network outage, cache at least 30 days of raw data to ensure that no data is lost.

[0098] Local aggregation: Aggregate high-frequency raw data according to preset rules (such as 5-minute average) to reduce the amount of data uploaded.

[0099] Event Log: Records all alarm events triggered by local rules and raw data snapshots for offline analysis. (2) Cloud Core Storage Layer (IntheCloud): ①Hot storage layer - time-series database cluster: Selection: TimescaleDB (a time-series extension based on PostgreSQL) is chosen as the core. Its advantages include support for complete SQL syntax, native compatibility with relational models, and powerful time partitioning and compression capabilities.

[0100] Table structure design: Main table: `sensor_readings` Core fields: `time` (TIMESTAMPTZ, primary key), `scene_id` (VARCHAR), `node_id` (VARCHAR), `sensor_type` (VARCHAR), `value` (DOUBLEPRECISION), `quality_flag` (INT).

[0101] Hypertable creation: Convert the `sensor_readings` table into a hypertable by the `time` column, and set a partitioning strategy by `time` (such as partitioning by day) to achieve automatic data sharding management and optimize the read and write performance of recent high-frequency data.

[0102] Compression and retention strategies: Automatically enable columnar compression for old partitions that have exceeded a certain period of time (e.g., 30 days), significantly reducing storage usage. Define data retention strategies (e.g., retain original high-frequency data for 1 year, and permanently retain daily statistical data).

[0103] ② Relational and Metadata Layer - Relational Databases: Option: PostgreSQL (from the same source as TimescaleDB, seamlessly integrated).

[0104] Core table: `pipeline_assets`: Pipeline asset information (material, coating, diameter, coordinates, commissioning date).

[0105] `monitoring_scenes`: Erosion scene definition (scene ID, description, start-end mileage, dominant mechanism).

[0106] `sensor_nodes`: Sensor node metadata (node ​​ID, location, deployment time, sensor list, calibration record).

[0107] `model_metadata`: Predictive model version, parameters, and performance metrics.

[0108] `maintenance_history`: Maintenance, inspection, and repair records.

[0109] ③ Cold Storage and Data Lake Layer - Object Storage: Selection: Object storage services such as Amazon S3 and Alibaba Cloud OSS.

[0110] Storage contents include: uncompressed raw high-frequency waveform files (acoustic emission, EIS full spectrum data), high-definition images and videos of UAV / robot inspections, laboratory microscopic characterization results (SEM, EDS spectral files), and regular full backups and archived data of the database.

[0111] It should be noted that the construction of the cloud-edge collaborative hybrid time-series database described in this embodiment is as follows: (1) At the logical level, all data is organized into a four-dimensional cube: Time × Space (Scene / Node) × Measurement Variable (SensorType) × Data Quality. This model provides a consistent data view for upper-layer applications. Whether querying historical trends at a certain point or comparing the status of different scenarios at the same time, it is possible to quickly slice, dice, and drill down on this cube.

[0112] (2) Related indexes and materialized views: Composite index creation: On the `sensor_readings` table in the time series database, in addition to time partitioning, create a composite index `(scene_id, node_id, sensor_type, time)` to accelerate range queries for specific locations and specific sensor types.

[0113] Materialized Views: Pre-compute and store commonly used aggregate query results, such as `daily_corrosion_rate_by_scene`: the average daily corrosion rate for each scene.

[0114] `environment_correlation_matrix`: The rolling correlation coefficient matrix among environmental factors.

[0115] These materialized views refresh periodically (e.g., hourly), greatly improving the loading speed of dashboards and frequently used reports.

[0116] (3) Data lifecycle and streaming pipeline Data Pipeline: Toolchain: Apache Kafka is used as the data bus, and Apache Flink or KSQL is used as the stream processing engine.

[0117] Processing flow: Ingestion: The edge gateway publishes data to the corresponding KafkaTopic (such as `topic-env-data`, `topic-ec-data`).

[0118] Real-time cleaning and transformation: Flink jobs consume Kafka data and perform real-time cleaning, unit conversion, and simple feature calculations (such as 1-minute sliding window average).

[0119] Multiple outputs (Sink): SinktoTimescaleDB: Writes cleaned structured data into a time-series database.

[0120] SinktoObjectStorage: Raw waveform data is written directly to object storage.

[0121] SinktoEventBus: When an abnormal event is detected, a message is published to the event bus (such as another topic in Kafka) to trigger the alert process.

[0122] (4) Query engine and computing services Unified Query Service: Encapsulate a RESTful API / GraphQL service to provide a unified data access point to the outside world.

[0123] The service internally parses query requests, which may be broken down into multiple subqueries targeting time-series databases, relational databases, and object storage, and then merges and assembles the results.

[0124] Example query: "Get the salt spray concentration and synchronized LPR value of all nodes in scenario C4 during the most recent typhoon (time range), and associate it with the coating type history of this pipe segment."

[0125] The service will: 1) retrieve a list of nodes from the relational database; 2) query environmental and corrosion data from the time-series database; 3) retrieve asset information from the relational database; and 4) return a merged JSON response.

[0126] Time series data computation engine: By leveraging TimescaleDB's built-in Continuous Aggregates and custom aggregation functions, time-series statistical features such as moving averages, Time Series Decomposition (STL), and similarity searches can be efficiently calculated directly at the database level, avoiding the need to pull massive amounts of data to the application layer for computation.

[0127] ⑤ Data security, governance and observability Data security: Encryption: Transport Layer TLS encryption; server-side encryption is supported for data at rest (object storage).

[0128] Access control: Role-based access control (RBAC), granular down to the table / row level (e.g., regional operations personnel can only access data within their jurisdiction).

[0129] Data governance: Data lineage: Recording information throughout the entire data lifecycle, from generation and processing to storage, to ensure traceability.

[0130] Data Quality Dashboard: Monitors the completeness and timeliness of data reporting from each node and generates a data health score.

[0131] System observability: Monitor database performance metrics (write latency, query QPS, storage growth), set alarms, and ensure stable system operation.

[0132] Step S4: Input the stored standardized spatiotemporal sequence feature dataset into the trained intelligent prediction model to obtain a corrosion risk cognitive map.

[0133] Specifically, the following steps are included: S41. On-site dynamic correlation and dominant factor analysis: Based on the historical and real-time standardized spatiotemporal sequence feature datasets stored in the cloud-edge collaborative hybrid time series database constructed in step S3, time-delay cross-correlation analysis and principal component analysis are used to analyze the dynamic coupling relationship between environmental variable sequences and corrosion response signals, quantify their causal and temporal correlation strength, and thus identify the key environmental factor combinations driving the corrosion process and their contribution weights under different corrosion evolution scenarios.

[0134] It should be noted that this step aims to initially extract statistical regularities and correlations at the data level from the massive data in the cloud-edge collaborative hybrid time series database, providing feature engineering guidance and hypothesis direction for the subsequent construction of intelligent prediction models based on mechanism-data fusion.

[0135] Multivariate time series association analysis: Methods: Time-delay cross-correlation analysis and Granger causality tests were used to quantify the dynamic correlation and time lag effect between environmental variables (such as salt spray concentration C, relative humidity RH, and temperature T) and corrosion response signals (such as corrosion current Icorr and noise resistance Rn). For example, the analysis showed that "when the daily average salt spray deposition increased, the linear polarization resistance value with a lag of 48 hours showed a significant decrease".

[0136] Dimensionality reduction and feature extraction: Principal component analysis (PCA) or t-SNE is applied to reduce the dimensionality of high-dimensional environmental parameters and identify key combinations of environmental factors (dominant factors) that can explain most of the data variance, such as "First principal component: marine atmospheric factor (high loading: Cl⁻, RH); Second principal component: wet-dry cycle factor (high loading: wet / dry cycle frequency, sunshine duration)".

[0137] Output: Generates a "Field Corrosion Driving Factor Analysis Report", which clarifies the key environmental stress factors under different corrosion scenarios and their empirical relationship with corrosion rate, providing a basis for accelerating the compilation of experimental spectra and the selection of model features.

[0138] S42. Laboratory Mechanism Mapping and Model Calibration: Based on the key environmental factor combinations analyzed in step S41 and the typical environmental load spectra obtained from field monitoring, the accelerated corrosion environment of multi-stress coupling is reproduced in the laboratory, and tests are conducted on samples of the same material. Combining microscopic characterization methods such as scanning electron microscopy, energy dispersive spectroscopy, and electrochemical impedance spectroscopy, the evolution law and failure microscopic mechanism of the corrosion product film are revealed. Based on this microscopic mechanism knowledge, the corrosion rate mechanism model used to interpret the field data is calibrated and corrected to improve its physical reliability.

[0139] This step aims to reveal the microscopic mechanisms behind macroscopic phenomena observed in field data through controlled laboratory experiments, and to inject physicochemical laws into data models.

[0140] (1) Accelerated test design based on field environmental spectrum: ① Environmental Spectrum Compilation: From the key environmental factor combinations driving the corrosion process under different corrosion evolution scenarios, characteristic environmental load spectra of typical corrosion scenarios are extracted. These characteristic environmental load spectra are then analyzed using data-driven methods to uncover the key environmental factors dominating the corrosion process and their dynamic effects. Based on this, standardized load sequences that reflect the essential characteristics of the field corrosion environment and are suitable for accelerated laboratory testing are refined and compiled. These sequences include not only single-factor time series but also emphasize multi-factor coupling relationships (such as the synergistic effect cycle of "high temperature-high humidity-high salinity").

[0141] ② Accelerated testing implementation: In an environmental simulation chamber or multi-factor coupled corrosion testing device, the above environmental spectrum is reproduced, and accelerated corrosion tests are conducted on samples of the same material as the actual pipeline. The electrochemical impedance spectroscopy (EIS) and potential responses of the samples are monitored simultaneously during the test and compared with the on-site monitoring signals.

[0142] (2) Microscopic mechanism analysis and mechanism model construction: ① Multi-scale characterization: Samples are taken for microscopic analysis at different stages of the experiment: ② Morphology and composition: The morphology, thickness, and cracks of the corrosion product film were observed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), and the elemental distribution was analyzed.

[0143] ③Phase analysis: X-ray diffraction (XRD) and Raman spectroscopy are used to determine the type of corrosion products (such as γ-FeOOH, α-FeOOH, Fe3O4) and clarify their protective properties.

[0144] ④ Electrochemical mechanism: The pitting sensitivity and passivation film semiconductor properties were studied through potentiodynamic polarization, Mott-Schottky analysis, etc.

[0145] ⑤ Mechanism Model Calibration: Based on the above analysis, establish or calibrate a corrosion rate mechanism model that reflects the corrosion evolution under this environment (such as a pitting corrosion model considering chloride ion competitive adsorption, or a rust layer resistance evolution model under alternating wet and dry conditions). This mechanism model will serve as a "physics teacher" to guide the training of subsequent data-driven models.

[0146] S43. Construction of a Hybrid Prediction Model Based on Mechanism-Data Fusion: A hybrid prediction model integrating field data patterns and laboratory corrosion rate mechanisms is constructed. The construction process includes: S431. Model Input and Preprocessing: The multidimensional time series data organized by spatiotemporal index after the treatment in step S2 is used as input and normalized. S432. Multi-task-physical information network architecture design: Design a neural network that includes a shared feature encoder and parallel task heads; the shared feature encoder adopts a temporal convolutional network or Transformer structure to extract spatiotemporal features; the parallel task heads include at least: a regression task head for predicting uniform corrosion depth / rate, a classification task head for outputting the probability of multiple failure modes such as pitting corrosion / stress corrosion cracking, and a survival analysis task head for generating the remaining lifetime probability distribution based on a deep learning survival analysis model. S433, Integration of Physical Constraints and Uncertainty Quantification: The electrochemical kinetic equation calibrated in step S42 is embedded as a physical constraint term in the model's loss function; at the same time, Monte Carlo Dropout technology is used in the neural network to achieve Bayesian inference, enabling the model to output the probability distribution and confidence interval of the predicted value, thus completing the quantification of the uncertainty of the prediction result. S434. Model Training and Output: The hybrid model is trained using historical data, and its final output is a structured "corrosion risk perception map". This map comprehensively describes the corrosion depth distribution, risk of each failure mode, contribution of dominant causes, and confidence interval of remaining life in future time periods in probabilistic form.

[0147] Specifically, the workflow for constructing a hybrid intelligent prediction model that integrates data, mechanisms, and uncertainties is as follows: ① Input layer and preprocessing: Input the multidimensional time series matrix X from the second target data, with dimensions [T×N], where T is the time step and N is the number of features (including environmental features and corrosion response features).

[0148] Normalization, missing value imputation (based on spatiotemporal correlation), and outlier smoothing are performed.

[0149] ②. Module A: Multi-task learning and hierarchical feature encoder: Shared Feature Encoder: Employs a Temporal Convolutional Network (TCN) or a Transformer Encoder as the backbone. TCN uses dilated causal convolutions to capture long temporal dependencies; Transformer uses a self-attention mechanism to dynamically measure the global correlation between different features at different time points.

[0150] Parallel task decoding head: 1) Regression Head (R): Implemented with a fully connected layer, it outputs the uniform corrosion depth increment ΔD and corrosion rate v within the future Δt time period.

[0151] 2) Classification Head (C): Employs a Softmax output layer to output multiple failure mode probability vectors [P_Uniformity, P_Pitting, P_SCC, P_Coating Failure].

[0152] 3) Survival analysis head (S): Using neural network survival analysis models such as DeepSurvivalMachines or Cox-Time, the probability density function f(t) and survival function S(t) of pipeline remaining lifetime T_remaining are output.

[0153] 4) Collaborative Training: The total loss function is a weighted sum: `L_total=λ1L_regression+λ2L_classification+λ3L_survival`. Through multi-task collaboration, the encoder is forced to extract robust features that are discriminative against various erosion outcomes.

[0154] ③Module B: Bayesian Deep Learning and Uncertainty Quantification Implementation: Monte Carlo Dropout (MCDropout) is used in some key fully connected or convolutional layers of the encoder and task head. That is, Dropout is kept on during both training and prediction, and multiple forward propagations are performed (e.g., T=100 times).

[0155] Probabilistic output: For regression tasks, the T prediction results are treated as a distribution, outputting the mean μ and standard deviation σ, such as `ΔD~N(μ, σ²)`. For classification tasks, the probability distribution of each category is output. This intuitively reflects the uncertainty of the model due to data noise and cognitive limitations.

[0156] ④ Module C: Physical Constraints and Mechanism Guidance: Physical information loss: Add a physical constraint term to the loss function. For example, the loss for a regression task can be modified as: `L_reg = MSE(ΔD_pred, ΔD_true) + βPhysics_Loss`. Here, `Physics_Loss` can be designed based on Faraday's law: `|ΔD_pred - (k∫Icorrdt)|`, encouraging that the predicted corrosion depth and the integral corrosion current (which can be estimated from the monitored LPR) are physically consistent.

[0157] Mechanism-guided attention: In the Transformer's self-attention mechanism, a learnable prior attention mask is introduced. For example, based on mechanistic knowledge ("chloride ions and humidity synergistically accelerate corrosion"), a matrix is ​​initialized so that the attention weights between Cl⁻ features and RH features have a higher initial bias, allowing the model to adaptively adjust accordingly.

[0158] ⑤ Module D: Interpretable Fusion and Contribution Analysis: Real-time contribution calculation: While the model is making predictions, the Integrated Gradients method is used to calculate the contribution of each input feature (such as the salt spray value at time t) to the current prediction result (such as the pitting probability at time t+Δt).

[0159] Visualization and Reporting: Generate feature contribution heatmaps and structured reports. For example: "In this high-risk warning, the cumulative contribution of salt spray deposition over the past 7 days is 52%, making it the most important factor; followed by continuous high humidity (contribution of 28%)."

[0160] ⑥. Output layer and corrosion risk perception map generation: The model's final output is not a single value, but a structured JSON-formatted cognitive graph, which includes: `prediction_window`: the prediction time period.

[0161] `corrosion_depth`: {`mean`: 0.25, `std`: 0.03, `unit`: "mm", `distribution`: "normal"}.

[0162] `failure_mode_risk`: {"pitting": 0.76, "scc": 0.12, ...}.

[0163] `dominant_factors`: [{"feature": "salt_deposition", "contribution": 0.45}, ...].

[0164] `remaining_life`: {"median": "8months", "pdf_params": ..., "safe_window_95%": ["6", "10months"]}.

[0165] `uncertainty_level`: "Low / Medium / High".

[0166] ⑦. Model training, validation, and deployment: Training strategy: Use time-series cross-validation to prevent data leakage. Use early stopping to prevent overfitting.

[0167] Continuous learning: After deployment, the system regularly uses new monitoring data and corresponding actual inspection results (such as excavation measurement data) as incremental data to perform online fine-tuning or periodic retraining of the model, so that the model can adapt to the slow changes in environment and material state.

[0168] It should be noted that this invention constructs not only a predictive model, but also a corrosion evolution simulator that integrates "data patterns and physical mechanisms," quantifies predictive uncertainty, and can explain its own decision-making basis. This provides a powerful digital tool for fundamentally understanding and proactively managing pipeline corrosion risks.

[0169] Step S5: Collect real-time monitoring data, combine the corrosion risk perception map, historical baseline and corrosion mechanism rules to conduct risk level assessment, trigger graded early warning and output risk-leading causes and maintenance decision suggestions.

[0170] The specific implementation process is as follows: Step S51: Establish an early warning judgment engine based on fuzzy reasoning and evidence theory, integrate real-time data streams, prediction maps, historical baselines and mechanism rules, and calculate the comprehensive risk confidence level.

[0171] Early warning judgment engine (intelligent judgment engine based on multi-source information fusion): (1) Input layer (four-dimensional data stream fusion): ① Real-time monitoring stream: Environmental and corrosion response characteristic data after edge processing (e.g., instantaneous salt spray concentration, galvanic current trend, acoustic emission event rate).

[0172] ② Predictive cognitive flow: "Corrosion risk cognitive map" from S4, including the probability distribution of corrosion depth in future time periods (such as the next 30 days) and the risk probability curves of each failure mode.

[0173] ③ Historical baseline flow: Historical statistical baselines (such as average corrosion rate, normal fluctuation range) and pipeline design safety thresholds (such as maximum allowable wall thickness reduction) at this location / similar scenarios.

[0174] ④ Mechanism rule flow: A formally expressed base of corrosion expert rules, for example: `IF (Cl⁻>50μg / cm² / day) AND (relative humidity>80% and duration>4h) THEN Pitting susceptibility coefficient = High` `IF (potential fluctuation > 100mV) AND (soil resistivity < 1000Ω·cm) THEN stray current risk level = Medium to High` (2) A hybrid judgment method combining fuzzy reasoning system and evidence theory (DS theory) is adopted. Step 1 (evidence generation): Real-time data, predicted probability, deviation from historical baseline, rule matching degree, etc. are transformed into a unified "evidence body" through membership function or confidence assignment function.

[0175] The second step (evidence fusion): Using the DS synthesis rules, evidence from different data sources is fused and calculated to obtain a comprehensive confidence level allocation for the "risk status".

[0176] Step 3 (Decision Output): Based on the comprehensive confidence level and combined with the preset warning level determination logic (see S5.2), output the determined warning level, risk type and confidence level.

[0177] Step S52: Based on the comprehensive risk confidence level and preset logic, trigger a tiered warning. Triggering a tiered warning includes: Level 1 warning is triggered when the risk confidence level is 30%-60% or the predicted trend continues to be unfavorable. Response measures include: interface marking, generating briefings, and enhanced monitoring. Level 2 warnings are triggered when the risk confidence level is 60% to 85% or the probability that the predicted indicator exceeds the safety threshold by 70% is greater than 80%. Response measures include issuing pre-maintenance work orders and initiating special inspections.

[0178] Among them, the "prediction index" refers to the quantitative risk parameters contained in the structured top-down risk cognition map output by the intelligent prediction model, which mainly includes: corrosion depth prediction value (such as uniform corrosion depth, pitting depth), corrosion rate prediction value, and the probability of occurrence of each failure mode (such as pitting corrosion, stress corrosion cracking).

[0179] Level 3 alarms are triggered when the risk confidence level is greater than 85% or when real-time data is severely exceeded. Response measures include: audible and visual alarms, emergency notifications, and activation of emergency plans.

[0180] Step S53: The root cause diagnosis module matches historical cases with mechanism rules to provide analysis of dominant causes; the strategy optimization module simulates and compares the costs and benefits of different maintenance schemes.

[0181] (1) Root cause diagnosis module: Case matching: The current risk pattern (feature vector) is compared with the historical case database to retrieve similarities and push the handling measures and effect evaluation reports of similar situations in the past.

[0182] Output: Clear diagnostic conclusions, such as: "The current high risk is mainly caused by stray current interference (contribution 55%) combined with seasonal high salt spray (contribution 30%). It is recommended to prioritize the investigation of the grounding system of nearby electrified railways."

[0183] (2) Strategy optimization and simulation module: Solution library: Includes multiple maintenance strategy models, such as "adjusting cathodic protection parameters", "reinforcing coating", "fixture repair", and "pipe section replacement".

[0184] Cost-benefit-risk assessment: Multi-objective simulation of feasible solutions. Inputs include: solution cost, estimated downtime, predicted risk reduction level, and estimated duration of effectiveness of measures.

[0185] Visual comparison: The trade-offs between various options are presented in chart form (such as a "cost-risk reduction curve") to assist managers in making informed decisions.

[0186] (3) Knowledge Continuous Learning and Optimization Module: Establish a closed-loop feedback chain of "early warning-response-effect".

[0187] After each early warning response is completed, the response measures, changes in monitoring data after implementation, and the final maintenance results (such as the actual corrosion depth measured during excavation) are fed back to the system as new knowledge samples.

[0188] Using this feedback data, the rule thresholds of the early warning judgment engine are automatically optimized periodically and used as incremental data to fine-tune the prediction model, enabling the system to have adaptive evolution capabilities.

[0189] It has achieved a complete intelligent closed loop from "risk perception" to "early warning decision-making" and then to "action feedback", completely transforming the traditional passive response and regular maintenance mode into a new mode of proactive, accurate and dynamic integrity management based on real-time status and forward-looking prediction.

[0190] To enable those skilled in the art to better understand the technical solutions of this application, the embodiments are described clearly and completely below. The described embodiments are only a part of this application, and not all of them.

[0191] Example: A crude oil pipeline with a diameter of Φ800mm laid along the coastline.

[0192] Sensing network deployment: Four typical scenarios were defined along the pipeline: splash zone (0m~50m), marine atmosphere zone (50m~200m), soil-atmosphere interface zone (200m~500m), and fully buried zone (>500m), with a total of 15 intelligent monitoring nodes deployed.

[0193] Data governance and storage: All data is indexed by “scenario-location-timestamp” and stored in a cloud-edge collaborative hybrid time-series database, and associated with attributes such as the material (X70 steel) and anti-corrosion coating (3LPE) of the pipe section.

[0194] Training and validation of intelligent prediction models: Analysis of historical data revealed that during the rainy season, the increased migration of chloride ions with moisture in the boundary area led to a significant increase in galvanic corrosion current.

[0195] The laboratory conducted accelerated tests using environmental spectrometry and confirmed through EIS and SEM that the corrosion product film under these conditions was loose and porous, with poor protective properties.

[0196] The physical information neural network model was trained using monitoring data from the first two years, and then validated using data from the third year. The validation results showed that the average absolute percentage error for predicting uniform corrosion depth was less than 15%, and the accuracy rate for early warning of pitting corrosion reached 88%.

[0197] Verification of the early warning effectiveness of the intelligent prediction model: Three months before the rainy season, the model predicts that there is an 85% probability that the pitting rate at a monitoring point in a certain border area will exceed the safety threshold within the next month, triggering a level two (early warning) signal.

[0198] The system recommended a treatment plan of "reinforcing the pipe section with a coating and fine-tuning the cathodic protection potential". After the maintenance personnel implemented the recommendation, they conducted an excavation and inspection after the rainy season that year. The measured maximum pitting depth was 0.28 mm, which fell within the model prediction range (0.25–0.32 mm), thus avoiding a potential leakage accident.

[0199] Example 2 Based on Example 1, this embodiment discloses a multi-dimensional corrosion risk intelligent early warning system for semi-buried pipelines, used to implement the aforementioned multi-dimensional corrosion risk intelligent early warning method for semi-buried pipelines, including: A distributed intelligent sensing network is deployed at multiple monitoring nodes in various corrosion evolution scenarios to collect multidimensional data on corrosion environment parameters and pipeline corrosion response, and to complete edge preprocessing and feature extraction of the raw data. The cloud-edge collaborative data governance platform communicates with the distributed intelligent sensing network and is used to clean, spatiotemporally fuse and standardize the received data to construct a standardized spatiotemporal sequence feature dataset. A cloud-edge collaborative hybrid time-series database is connected to the cloud-edge collaborative data governance platform to store standardized spatiotemporal sequence feature data, pipeline assets and scene metadata after governance, and to provide a unified association query service based on spatiotemporal index. The mechanism-data fusion intelligent modeling platform communicates with a cloud-edge collaborative hybrid time-series database to build and train an intelligent prediction model based on mechanism-data fusion, and generate a corrosion risk cognitive map. The intelligent early warning and decision support application platform is connected to the mechanism-data fusion intelligent modeling platform. It is used to receive corrosion risk cognitive map, integrate real-time monitoring data, historical baseline and mechanism rules, trigger graded early warning through multi-level early warning judgment engine, and provide risk root cause diagnosis and maintenance decision suggestions. Among them, the distributed intelligent sensing network, cloud-edge collaborative data governance platform, cloud-edge collaborative hybrid time-series database, mechanism-data fusion intelligent modeling platform and intelligent early warning and decision support application platform are cascaded in sequence to form a closed-loop data processing and decision support chain.

[0200] The above-disclosed embodiments are merely preferred embodiments of the present invention. However, the embodiments of the present invention are not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A multi-dimensional intelligent early warning method for corrosion risk of semi-buried pipelines, characterized in that, Includes the following steps: Typical corrosion evolution scenarios are divided along the semi-buried pipeline according to the characteristics of corrosion environment gradient. Monitoring nodes are deployed in each scenario to collect environmental parameters and multi-dimensional data of corrosion response synchronously and continuously. The collected raw environmental parameters and corrosion response multidimensional data are assigned metadata tags containing data source identifiers and timestamps. After edge preprocessing and feature extraction, the data is uploaded to the cloud and then processed in the cloud to form a standardized spatiotemporal sequence feature dataset. Standardized spatiotemporal sequence feature datasets are stored in a cloud-edge collaborative hybrid time series database. The cloud-edge collaborative hybrid time series database also stores pipeline attributes, corrosion scenario definitions, and monitoring metadata. The two types of data are linked through a spatiotemporal index to achieve dynamic association and storage of multidimensional environmental-material-corrosion data. Input the stored standardized spatiotemporal sequence feature dataset into the trained intelligent prediction model to obtain a corrosion risk cognitive map; Real-time monitoring data is collected, and risk level assessment is performed by combining the corrosion risk perception map, historical baseline, and corrosion mechanism rules. This triggers graded early warnings and outputs the main risk factors and maintenance decision recommendations.

2. The intelligent early warning method for multi-dimensional corrosion risk of semi-buried pipelines according to claim 1, characterized in that, The process of forming a standardized spatiotemporal sequence feature dataset includes the following steps: The received data is cleaned and repaired in the cloud, and then the heterogeneous data from different monitoring nodes are time-aligned and spatially interpolated and fused using the corrosion evolution scenario and timestamp as the core index to form a standardized spatiotemporal sequence feature dataset.

3. The intelligent early warning method for multi-dimensional corrosion risk of semi-buried pipelines according to claim 2, characterized in that, The cloud-edge collaborative hybrid time-series database includes a time-series database and a relational database linked by a spatiotemporal index. The time-series database stores streaming sensor data, which includes environmental parameters, corrosion response-related dynamic data, and standardized spatiotemporal sequence feature datasets after edge preprocessing and cloud cleaning. The relational database stores pipeline attribute data, corrosion scenario definition data, and monitoring metadata. The pipeline attribute data includes pipeline material and anti-corrosion coating type. The corrosion scenario definition data includes the classification criteria and corrosion characteristics of each typical scenario. The monitoring metadata includes metadata tags of data source identifiers and timestamps. Both databases are linked for cross-database queries through a unified query service.

4. The intelligent early warning method for multi-dimensional corrosion risk of semi-buried pipelines according to claim 3, characterized in that, The construction process of the intelligent prediction model includes the following steps: Based on historical and real-time monitoring data stored in a cloud-edge collaborative hybrid time-series database, time-delay cross-correlation analysis and principal component analysis are used to analyze the dynamic coupling relationship between environmental variable sequences and corrosion response signals, and to identify key environmental factor combinations and their contribution weights under different corrosion evolution scenarios. Based on the combination of key environmental factors and the field environmental load spectrum, the multi-stress coupled accelerated corrosion environment was reproduced in the laboratory and tests were carried out on samples of the same material. The corrosion mechanism was revealed by combining microscopic characterization methods to calibrate and correct the corrosion rate mechanism model. A mechanism-data fusion intelligent prediction model is constructed based on the calibrated corrosion rate mechanism model. The mechanism-data fusion intelligent prediction model is trained using historical data. The normalized standardized spatiotemporal sequence feature dataset is input into the trained mechanism-data fusion intelligent prediction model, and a corrosion risk cognitive map is output.

5. The intelligent early warning method for multi-dimensional corrosion risk of semi-buried pipelines according to claim 4, characterized in that, The mechanism-data fusion intelligent prediction model adopts a multi-task-physical information network architecture. This model takes a standardized spatiotemporal sequence feature dataset as input and performs normalization processing on the input data. The mechanism-data fusion intelligent prediction model includes a shared feature encoder and parallel task heads. The shared feature encoder adopts a temporal convolutional network or Transformer structure. The parallel task heads include at least a regression task head for predicting corrosion depth, a classification task head for outputting the probability of multiple failure modes, and a survival analysis task head for generating the probability distribution of pipeline remaining life. During model training, electrochemical kinetic equations are embedded in the loss function as physical constraints. At the same time, Monte Carlo Dropout technology is used to achieve Bayesian inference, so that the probability distribution and confidence interval of the predicted values ​​are output after model training. Finally, a corrosion risk cognitive map containing corrosion depth distribution, multi-failure mode risk, contribution of dominant inducing factors, and remaining life confidence interval is output.

6. The intelligent early warning method for multi-dimensional corrosion risk of semi-buried pipelines according to claim 1, characterized in that, The process of conducting risk level assessment, triggering tiered early warnings, and outputting risk-leading causes and maintenance decision recommendations includes the following steps: Establish an early warning and judgment engine based on fuzzy reasoning and evidence theory; The engine inputs real-time monitoring data, corrosion risk perception map, historical baseline data and preset mechanism rules to calculate the comprehensive risk confidence level. Based on the range of the comprehensive risk confidence level, the probability of the predicted indicators exceeding the safety threshold, the changes in risk trends, and the extent of exceeding limits in real-time monitoring data, corresponding level 1, level 2, and level 3 warnings are triggered. After an early warning is triggered, the system performs a risk root cause diagnosis based on the contribution of risk characteristics, corrosion mechanism rules, and historical cases. It also conducts cost-benefit simulations to compare different maintenance strategies and outputs anti-corrosion maintenance decision recommendations.

7. The intelligent early warning method for multi-dimensional corrosion risk of semi-buried pipelines according to claim 6, characterized in that, The Level 1 warning is triggered when the overall risk confidence level is 30% to 60% or the risk prediction trend is unfavorable, and measures such as interface marking and enhanced monitoring are taken. The Level 2 warning is triggered when the overall risk confidence level is 60% to 85% or the probability of exceeding the safety threshold is >80%, and measures such as issuing pre-maintenance work orders and special inspections are taken. A Level 3 warning is triggered when the overall risk confidence level is greater than 85% or when real-time data seriously exceeds the limit, and measures such as audible and visual alarms and activation of emergency response plans are taken.

8. The intelligent early warning method for multi-dimensional corrosion risk of semi-buried pipelines according to claim 1, characterized in that, The typical corrosion evolution scenarios include at least three of the following: marine atmosphere zone, splash zone, tidal zone, soil-atmosphere interface zone, fully buried zone, and key heterogeneous connection point.

9. A multi-dimensional corrosion risk intelligent early warning system for semi-buried pipelines, used to implement the multi-dimensional corrosion risk intelligent early warning method for semi-buried pipelines as described in any one of claims 1 to 8, characterized in that, include: A distributed intelligent sensing network is deployed at multiple monitoring nodes in various corrosion evolution scenarios to collect multidimensional data on corrosion environment parameters and pipeline corrosion response, and to complete edge preprocessing and feature extraction of the raw data. The cloud-edge collaborative data governance platform communicates with the distributed intelligent sensing network and is used to clean, spatiotemporally fuse and standardize the received data to construct a standardized spatiotemporal sequence feature dataset. A cloud-edge collaborative hybrid time-series database is connected to the cloud-edge collaborative data governance platform to store standardized spatiotemporal sequence feature data, pipeline assets and scene metadata after governance, and to provide a unified association query service based on spatiotemporal index. The mechanism-data fusion intelligent modeling platform communicates with the cloud-edge collaborative data governance platform to build and train an intelligent prediction model based on mechanism-data fusion, and generate a corrosion risk cognitive map. The intelligent early warning and decision support application platform communicates with the mechanism-data fusion intelligent modeling platform to receive corrosion risk cognitive maps, integrate real-time monitoring data, historical baselines and mechanism rules, trigger graded early warnings through a multi-level early warning judgment engine, and provide risk root cause diagnosis and maintenance decision suggestions.

10. The intelligent early warning system for multi-dimensional corrosion risk of semi-buried pipelines according to claim 9, characterized in that, The monitoring node includes an environmental sensing unit, a corrosion response sensing unit, and an edge intelligent processing unit. The environmental sensing unit is used to collect multi-dimensional corrosion environment parameters under pipeline service scenarios. The corrosion response sensing unit is used to capture electrochemical and physical corrosion response signals of pipeline materials. The edge intelligent processing unit is used to perform local filtering, feature extraction, cache storage, and preliminary anomaly diagnosis based on preset rules on the collected raw data.

Images