A pipeline intelligent monitoring and early warning system and method based on multi-source data fusion and flow rate accelerated corrosion mechanism

Abstract

The application relates to the technical field of industrial pipeline safety monitoring, and provides a pipeline intelligent monitoring and early warning system and method based on multi-source data fusion and a flow speed accelerated corrosion mechanism.The system comprises a field perception layer, a data transmission layer, a cloud intelligent layer and a control linkage layer; wherein the field perception layer is used for synchronously collecting multi-parameter data such as pipeline wall thickness, fluid flow speed and temperature; the cloud intelligent layer establishes a multi-parameter coupling prediction model based on the flow speed accelerated corrosion mechanism, dynamically calculates a corrosion speed and evaluates a predicted residual safety time; and the control linkage layer sends structured early warning information to a distributed control system through a standardized industrial interface to trigger corresponding process control responses.Through the combination of multi-source data fusion and mechanism modeling, the application realizes real-time perception, accurate prediction and closed-loop control of the pipeline corrosion state, solves the problems of single monitoring dimension, insufficient prediction capability and system isolated operation in the prior art, and significantly improves the safety and intelligent level of pipeline operation.

Description

Technical Field

[0001] This invention relates to the field of industrial pipeline safety monitoring and integrity management technology, specifically to a pipeline intelligent monitoring and early warning system and method based on multi-source data fusion and flow velocity-accelerated corrosion mechanism, belonging to the intersection of non-destructive testing technology, industrial Internet of Things technology, and predictive maintenance technology. Background Technology

[0002] In industries such as power, petrochemicals, and nuclear energy, high-temperature and high-pressure fluid transportation pipelines are critical infrastructure, and their long-term safe and stable operation is of great significance to ensuring the reliability of production systems. Under such conditions, pipeline materials are constantly exposed to the combined effects of high-speed flowing media and complex chemical environments, making them prone to flow-accelerated corrosion (FAC). FAC is a localized thinning process caused by the coupling of fluid scouring and electrochemical corrosion. Essentially, the protective oxide film on the inner wall of the pipeline continuously dissolves or peels off under the action of fluid, exposing the base metal and causing corrosion. This leads to a gradual reduction in pipeline wall thickness, and in severe cases, may cause leaks or even pipe rupture accidents.

[0003] The occurrence and development of corrosion-associated corrosion (FAC) are influenced by a combination of factors, including fluid velocity, fluid temperature, water chemistry (such as pH and dissolved oxygen content), and pipe material composition. Fluid velocity is the primary driver of corrosion rate; increased velocity significantly accelerates oxide film peeling. Temperature alters corrosion behavior by affecting reaction kinetics and oxide film stability. Water quality parameters further influence the corrosion process by regulating the electrochemical environment. Additionally, flow field disturbances caused by local pipe structures (such as elbows and tees) can also exacerbate localized corrosion. Therefore, FAC is characterized by high corrosion rates, insidious development, and sudden onset, posing a significant threat to industrial pipeline safety.

[0004] To address the aforementioned issues, existing technologies typically employ periodic shutdown inspections or random checks of pipelines using handheld thickness gauges to obtain information on wall thickness variations. However, these methods suffer from long inspection cycles, limited coverage, and an inability to reflect real-time corrosion dynamics, making it difficult to promptly identify areas of accelerated corrosion. With the development of online monitoring technology, some systems have introduced electromagnetic ultrasonic thickness measurement to achieve continuous wall thickness monitoring. However, these systems usually only acquire single-layer wall thickness data, lacking simultaneous acquisition of key corrosion-inducing parameters such as flow velocity and temperature, making it difficult to achieve analysis at the corrosion mechanism level.

[0005] In addition, most existing online monitoring systems rely on simple threshold alarms or trend extrapolation methods for risk assessment, failing to introduce physicochemical mechanism models of flow rate-accelerated corrosion. This results in limited predictive capabilities and an inability to accurately quantify the remaining service life of pipelines, making maintenance decisions still mainly dependent on experience and subject to significant uncertainty.

[0006] Furthermore, existing monitoring systems typically operate as independent subsystems and lack an effective data interaction mechanism with the factory's distributed control system (DCS). Early warning information needs to be transmitted manually and cannot directly drive adjustments to process parameters, making it difficult to form a closed-loop management model from monitoring and analysis to control response. This limits their application effectiveness in practical engineering.

[0007] Therefore, how to provide a pipeline intelligent monitoring and early warning technology that can achieve synchronous acquisition of multi-source parameters, intelligent prediction based on corrosion mechanism, and proactive intervention in conjunction with the control system has become an urgent technical problem to be solved in this field. Summary of the Invention

[0008] In view of this, the purpose of this invention is to provide a pipeline intelligent monitoring and early warning system based on multi-source data fusion and flow velocity-accelerated corrosion mechanism, so as to solve the problems of single monitoring dimension, lack of mechanism model support, insufficient prediction capability and inability to link with control system in the existing technology, and realize real-time perception, accurate prediction and closed-loop control of pipeline corrosion status.

[0009] To achieve the above objectives, the present invention provides the following technical solution: In one possible implementation, a pipeline intelligent monitoring and early warning system based on multi-source data fusion and velocity-accelerated corrosion mechanism includes: The field sensing layer includes multi-parameter intelligent monitoring nodes deployed at key parts of the pipeline; each monitoring node integrates: an electromagnetic ultrasonic thickness measurement module for continuously measuring the pipeline wall thickness; a flow velocity sensor for real-time measurement of the fluid flow velocity inside the pipeline; and a temperature sensor for real-time measurement of the fluid temperature. The cloud-based intelligent layer includes an intelligent analysis server and a data processing program running on it. The data processing program is used to receive and integrate wall thickness, flow rate and temperature data from the field sensing layer, establish a corrosion rate prediction model based on the flow rate-accelerated corrosion mechanism coupled with multiple parameters such as flow rate and temperature, dynamically calculate and predict the corrosion rate, and calculate and predict the remaining safe time based on the current measured wall thickness and the preset limit safe wall thickness. The control linkage layer establishes a communication connection with the factory's distributed control system through a standardized industrial communication interface. It is used to send a structured early warning message containing at least the predicted remaining safety time to the distributed control system, so that the distributed control system triggers the corresponding process control response, thereby forming a closed-loop linkage of corrosion monitoring, analysis, early warning and control.

[0010] In one possible implementation, the corrosion rate prediction model satisfies the following expression:

[0011] Where CR is the predicted corrosion rate, K is the overall rate constant, and V is the fluid velocity. Where is the flow rate index, Q is the apparent activation energy, R is the ideal gas constant, T is the absolute temperature of the fluid, and M is the material factor.

[0012] In one possible implementation, the cloud-based intelligent layer is used to adaptively update the model parameters of the corrosion rate prediction model based on historical monitoring data, so that the model parameters are dynamically adjusted as the operating conditions change.

[0013] In one possible implementation, the adaptive update of the model parameters is achieved through a parameter fitting algorithm based on time series data.

[0014] In one possible implementation, the corrosion rate prediction model further includes a water quality influence function f(pH) to characterize the effect of fluid pH on the corrosion rate; the monitoring nodes of the field sensing layer also include pH sensors or electrochemical noise sensors.

[0015] In one possible implementation, the predicted remaining safety time is calculated based on the relationship between the current measured wall thickness, the preset limit safety wall thickness, and the predicted corrosion rate; the cloud-based intelligent layer is used to generate a warning event of a corresponding level based on the comparison result between the predicted remaining safety time and the preset multi-level warning threshold.

[0016] In one possible implementation, the structured early warning message may also include one or more of the following information: a unique identifier for the monitoring point, a description of the geographical location, the current measured wall thickness, the analysis results of the dominant corrosion influencing factors, and a suggested range of operating parameters based on the model.

[0017] In one possible implementation, the standardized industrial communication interface includes an OPC UA interface and / or a Modbus TCP interface; the distributed control system performs at least one of the following control responses based on the structured early warning message: generating an alarm signal, outputting a process control interface, or outputting control commands for adjusting fluid flow rate, switching pipelines, or adjusting fluid chemical parameters.

[0018] In one possible implementation, the monitoring node further includes a local processing unit and a wireless transmission unit, wherein the wireless transmission unit employs an NB-IoT communication module or a LoRa communication module; the system further includes a data transmission layer, which is used to cooperate with a gateway through an NB-IoT network or a LoRa network to upload the data of the monitoring node to the cloud intelligent layer.

[0019] In one possible implementation, the electromagnetic ultrasonic thickness measurement module uses a non-contact electromagnetic ultrasonic transducer and is fixed to the outer wall of the pipe by a clamp structure; the monitoring node uses a hybrid power supply method of battery and solar panel.

[0020] In one possible implementation, a closed-loop control method for pipeline corrosion based on the above system includes: a sensing step: synchronously collecting pipeline wall thickness, fluid flow velocity, and fluid temperature data through the same monitoring node; a prediction step: fusing and processing the collected data in a cloud-based intelligent layer, calculating the predicted corrosion rate based on a flow velocity-accelerated corrosion mechanism model, and then calculating the predicted remaining safe time; a decision-making step: generating a structured early warning message when the predicted remaining safe time meets preset early warning conditions; and a control step: sending the structured early warning message to a distributed control system through a standardized industrial communication interface, so that the distributed control system executes the corresponding process control response.

[0021] In one possible implementation, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the prediction and decision steps in the method described above.

[0022] Based on the above technical solutions, the intelligent pipeline monitoring and early warning system of the present invention deploys multi-parameter intelligent monitoring nodes integrating electromagnetic ultrasonic thickness measurement, flow velocity and temperature monitoring at key parts of the pipeline, and combines them with a multi-parameter coupled prediction model based on the flow velocity-accelerated corrosion mechanism in the cloud intelligent layer to dynamically calculate the corrosion rate and further predict the remaining safe time. At the same time, it sends structured early warning information to the distributed control system through a standardized industrial communication interface, thereby realizing the closed-loop linkage of corrosion state perception, intelligent prediction and control response. This solves the problems of single monitoring dimension, lack of mechanism model support, insufficient prediction capability and inability to link with the control system in the prior art.

[0023] Furthermore, by simultaneously collecting and fusing the corrosion result parameter of wall thickness with corrosion inducing parameters such as flow rate and temperature, the correlation model of "result-cause" of corrosion behavior is realized, so that the corrosion state can not only be detected, but also interpreted and traced, thereby significantly improving the accuracy and reliability of corrosion assessment.

[0024] Furthermore, by introducing a prediction model based on the corrosion mechanism accelerated by flow rate and dynamically and adaptively updating the model parameters by combining historical data, the model can continuously adapt to changes in actual operating conditions, achieving high-precision prediction of corrosion rate. This avoids the prediction errors caused by traditional methods based on experience or simple trend extrapolation, and improves the scientificity and stability of the prediction results.

[0025] Furthermore, by using the predicted remaining safe time as the core output indicator, the corrosion status is transformed from a qualitative judgment of "whether it exceeds the limit" to a quantitative assessment of "remaining life". This provides a clear and quantifiable basis for decision-making regarding equipment maintenance cycle formulation, risk classification management, and preventive maintenance, thereby effectively reducing the risk of over-maintenance or delayed maintenance.

[0026] Furthermore, by constructing structured early warning messages and connecting them with a standardized interface of the distributed control system, the early warning information can directly drive the control system response, transforming the traditional passive alarm mode that relies on manual judgment and transmission into an automated and proactive prevention and control mode, which significantly improves the system response speed and safety handling efficiency.

[0027] Furthermore, by controlling the linkage layer to output control commands or operation suggestions to the distributed control system, the system can automatically or assistedly adjust the flow rate, switch operating pipelines, or optimize fluid chemical parameters according to the dominant corrosion factors, thereby inhibiting corrosion development from the source and achieving an upgrade from "monitoring and early warning" to "proactive intervention".

[0028] Furthermore, by adopting non-contact electromagnetic ultrasonic thickness measurement technology and a wireless transmission and hybrid power supply scheme, the system can operate stably for a long time in high temperature, high pressure and complex industrial environments, with good adaptability and deployment flexibility, and reduced maintenance costs.

[0029] Furthermore, through the overall closed-loop architecture of "perception-analysis-decision-control", the digitalization, intelligence and integration of pipeline corrosion management have been realized, transforming pipeline operation from the traditional post-maintenance mode to predictive maintenance and proactive safety protection, and improving the overall operational safety, economy and management level of industrial pipeline systems. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only a part of the embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1This is a schematic diagram of the overall architecture of a pipeline intelligent monitoring and early warning system based on multi-source data fusion and flow velocity-accelerated corrosion mechanism according to the present invention. The diagram shows the overall structural relationship of the system, which consists of a field perception layer, a data transmission layer, a cloud intelligence layer, and a control linkage layer, as well as the data flow and interaction relationship between each layer.

[0032] Figure 2 This is a schematic diagram of the workflow for corrosion rate prediction and early warning generation based on the flow rate accelerated corrosion mechanism in this invention. The diagram shows the overall processing flow from multi-source data acquisition, data fusion processing, mechanism model calculation to predict corrosion rate and remaining safety time calculation, to early warning judgment and generation of structured early warning messages.

[0033] Explanation of reference numerals in the attached figures: 100—Field Sensing Layer; 110—Multi-parameter Intelligent Monitoring Node; 111—Electromagnetic Ultrasonic Thickness Measurement Module; 112—Flow Rate Sensor; 113—Temperature Sensor; 114—pH Sensor; 115—Local Processing Unit; 116—Wireless Transmission Unit; 200—Data Transmission Layer; 210—Gateway; 300—Cloud Intelligent Layer; 310—Intelligent Analysis Server; 3101—Data Management Module; 3102—Model Calculation Module; 3103—Parameter Self-Learning Module; 3104—Lifetime Assessment Module; 3105—Early Warning Decision Module; 400—Control and Linkage Layer; 410—Communication Interface Module; 420—Control and Interaction Module. Detailed Implementation

[0034] To enable those skilled in the art to more clearly understand the technical solution of the present invention, the specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. In the following description, the system structure, functional modules and their working process of the present invention are illustrated through several specific embodiments. However, it should be understood that these embodiments are only used to explain the technical solution of the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0035] It should be noted that, where there is no conflict, the various embodiments and features described in this specification can be combined with each other. For parts not described in detail, conventional techniques in the art can be used. Furthermore, the structures shown in the accompanying drawings are merely illustrative, used to illustrate the logical relationships between the components, and not to limit the actual structural dimensions or proportions.

[0036] I. System Overall Structure and Implementation Method In one specific implementation, such as Figure 1As shown, the intelligent pipeline monitoring and early warning system based on multi-source data fusion and flow velocity-accelerated corrosion mechanism provided by this invention adopts a layered architecture design, including a field perception layer 100, a data transmission layer 200, a cloud intelligence layer 300, and a control linkage layer 400. The layers interact and coordinate functions through standardized interfaces and communication networks, thereby constructing a complete closed-loop system of "perception-transmission-analysis-decision-control".

[0037] The field sensing layer 100 is deployed at key locations in the industrial pipeline system to achieve real-time sensing of pipeline corrosion status and its inducing factors; the data transmission layer 200 is used to carry stable transmission of field data to the cloud; the cloud intelligence layer 300 serves as the core processing unit of the system, used to complete data fusion, corrosion modeling, predictive analysis, and early warning generation; and the control linkage layer 400 is used to interface the analysis results with the industrial control system to achieve proactive regulation of the pipeline's operating status.

[0038] Specifically, the functional relationships between the layers are as follows: The wall thickness, flow velocity, and temperature data collected by the field sensing layer 100 are uploaded to the cloud intelligence layer 300 via the data transmission layer 200. The cloud intelligence layer 300 performs unified management and analysis on the received data, and outputs key evaluation indicators including predicted corrosion rate and predicted remaining safe time. When the predicted remaining safe time meets the preset early warning conditions, the cloud intelligence layer 300 generates a structured early warning message and sends it to the distributed control system via the control linkage layer 400. The distributed control system executes the corresponding control strategy according to the early warning message, thereby realizing proactive intervention in corrosion risk.

[0039] Furthermore, the overall system architecture has the following characteristics: 1. Hierarchical decoupling design Each functional layer is relatively independent and connected through standardized interfaces, giving the system good scalability and compatibility, making it easy to deploy and apply in different industrial scenarios; 2. Collaborative processing of multi-source data By integrating wall thickness monitoring and operating parameter monitoring within the same system framework, data can be fused and processed on a unified platform, providing a complete data foundation for corrosion mechanism modeling. 3. Centralized cloud-based analysis capabilities Deploying complex corrosion mechanism calculations and model update tasks on the cloud-based intelligent layer 300 helps improve computing power and model accuracy, and supports centralized management of multiple monitoring points. 4. Deep linkage of control systems By integrating the control linkage layer 400 with the distributed control system, the monitoring system not only has the function of information output, but also the ability to drive control response, realizing closed-loop operation from information perception to execution control. 5. Modular deployment capability Each layer can be flexibly configured according to the actual application scenario. For example, the data transmission method can be adjusted in scenarios with limited network conditions, or a cloud-based intelligent layer can be deployed locally to meet real-time requirements.

[0040] Through the above structural design, this implementation method realizes full-process management of the corrosion status of industrial pipelines, enabling the system to expand from traditional single-point monitoring to multi-parameter fusion analysis, and further extend to prediction and control, effectively improving the system's intelligence level and engineering applicability.

[0041] II. Implementation Methods of the On-Site Perception Layer In one specific implementation, such as Figure 1 As shown, the field sensing layer 100 serves as the data acquisition front end of the system of the present invention, used for real-time, continuous, and synchronous monitoring of pipeline corrosion status and its key influencing factors. The field sensing layer 100 includes multiple multi-parameter intelligent monitoring nodes 110, each of which is distributed along the pipeline at locations with drastic flow velocity changes or high corrosion risk, including but not limited to elbows, tees, diameter transition sections, throttling areas, and high-temperature and high-pressure operating sections.

[0042] Furthermore, each of the multi-parameter intelligent monitoring nodes 110 adopts an integrated design, including an electromagnetic ultrasonic thickness measurement module 111, a flow velocity sensor 112, a temperature sensor 113, a local processing unit 115, and a wireless transmission unit 116, thereby realizing the synchronous acquisition of "corrosion result parameters" and "corrosion cause parameters".

[0043] The electromagnetic ultrasonic thickness measurement module 111 is a non-contact measuring device. It uses the principle of electromagnetic acoustic transduction to excite ultrasonic guided waves or bulk wave signals within the pipe wall and receives the reflected signals to calculate the pipe wall thickness. This module is fixed to the outer wall of the pipe using a specialized clamp structure, eliminating the need for coupling agents. It can adapt to high temperatures, coatings, or complex surface conditions, making it suitable for long-term online monitoring. Through periodic or continuous sampling, the trend of wall thickness changes can be obtained, thus reflecting the corrosion development process.

[0044] The flow velocity sensor 112 is used to acquire the flow velocity information of the fluid in the pipeline in real time. It can be a vortex flow meter, ultrasonic flow meter, or other flow velocity measurement device suitable for high temperature and high pressure conditions. Flow velocity is the main driving factor for velocity-accelerated corrosion, and its measurement accuracy directly affects the subsequent corrosion model calculation results.

[0045] The temperature sensor 113 is used to measure the fluid temperature and can be a high-precision temperature detection element such as a thermocouple or a resistance temperature detector. Temperature data reflects the kinetic conditions of the corrosion reaction and is an important input parameter in the corrosion rate prediction model.

[0046] In some alternative embodiments, the monitoring node 110 may further include a pH sensor 114 or an electrochemical noise sensor to acquire information about the chemical environment of the fluid, thereby further enhancing the ability to characterize the corrosion mechanism.

[0047] Furthermore, the local processing unit 115 includes a microprocessor and a signal conditioning circuit, which is used to filter, amplify, convert analog to digital and encapsulate the raw signals collected by each sensing module, and can perform preliminary anomaly detection or data compression operations to improve data transmission efficiency and reliability.

[0048] The wireless transmission unit 116 is used to send the processed data to the data transmission layer 200. Its communication method can be selected from low-power wide-area communication technologies such as NB-IoT or LoRa, depending on the deployment scenario. In a wide-area distribution scenario, the monitoring node can directly access the cloud via the NB-IoT network; in a locally dense deployment scenario, the monitoring node can communicate with the nearest gateway via the LoRa protocol.

[0049] Furthermore, in terms of power supply, the monitoring node 110 adopts a low-power design and is equipped with a hybrid power supply unit of batteries and solar panels to meet the requirements of long-term unattended operation. In high-temperature or shaded environments, a high-temperature resistant power supply or an external power supply interface can also be configured as needed.

[0050] Furthermore, to ensure the accuracy and consistency of measurement data, each sensor module is calibrated at the factory or during installation, and can be remotely calibrated or compensated by sending parameters through the cloud during operation.

[0051] Through the above structural design, the field sensing layer 100 can realize multi-dimensional, synchronous, and highly reliable acquisition of pipeline corrosion status and its key influencing factors, providing a high-quality data foundation for subsequent corrosion mechanism modeling and predictive analysis, and also providing support for the interpretation of corrosion process analysis.

[0052] III. Implementation of Data Transmission Layer In one specific implementation, such as Figure 1 As shown, the data transmission layer 200 is located between the field perception layer 100 and the cloud intelligence layer 300. It is used to realize the reliable collection, aggregation and remote transmission of monitoring node data, and is a key communication bridge connecting field equipment and the upper-level intelligent analysis platform.

[0053] The data transmission layer 200 can adopt various communication architectures, including wide area direct connection mode and regional aggregation mode, depending on the actual deployment environment and communication conditions, to adapt to the network coverage requirements and data transmission requirements in different industrial scenarios.

[0054] In one implementation, the data transmission layer 200 adopts a wide-area direct connection mode. In this mode, each monitoring node 110 directly connects to the operator's network through its built-in NB-IoT communication module and uploads the collected data such as wall thickness, flow rate, and temperature to the cloud intelligent layer 300 via the cellular network. This method eliminates the need for additional local gateway deployment and has the advantages of wide coverage, flexible deployment, and low maintenance costs, making it suitable for scenarios with wide pipeline distribution and dispersed nodes.

[0055] In another implementation, the data transmission layer 200 adopts a regional aggregation mode. In this mode, each monitoring node 110 communicates with the gateway 210 located on-site via the LoRa wireless communication protocol. Data from multiple nodes is centrally aggregated and preliminarily processed at the gateway 210 before being uploaded to the cloud-based intelligent layer 300 via Ethernet, 4G / 5G mobile communication networks, or industrial fiber optic networks. This method is suitable for application scenarios with dense deployment within the factory area, complex wireless signal environments, or high real-time requirements for data transmission.

[0056] Furthermore, the data transmission layer 200 supports multiple communication protocols and data format conversion functions to achieve interconnection and interoperability between different devices. For example, in a LoRa network, nodes use a low-power short message communication protocol, while when communicating with the cloud, they can be converted to a data transmission format based on the TCP / IP protocol, thereby ensuring system compatibility and scalability.

[0057] In some implementations, the gateway 210 may also possess edge computing capabilities for preprocessing the collected data, including data caching, outlier filtering, data compression, and simple statistical analysis, to reduce network transmission load and improve system response efficiency. In the event of a network outage, the gateway 210 can also store the data locally and retransmit it after communication is restored, thereby ensuring data integrity.

[0058] Furthermore, to improve the reliability and security of data transmission, the data transmission layer 200 may employ data encryption transmission mechanisms and identity authentication mechanisms, such as TLS / SSL-based encrypted communication protocols, as well as device identity authentication and access control policies, to prevent data leakage or unauthorized access.

[0059] In addition, the data transmission layer 200 can also set the data upload cycle and transmission strategy according to the system operation requirements. For example, it can use a periodic upload method under normal operation, and automatically switch to a high-frequency upload or real-time push mode when an abnormality or warning trigger condition is detected, so as to balance communication energy consumption and data real-time performance.

[0060] Through the above structural and functional design, the data transmission layer 200 can achieve stable, efficient and secure transmission of multi-source monitoring data, providing reliable data support for the real-time analysis and prediction of the cloud intelligent layer 300, while ensuring the communication reliability and adaptability of the system in complex industrial environments.

[0061] IV. Implementation Methods of the Cloud-Based Intelligent Layer In one specific implementation, such as Figure 1 and Figure 2 As shown, the cloud-based intelligent layer 300 serves as the core processing unit of the system of this invention, used to realize functions such as multi-source data fusion, corrosion mechanism modeling, corrosion rate prediction, remaining lifetime assessment, and early warning decision generation. The cloud-based intelligent layer 300 includes an intelligent analysis server 310 and a data processing program running on it, and can be deployed on a public cloud platform or a local private cloud environment.

[0062] Furthermore, the cloud-based intelligent layer 300 mainly includes a data management module 3101, a model calculation module 3102, a parameter self-learning module 3103, a life assessment module 3104, and an early warning decision module 3105. The modules work together to form a complete data processing and decision-making chain.

[0063] 1. Data Management and Fusion Processing In one specific embodiment, the data management module 3101 is used to receive multi-source monitoring data from the data transmission layer 200, including wall thickness data, flow velocity data, temperature data and optional water quality parameter data, and to uniformly store and manage the data.

[0064] Specifically, the data management module 3101 performs time alignment, outlier removal, and data compensation on data from different sources to establish a unified time-series database, ensuring data consistency in both time and spatial dimensions. Simultaneously, by fusing historical and real-time data, it provides complete data input for subsequent model calculations.

[0065] 2. Corrosion Rate Model Construction and Calculation In one specific implementation, the model calculation module 3102 is used to establish a corrosion rate prediction model based on the flow rate accelerated corrosion mechanism, calculate the input data, and output the predicted corrosion rate CR.

[0066] The model comprehensively considers the coupled effects of fluid velocity, fluid temperature, and pipe material on the corrosion rate, and its expression can be expressed in the following form:

[0067] The meanings of each parameter are the same as described above. By inputting the real-time collected flow rate and temperature data into this model, the instantaneous corrosion rate under the current operating conditions can be obtained.

[0068] In one specific embodiment, the corrosion rate prediction model may further incorporate a water quality influence function f(pH) to characterize the modulating effect of fluid pH on the corrosion rate. The water quality influence function can be expressed as follows:

[0069] Where m is an empirical constant, pH1 is the fluid pH value obtained from real-time monitoring, and pH0 is the reference pH value corresponding to the current operating temperature and medium conditions.

[0070] When pH1 is lower than pH0, f(pH) is greater than 1, indicating an accelerated corrosion rate; when pH1 is higher than pH0, f(pH) is less than 1, indicating a decreased corrosion rate. By introducing this function, the model can better respond to changes in the water's chemical environment, thereby improving the accuracy of prediction results.

[0071] In one specific embodiment, the material factor M is used to characterize the influence of the pipe material composition and heat treatment state on the susceptibility to flow rate-accelerated corrosion. The material factor M can be preset according to the chromium (Cr) content, material type, and heat treatment state of the pipe material, or determined by looking up a table in a material database.

[0072] For example, for carbon steel pipes with a chromium content of less than 0.1%, M can be set to 1.0; for low alloy steel pipes with a chromium content of about 2.25%, M can be set to 0.01 to 0.10; for alloy materials with higher corrosion resistance, M can be further reduced.

[0073] By introducing the material factor M, the model can reflect the differences in corrosion rates of different material systems under the same working conditions, thereby improving the model's engineering applicability.

[0074] In one specific embodiment, the corrosion rate prediction model can be expressed using the following complete relationship:

[0075] Where CR is the predicted corrosion rate; K is the overall rate constant; and V is the fluid velocity. denoted as ρ = ρ_flow velocity index; Q = ρ_apparent activation energy; R = ρ_ideal gas constant; T = ρ_absolute fluid temperature; f(pH) = ρ_water quality influence function; M = material factor.

[0076] When the monitoring node is not configured with a pH sensor, f(pH) can be set to 1, or an empirical value under the default water quality conditions can be used to ensure that the model can run under different configuration scenarios.

[0077] 3. Adaptive update of model parameters (corresponding to) Figure 2 (Middle section) In one specific implementation, the parameter self-learning module 3103 is used to dynamically update the model parameters based on historical monitoring data so that the model continuously conforms to the actual corrosion behavior.

[0078] Specifically, the parameter self-learning module 3103 constructs a rolling time window based on time series data, performs parameter fitting on historical data of each monitoring point, and updates the parameters K in the model. And Q. In this way, the model can adaptively reflect the effects of factors such as material aging, changes in operating conditions, and water quality fluctuations, thereby improving prediction accuracy.

[0079] In one specific implementation, the parameter self-learning module 3103 employs a rolling time window mechanism to process the model parameters K, The parameters Q are updated periodically. Specifically, for each monitoring point, the historical monitoring data of the most recent N days are extracted as training samples, where N can be 7 days, 15 days, or 30 days. In each update cycle, parameter fitting is performed based on the training samples to obtain the optimal parameter estimate for the current time period.

[0080] In one implementation, the update cycle can be set to 24 hours, meaning the system updates the parameters of each monitoring point once a day. When the operating conditions fluctuate greatly or the corrosion rate changes significantly, the update cycle can be shortened to 12 hours or 6 hours to improve the model's adaptability to dynamic operating conditions.

[0081] In one specific implementation, parameter fitting can employ nonlinear least squares, adaptive filtering algorithms, or other time series parameter estimation algorithms. To improve fitting stability, the parameter self-learning module 3103 can set constraints on the parameter variation amplitude, such as limiting the parameters K and K within two adjacent update periods. The change ratio of Q should not exceed a preset threshold to avoid parameter mutations caused by abnormal data.

[0082] In one example implementation, a monitoring point obtains parameter K=0.0105 during the initial modeling stage. =1.72, Q=4800J / mol; After the system ran for 30 days, K=0.0117 was obtained by refitting based on the monitoring data within the rolling time window. =1.85, Q=5200J / mol. After parameter updates, the fitting error between the model calculation results and the actual wall thickness variation trend was significantly reduced, indicating that the dynamic update mechanism can effectively improve prediction accuracy.

[0083] In one specific implementation, the model parameters K, The initial values ​​of Q can be determined based on at least one of the following methods: (1) Fitting based on historical operating data of similar pipelines; (2) Set the parameters according to the typical parameter range in publicly available documents or industry standards; (3) Perform rapid calibration based on short-term test data during the initial online phase.

[0084] During system operation, the parameter self-learning module 3103 will continuously use newly added monitoring data to calibrate and update the parameters, so that the model parameters gradually converge to a stable value that matches the actual corrosion behavior of the target monitoring point.

[0085] 4. Remaining useful life assessment In one specific embodiment, the life assessment module 3104 is used to calculate the predicted remaining safe time Tr based on the current measured wall thickness and the predicted corrosion rate.

[0086] Specifically, the life assessment module 3104 calculates based on the following relationship:

[0087] Where Wt is the current measured wall thickness, Wmin is the preset limit safe wall thickness, and CR is the predicted corrosion rate. By continuously updating the Tr value, the remaining safe operating time of the pipeline can be dynamically reflected.

[0088] 5. Early warning decision-making and information generation In one specific implementation, the early warning decision module 3105 is used to generate early warning information based on the relationship between the predicted remaining safe time Tr and the preset early warning threshold.

[0089] Specifically, the system sets three levels of early warning thresholds: Level 1 early warning threshold Ta1 is 12 months, Level 2 early warning threshold Ta2 is 6 months, and Level 3 early warning threshold Ta3 is 3 months, and Ta1 > Ta2 > Ta3.

[0090] When Tr≤Ta1 and Tr>Ta2, a Level 1 warning (yellow) is generated. When Tr≤Ta2 and Tr>Ta3, a level 2 warning (orange) is generated. When Tr≤Ta3, a level 3 warning (red) is generated.

[0091] In one implementation, to avoid frequent fluctuations in the warning level around a threshold, the system sets a warning hysteresis interval. When Tr recovers from below a certain warning threshold to above that threshold, the warning level is only reduced after Tr has been above the recovery condition corresponding to the threshold and the preset hysteresis time threshold for multiple consecutive sampling periods.

[0092] The early warning decision module 3105 further generates a structured early warning message, which includes at least: monitoring point identifier, current wall thickness value, predicted corrosion rate, predicted remaining safe time, early warning level and color identifier, analysis results of dominant corrosion influencing factors, and suggested operating parameter range.

[0093] In one implementation, to avoid frequent fluctuations in the warning level around the threshold, the system can set a warning hysteresis interval. For example, when Tr recovers from below Ta2 to above Ta2, the warning level is only reduced after Tr has been above Ta2+ΔT for several consecutive sampling periods, where ΔT is the hysteresis time threshold, which can be 12 hours or 24 hours.

[0094] By introducing a hysteresis mechanism, the stability of the early warning system can be improved, and frequent alarms caused by short-term fluctuations can be avoided.

[0095] In one specific implementation, the warning threshold can also be set differently based on the importance of the monitoring point. For critical pipe sections or areas with serious accident consequences, the warning threshold can be appropriately increased to trigger the warning in advance; for areas with lower risk, a relatively lenient threshold setting can be adopted to reduce unnecessary maintenance intervention.

[0096] 6. Visualization and diagnostic analysis (optional implementation) In some implementations, the cloud-based intelligent layer 300 also includes a visualization and diagnostic module for graphically displaying the analysis results, including corrosion rate variation trends, wall thickness variation curves, and multi-parameter correlation analysis graphs.

[0097] Meanwhile, through correlation analysis of multi-parameter data, the system can identify the dominant corrosion factors, such as determining that corrosion is mainly caused by increased flow rate or temperature changes, thus providing a basis for subsequent control strategies.

[0098] Through the above structural and functional design, the cloud-based intelligent layer 300 realizes a complete processing flow from data reception and mechanism modeling to predictive decision-making, enabling the system to perform real-time analysis and accurate prediction of pipeline corrosion behavior, and providing reliable data support and decision-making basis for the control linkage layer, thereby significantly improving the system's intelligence level and engineering application value.

[0099] V. Implementation Method of Control and Linkage Layer In one specific implementation, such as Figure 1As shown, the control linkage layer 400 is used to realize the communication and control interaction between the cloud intelligent layer 300 and the factory distributed control system (DCS), and is the key execution layer for transforming corrosion monitoring and prediction results into actual process control behavior.

[0100] Furthermore, the control linkage layer 400 includes a communication interface module 410 and a control interaction module 420. The communication interface module 410 is used to establish a standardized communication connection with the DCS, and the control interaction module 420 is used to parse, encapsulate, and send out the early warning information generated by the cloud intelligent layer 300.

[0101] 1. Communication Interface Implementation Method In one specific embodiment, the communication interface module 410 establishes a data connection with the DCS using a standardized industrial communication protocol, which includes, but is not limited to, the OPC UA protocol or the Modbus TCP protocol.

[0102] Specifically, the cloud-based intelligent layer 300 provides a data access interface to the DCS through an OPC UA server, or achieves compatible communication with the Modbus TCP protocol through an API gateway / protocol conversion module, thereby realizing data interconnection and interoperability between different systems. Through standardized interface design, the system of this invention can be adapted to control systems from different manufacturers, improving the system's versatility and scalability.

[0103] 2. Generation and delivery of structured early warning messages In one specific implementation, the control interaction module 420 is used to receive the early warning information generated by the cloud intelligent layer 300, encapsulate it into a structured early warning message, and then send it to the DCS.

[0104] The structured early warning message includes at least the following information: Unique identifier and geographical location description of the monitoring point; Current measured wall thickness; Predicting corrosion rate and remaining safe time; Analysis results of the dominant influencing factors of corrosion; The recommended parameter range for process optimization is generated based on the model.

[0105] Furthermore, the warning messages are encapsulated in a unified data format so that the DCS can directly parse and call the relevant data fields to achieve automated processing.

[0106] 3. DCS-side response mechanism In one specific implementation, after receiving a structured early warning message, the DCS executes a corresponding response action according to preset control logic.

[0107] Specifically, the response action includes at least one of the following: Generate an audible and visual alarm on the operator interface and display detailed warning information; The process control interface related to the corresponding pipeline or equipment will pop up automatically; Highlight key control parameters related to corrosion (such as flow rate, temperature, etc.); Output control commands to adjust the operating status of relevant equipment, such as adjusting pump speed to reduce fluid flow rate, switching to standby pipelines, or adjusting system pH.

[0108] In some implementations, the control response can be executed in stages according to the warning level. For example, the operator is only prompted during a low-level warning, while the control action is automatically triggered during a high-level warning, thereby achieving risk classification management.

[0109] 4. Closed-loop control implementation mechanism In one specific implementation, the control linkage layer 400 not only realizes one-way information transmission, but also supports a closed-loop feedback mechanism.

[0110] Specifically, after the DCS executes the control action, the adjusted process parameters (such as the actual value after the flow rate is reduced) will be re-collected by the field sensing layer 100 and uploaded to the cloud intelligent layer 300. The cloud intelligent layer 300 will recalculate the corrosion rate and predict the remaining safe time based on the updated data, thus forming a closed-loop adjustment process of "control-feedback-reassessment".

[0111] Through this closed-loop mechanism, the system can dynamically evaluate the actual effect of control measures and continuously optimize control strategies to improve the accuracy and effectiveness of corrosion control.

[0112] 5. Safety and Reliability Design (Optional Implementation) In some implementations, the control linkage layer 400 further includes a safety control strategy module to ensure the safety and reliability of system operation.

[0113] For example, before executing automatic control commands, the system can set up permission verification or double confirmation mechanisms; for critical control actions, their execution scope can be restricted or safety boundaries can be set to avoid adverse effects on the production system.

[0114] In addition, in the event of communication or data anomalies, the system can automatically switch to a safe mode, retaining only the monitoring and alarm functions, thereby ensuring the overall stability of the system operation.

[0115] Through the above structural and functional design, the control linkage layer 400 achieves deep integration between the monitoring system and the industrial control system, enabling corrosion early warning information to directly drive process control behavior, transforming the traditional "monitoring + manual response" mode into an active prevention and control mode of "intelligent analysis + automatic linkage", thereby significantly improving the safety assurance capability and operating efficiency of the industrial pipeline system.

[0116] VI. Workflow Implementation Method In one specific implementation, such as Figure 2 As shown, the intelligent pipeline monitoring and early warning system of the present invention operates according to the process of "data acquisition—data transmission—model calculation—life assessment—early warning generation—control linkage—feedback optimization". For ease of understanding, each step is described in detail below.

[0117] Step S1: Synchronous Acquisition of Multi-Source Data The target pipeline is continuously monitored by multi-parameter intelligent monitoring nodes 110 in the field sensing layer 100. Each monitoring node 110 synchronously collects the following data within a preset sampling period: (1) Pipe wall thickness data Wt collected by electromagnetic ultrasonic thickness measurement module 111; (2) Fluid velocity data V collected by flow velocity sensor 112; (3) Fluid temperature data T collected by temperature sensor 113; (4) Optionally, water quality parameter data acquired by pH sensor 114 or electrochemical noise sensor.

[0118] In this embodiment, the sampling period can be set to 1 minute, 5 minutes or 10 minutes depending on the operating conditions; when the system detects an abnormal trend, the sampling period can be automatically shortened to improve the monitoring resolution.

[0119] Step S2: Data Upload and Transmission Management Monitoring node 110 sends the collected raw data to local processing unit 115. Local processing unit 115 filters, denoises, performs analog-to-digital conversion, and encapsulates the raw signal, and adds identification information such as timestamps and monitoring point numbers. The processed data is then sent to data transmission layer 200 via wireless transmission unit 116.

[0120] In wide-area deployment scenarios, monitoring node 110 directly uploads data via the NB-IoT network; in densely deployed regional scenarios, monitoring node 110 sends data to gateway 210 via the LoRa network, where it is aggregated and uploaded to the cloud intelligent layer 300. To ensure data integrity, the data transmission layer 200 performs local caching when communication is interrupted and resumes transmission after the link is restored.

[0121] Step S3: Data Fusion and Preprocessing After receiving data from each monitoring node 110, the cloud-based intelligent layer 300 processes the data uniformly by the data management module 3101. Specifically, this includes: (1) Time-align the wall thickness, flow rate, temperature and water quality parameters according to the timestamp; (2) Identify and remove outliers, such as mutation values, duplicate values, and missing values; (3) Perform interpolation compensation or use historical mean compensation for missing data; (4) Store the processed data in a unified time series database to form a standardized input dataset that can be used for model computation.

[0122] In some implementations, the data management module 3101 can also establish a spatial index based on the geographical location of the monitoring point and the pipeline topology to facilitate subsequent comparative analysis of regional corrosion risks.

[0123] Step S4: Calculation of corrosion rate prediction model The model calculation module 3102 calls the corrosion rate prediction model based on the flow rate accelerated corrosion mechanism to calculate the predicted corrosion rate CR from the real-time data of each monitoring point. The model can adopt the following relationship:

[0124] Where K is the overall rate constant. Where is the flow rate index, Q is the apparent activation energy, R is the ideal gas constant, and M is the material factor.

[0125] In an optional implementation, the model may also incorporate a water quality influence function f(pH) to reflect the modulating effect of water quality conditions on the corrosion rate.

[0126] To improve model adaptability, the parameter self-learning module 3103, based on historical monitoring data, adjusts the model parameters K, K, and K according to a rolling time window mechanism. The model is periodically updated to keep pace with changes in current operating conditions.

[0127] Step S5: Calculate the remaining safe time The life assessment module 3104 calculates the predicted remaining safe time Tr based on the current measured wall thickness Wt, the preset limit safe wall thickness Wmin, and the predicted corrosion rate CR. The calculation relationship is as follows:

[0128] Among them, Wmin can be preset according to pipeline design specifications, material strength requirements and operational safety standards.

[0129] The system continuously updates the Tr value for each monitoring point and generates a remaining lifespan change curve so that maintenance personnel can understand the corrosion development trend.

[0130] Step S6: Early Warning Judgment and Structured Message Generation The early warning decision module 3105 compares the calculated Tr with the preset early warning threshold and performs an early warning determination.

[0131] In one implementation, the warning threshold includes three levels: The Level 1 warning threshold, Ta1, is 12 months and corresponds to a yellow warning. The Level 2 warning threshold, Ta2, is 6 months, corresponding to an orange warning. The Level 3 warning threshold, Ta3, is 3 months and corresponds to a red warning.

[0132] When Tr≤Ta1 and Tr>Ta2, the system generates a Level 1 warning (yellow). When Tr≤Ta2 and Tr>Ta3, the system generates a level 2 warning (orange). When Tr≤Ta3, the system generates a Level 3 warning (red).

[0133] In one implementation, to avoid frequent switching of the warning level near the threshold, the system sets up a warning hysteresis mechanism: when Tr recovers from below a certain threshold to above that threshold, the warning level is only reduced after Tr meets the recovery condition for multiple consecutive sampling periods.

[0134] The early warning decision module 3105 then generates a structured early warning message, which includes at least: (1) Unique identifier and location description of the monitoring point; (2) Current measured wall thickness Wt; (3) Predicting the corrosion rate CR; (4) Predict the remaining safe time Tr; (5) Warning levels and color codes; (6) Analysis results of the dominant corrosion influencing factors; (7) Suggested range of process adjustment parameters (optional).

[0135] Step S7: Early Warning Information Push and Control Linkage The control linkage layer 400 sends structured early warning messages to the distributed control system (DCS) through standardized industrial communication interfaces (such as OPC UA or Modbus TCP).

[0136] After receiving the warning message, the DCS executes at least one of the following responses according to the preset control logic: (1) Trigger an audible and visual alarm on the operator interface and display the warning details; (2) The corresponding process control screen will pop up automatically and the relevant parameters will be highlighted; (3) Output control commands to adjust flow rate, switch to standby pipeline, or adjust water quality chemical parameters; (4) Execute interlocking protection actions under high-level early warning conditions (optional).

[0137] Step S8: Control Feedback and Closed-Loop Optimization After the DCS executes the control action, the field sensing layer 100 continues to collect adjusted flow rate, temperature, and wall thickness data and uploads them to the cloud-based intelligent layer 300. The cloud-based intelligent layer 300 recalculates CR and Tr based on the new data and compares the changes before and after control to evaluate the effectiveness of the control measures.

[0138] If the assessment results show that the corrosion rate decreases and Tr increases, the system will automatically lower the warning level; if the effect of control is not obvious, the system can generate optimization suggestions again and push them to DCS, forming a closed-loop optimization process of "perception-analysis-decision-control-feedback".

[0139] In one implementation, the system can execute a proactive intervention strategy for monitoring points approaching the next level of warning threshold. For example, when Tr is in the first-level warning range and Tr−Ta2≤1 month, the system, while maintaining the first-level warning level, outputs a mild adjustment suggestion to the DCS to suppress the upward trend of corrosion rate in advance.

[0140] Step S9: Historical Archiving and Operational Decision Support (Optional) In some implementations, the cloud-based intelligent layer 300 can also archive historical monitoring data, early warning event records, and control response records, and generate periodic reports to support equipment maintenance plan development, risk classification management, and preventive maintenance strategy optimization.

[0141] Through the above workflow, the system of the present invention can realize real-time monitoring, dynamic prediction and active control of pipeline corrosion status, significantly improving the timeliness, accuracy and automation level of corrosion risk management.

[0142] VII. Engineering Application Examples (corresponding) Figure 1 and Figure 2 ) Example 1: Application of Corrosion Monitoring in Water Supply Pipelines of Thermal Power Plants In one specific implementation, the deployment and operation of the system of the present invention will be described below in the context of a high-temperature and high-pressure feedwater pipeline corrosion monitoring scenario in a large thermal power plant.

[0143] 1. On-site deployment In this thermal power plant, high-risk areas for corrosion due to accelerated flow velocity, such as the economizer inlet, downcomer, and header, were selected as monitoring areas, with a total of 20 monitoring points deployed. Each monitoring point is equipped with a multi-parameter intelligent monitoring node 110, which is fixed to the outer wall of the pipeline using an explosion-proof shell and a clamp structure with a heat insulation layer. Each monitoring node 110 integrates an electromagnetic ultrasonic thickness measurement module 111, a flow velocity sensor 112, and a temperature sensor 113. The flow velocity sensor 112 is an insertion-type vortex flow meter, and the temperature sensor 113 is a sheathed thermocouple.

[0144] 2. Network Topology and Data Transmission Due to the large area and complex building structure of the plant, this embodiment adopts a data transmission mode of "LoRa aggregation + fiber optic backhaul". Specifically, three LoRa gateways 210 are deployed at the highest point in the plant to cover all monitoring points. Each monitoring node 110 collects wall thickness, flow velocity, and temperature data every 5 minutes according to a preset sampling cycle, and sends the data to the corresponding LoRa gateway 210 via the LoRa wireless network. The LoRa gateway 210 then uploads the aggregated data in real time to the local private cloud server of the power plant information center through the plant's industrial fiber optic ring network, serving as the deployment carrier for the cloud-based intelligent layer 300.

[0145] 3. Model Initialization and Online Execution When the system is first launched, the pipeline's operating data from the past two years and the wall thickness data obtained from the most recent maintenance are imported into the cloud-based intelligent layer 300 for initializing and fitting model parameters for 20 monitoring points. The parameter self-learning module 3103 in the cloud-based intelligent layer 300 adopts a rolling time window mechanism, updating the model parameters of each monitoring point every 24 hours.

[0146] Taking "Downcomer Measurement Point D-05" as an example, at a certain operating moment, the cloud-based intelligent layer 300 receives the following real-time data for this measurement point: fluid velocity V = 15.2 m / s, fluid temperature T = 498 K, current measured wall thickness Wt = 10.5 mm, and preset limit safety wall thickness Wmin = 7.0 mm. Based on the most recently updated model parameters K = 0.0117 for this measurement point... =1.85, Q=5200J / mol, the cloud-based intelligent layer 300 calculates the current predicted corrosion rate CR=3.05mm / year through model calculation, and calculates the remaining safe time Tr=(10.5-7.0) / 3.05≈1.15 years, or about 13.8 months.

[0147] In one implementation, for monitoring points that are close to the warning threshold, the system can mark the monitoring point as a key focus and increase the data analysis frequency by combining historical change trends, so as to identify in a timely manner whether it has entered the warning range.

[0148] 4. Early Warning Generation and Classification In this embodiment, the system presets three warning thresholds, where the first warning threshold Ta1 is 12 months (yellow warning), the second warning threshold Ta2 is 6 months (orange warning), and the third warning threshold Ta3 is 3 months (red warning).

[0149] A Level 1 warning (yellow) is triggered when the predicted remaining safe time Tr≤Ta1 and Tr>Ta2; a Level 2 warning (orange) is triggered when Tr≤Ta2 and Tr>Ta3; and a Level 3 warning (red) is triggered when Tr≤Ta3.

[0150] In this embodiment, after continuous monitoring and model updates, the input data for measuring point D-05 in the next evaluation cycle changes as follows: the current measured wall thickness Wt = 10.1 mm, other operating parameters remain basically unchanged, and the cloud-based intelligent layer 300 recalculates the predicted corrosion rate CR = 3.18 mm / year, and based on this, the predicted remaining safe time Tr = (10.1 - 7.0) / 3.18 ≈ 0.97 years, or approximately 11.6 months. Since this result satisfies Tr ≤ 12 months and Tr > 6 months, the system generates a Level 1 warning (yellow), and the warning decision module 3105 generates a structured warning message.

[0151] Considering that the remaining safe time for this monitoring point has entered the first-level warning range, the system adds a prompt message to the warning message: "It is recommended to give priority to this point and arrange for operational status verification" to remind maintenance personnel to take timely control measures.

[0152] The structured early warning message includes at least the following fields: monitoring point identifier "D-05", monitoring point location description "downcomer area", current measured wall thickness 10.1 mm, predicted corrosion rate 3.18 mm / year, predicted remaining safe time approximately 11.6 months, warning level "Level 1 (Yellow)", dominant influencing factor "high flow rate (contribution greater than 70%)", and recommended operating parameters "it is recommended to control the flow rate below 14 m / s".

[0153] 5. DCS linkage and control response The control linkage layer 400 sends the aforementioned structured early warning message to the power plant's main DCS system in real time via the OPC UA interface. Upon receiving the message, the DCS system triggers a yellow audible and visual alarm on the operator console and displays the process control screen associated with "Downcomer D-05," while simultaneously highlighting the flow rate and speed parameters of the corresponding feedwater pump.

[0154] In this embodiment, the DCS system provides a clear prompt to the operator based on preset logic: "FAC Warning (Yellow): Downcomer D-05 section; current wall thickness 10.1mm; estimated remaining safe time approximately 11.6 months; main cause: high flow rate; it is recommended to check the speed of No. 1 feedwater pump and try to reduce it by 2% to 3%." In one implementation, the operator adjusts the relevant pump set according to the prompts to reduce the fluid velocity in the pipe section from 15.2 m / s to 13.9 m / s, thereby reducing the promoting effect of the flow velocity on corrosion.

[0155] 6. Closed-loop feedback and effect verification After the control actions are executed, the on-site sensing layer 100 continuously collects adjusted flow rate, temperature, and wall thickness data, and uploads them to the cloud-based intelligent layer 300 via the data transmission layer 200. Based on the updated data, the cloud-based intelligent layer 300 recalculates the predicted corrosion rate and the predicted remaining safe time. The results show that the predicted corrosion rate at this measuring point decreased from 3.18 mm / year to 2.46 mm / year, the predicted remaining safe time increased from approximately 11.6 months to approximately 14.8 months, and the warning level recovered from the edge of the first-level warning to above the warning threshold, indicating that the control measures effectively suppressed the corrosion development trend.

[0156] As can be seen from this embodiment, the system of the present invention can realize closed-loop management of the entire process from online sensing of multiple parameters, prediction of mechanism models, generation of early warning decisions to DCS linkage control, so that pipeline corrosion risk is transformed from passive monitoring to active intervention, significantly improving the safety and controllability of industrial pipeline operation.

[0157] Example 2: Petrochemical Pipeline Network Scenario Example In one specific embodiment, the intelligent pipeline monitoring and early warning system provided by the present invention is applied to corrosion monitoring scenarios of high-temperature oil and gas transmission pipelines in petrochemical plants, such as complex corrosive environments like hydrogenation units, atmospheric and vacuum distillation units, and sulfur-containing crude oil transmission systems.

[0158] In this embodiment, the system includes a field sensing layer, a data transmission layer, a cloud-based intelligent layer, and a control and linkage layer, and is deployed in critical corrosion areas of the petrochemical plant. These critical areas include reactor outlet pipelines, heat exchanger inlet pipe sections, elbows, tees, and high-velocity scouring areas.

[0159] Specifically, the field sensing layer includes multiple multi-parameter intelligent monitoring nodes, which are distributed along the pipeline. In one embodiment, the spacing between monitoring nodes can be set from 10m to 50m according to the corrosion risk, and for high-risk locations such as bends or diameter changes, the nodes can be densely arranged to a spacing of 5m.

[0160] Each monitoring node includes an electromagnetic ultrasonic thickness measurement module, a flow velocity sensor, a temperature sensor, and an optional pH sensor or electrochemical noise sensor, used to acquire pipe wall thickness, fluid flow velocity, fluid temperature, and water quality or corrosion electrochemical parameters, respectively.

[0161] In one implementation: The electromagnetic ultrasonic thickness measurement module has a measurement range of 3mm to 50mm, a measurement accuracy of ±0.1mm, and a sampling period of 5 minutes. The flow velocity sensor has a measurement range of 0 m / s to 25 m / s, a measurement accuracy better than ±1%, and a sampling period that can be set to 1 minute. The temperature sensor has a measurement range of 50℃ to 400℃, and the sampling period can be set to 1 minute. The pH sensor has a measurement range of pH 2 to 12, and the sampling period can be set to 5 minutes.

[0162] The electromagnetic ultrasonic thickness measurement module uses a non-contact electromagnetic ultrasonic transducer and is fixed to the outer wall of the pipe by a high-temperature resistant clamp structure, making it suitable for high-temperature environments above 200°C. In one embodiment, the monitoring node adopts an explosion-proof design and is equipped with a hybrid power supply method of batteries and solar panels, which can meet the continuous operation for more than 6 months on a single power supply cycle.

[0163] Furthermore, the monitoring nodes are connected to the data transmission layer via wireless communication. In one embodiment, when the pipeline network has a large distribution area, data is directly uploaded using NB-IoT; when the monitoring nodes are centrally deployed within the plant area, LoRa communication is used to connect to the gateway, with a communication distance of 1km to 3km.

[0164] In this implementation, the cloud-based intelligent layer constructs a corrosion rate prediction model based on multi-source data. The model comprehensively considers factors such as flow rate, temperature, material, and water quality, and its basic expression relationship can be expressed in the following form:

[0165] Where CR is the corrosion rate in mm / year; V is the fluid velocity in m / s; T is the absolute temperature of the fluid in K; and K is the overall rate constant. denoted as ρ = Flow velocity index; Q is the apparent activation energy; R is the ideal gas constant; f(pH) is the water quality influence function; M is the material factor.

[0166] In one implementation: K can be taken as 0.008 to 0.015; A value of 1.5 to 2.0 is acceptable. Q can be taken as 4000 J / mol to 6000 J / mol.

[0167] The water quality influence function f(pH) can be expressed in the following form:

[0168] Where m can be 0.2 to 0.5, and pH0 is the reference pH value, for example, it can be set to 9.0.

[0169] In some implementations, for common operating conditions in petrochemical plants such as sulfur-containing media, chloride ion environments, or dissolved oxygen fluctuations, the cloud-based intelligent layer can modify the comprehensive rate constant K, water quality influence function f(pH), and / or material factor M based on historical monitoring data, water quality parameters, and actual wall thickness variation trends, so that the corrosion rate prediction model better reflects the actual corrosion behavior under petrochemical conditions.

[0170] In one embodiment, the pipeline material can be carbon steel or low alloy steel; for pipelines of different materials, the cloud-based intelligent layer can preset the corresponding material factor M according to the material composition, thereby improving the applicability of the model to different petrochemical pipelines.

[0171] In a specific example, the real-time data at a monitoring point are: flow velocity V = 16.8 m / s, temperature T = 520 K, pH = 8.5, current wall thickness Wt = 9.8 mm, preset limit safety wall thickness Wmin = 7.5 mm, and the model parameter is K = 0.011. =1.8, Q=5100J / mol, and combined with the material parameters corresponding to the monitoring point and the current water quality conditions, the corrosion rate CR is calculated to be approximately 0.58mm / year.

[0172] Furthermore, the cloud-based intelligent layer calculates and predicts the remaining safe time based on the following relationship:

[0173] In the example above, Tr = (9.8 - 7.5) / 0.58 ≈ 3.97 years, or about 47.6 months.

[0174] In one implementation, the system sets multiple warning thresholds: The threshold for Level 1 warning is 12 months; The threshold for a Level II warning is 6 months. The threshold for a Level 3 warning is 3 months.

[0175] When the predicted remaining safe time is less than or equal to the corresponding threshold, a warning of the corresponding level is generated.

[0176] In another example, when the data at a certain monitoring point are: Wt=8.2mm, Wmin=7.5mm, flow velocity V=18.5m / s, temperature T=535K, the model calculates CR=0.92mm / year, and the predicted remaining safe time Tr≈0.76 years, or about 9.1 months, at which point a level one warning is triggered.

[0177] The cloud-based intelligent layer further generates structured early warning messages, which include: monitoring point identifier, location description, current wall thickness, corrosion rate, remaining safe time, warning level, and analysis results of dominant corrosion factors. In one implementation, when the system determines that the flow rate contribution exceeds 70%, the dominant factor is marked as "flow rate-dominated corrosion".

[0178] In terms of control linkage, the control linkage layer is connected to the distributed control system via a standardized industrial communication interface. In one embodiment, the system sends early warning information to the DCS via the OPC UA interface.

[0179] When flow rate is identified as the dominant factor in corrosion, the system can output the following control recommendations: Reduce the speed of the relevant pump units by 2% to 5%; The flow velocity in the pipeline was reduced from 18 m / s to below 15 m / s.

[0180] When water quality is identified as the dominant factor in corrosion, the following recommendations can be provided: Adjust the dosing system to raise the pH value from 8.5 to 9.2–9.5; Add corrosion inhibitors at a concentration of 10 ppm to 30 ppm.

[0181] In terms of data transmission, the system adopts a periodic upload strategy under normal operating conditions. For example, wall thickness data is uploaded every 5 minutes, and flow rate and temperature data are uploaded every 1 minute. When a rapid increase in corrosion rate is detected or the system approaches the warning threshold, the system automatically switches to a high-frequency upload mode, such as uploading key parameters at 30-second intervals.

[0182] In terms of implementation, this method includes the following steps: collecting pipe wall thickness, flow velocity, temperature and water quality data through monitoring nodes; performing data fusion and corrosion rate calculation in the cloud intelligent layer; obtaining the predicted remaining safe time based on the calculation results; generating an early warning message when the early warning conditions are met; and sending it to the distributed control system through the control linkage layer and executing the corresponding control response.

[0183] Through the above technical solution, this implementation method can be applied to the complex corrosive environment of petrochemical industry, realize real-time monitoring and accurate prediction of corrosion behavior under the coupling effect of multiple factors, and achieve active control of corrosion development through process parameter adjustment, thereby significantly improving the safety and reliability of pipeline system operation.

[0184] Example 3: Edge Computing Deployment Example In one specific embodiment, the intelligent pipeline monitoring and early warning system provided by the present invention adopts an edge computing and cloud-based collaborative deployment architecture to improve the system's real-time response capability and operational reliability in complex industrial environments.

[0185] In this embodiment, the system includes a field perception layer, a data transmission layer, a cloud intelligence layer, and a control linkage layer. The gateway device in the data transmission layer integrates an edge computing module to perform local data processing and analysis before data is uploaded to the cloud, thus forming a layered collaborative system of "field perception—edge processing—cloud analysis—control linkage".

[0186] Specifically, the on-site sensing layer includes multiple multi-parameter intelligent monitoring nodes, each positioned at high-risk corrosion locations such as pipe bends, tees, diameter transition sections, and high-flow-velocity sections. Each monitoring node includes an electromagnetic ultrasonic thickness measurement module, a flow velocity sensor, and a temperature sensor, used to acquire pipe wall thickness, fluid flow velocity, and fluid temperature information, respectively. The electromagnetic ultrasonic thickness measurement module employs a non-contact electromagnetic ultrasonic transducer and is fixed to the outer wall of the pipe using a clamp structure to adapt to high-temperature, high-pressure, and complex operating environments.

[0187] In one embodiment, the electromagnetic ultrasonic thickness measurement module has a thickness resolution of 0.1 mm and a wall thickness sampling period of 5 minutes; the flow velocity sensor has a measurement range of 0 m / s to 20 m / s and a sampling period of 1 minute; the temperature sensor has a measurement range of 50℃ to 300℃ and a sampling period of 1 minute. After the monitoring node samples different parameters, the local processing unit performs time alignment and unified encapsulation, adds a timestamp and monitoring point identifier, and then uploads the data.

[0188] Furthermore, the monitoring node also includes a local processing unit and a wireless transmission unit. The local processing unit is used to filter, denoise, remove outliers, and encapsulate the collected raw data; the wireless transmission unit uses NB-IoT or LoRa communication to send the processed data to the gateway device in the data transmission layer. In one embodiment, when the monitoring nodes are widely distributed, NB-IoT is used to directly access the operator's network; when the monitoring nodes are concentrated within the factory area, LoRa is used to communicate with the gateway, with a LoRa communication distance of 1km to 3km.

[0189] In this embodiment, the edge computing module in the gateway device processes the received data in real time. Specifically, this includes: performing time alignment and fusion processing on data from multiple monitoring nodes, and rapidly calculating the data based on a preset simplified corrosion rate model to obtain the preliminary corrosion rate under the current operating conditions.

[0190] In one embodiment, the simplified corrosion rate model can take the following form:

[0191] Where CRe is the initial corrosion rate calculated at the edge, K1 is the comprehensive rate constant of the edge model, and V is the fluid velocity. 1 represents the velocity index of the edge model, Q1 represents the apparent activation energy of the edge model, R represents the ideal gas constant, and T represents the absolute temperature of the fluid.

[0192] In one example, K1 can be preset to 0.0108. =1.75, Q1 = 5000 J / mol. When the real-time data uploaded by a certain monitoring point is: fluid velocity V = 14.6 m / s, fluid temperature T = 485 K, current measured wall thickness Wt = 11.2 mm, and preset limit safety wall thickness Wmin = 8.0 mm, the edge computing module can calculate the initial corrosion rate as approximately 0.43 mm / year based on the simplified model.

[0193] Furthermore, the edge computing module calculates the predicted remaining safe time based on the current measured wall thickness, the preset limit safe wall thickness, and the calculated corrosion rate. In one embodiment, the predicted remaining safe time Tr can be calculated according to the following relationship:

[0194] In the example above, when Wt=11.2mm, Wmin=8.0mm, and CR=0.43mm / year, the predicted remaining safe time Tr is approximately 7.4 years. To adapt to the needs of industrial site early warning management, the system can also convert the year into months for output, which is approximately 88.8 months.

[0195] In one implementation, the system sets multi-level early warning thresholds, where the first-level threshold can be set to 12 months, the second-level threshold to 6 months, and the third-level threshold to 3 months. When the predicted remaining safe time is less than or equal to the corresponding threshold, the corresponding level of early warning is triggered. The edge computing module can directly generate local early warning information and form a structured early warning message. The early warning message includes at least the monitoring point identifier, current wall thickness, predicted corrosion rate, and predicted remaining safe time.

[0196] In another example, when the real-time data for a monitoring point is: Wt=8.9mm, Wmin=8.0mm, V=17.2m / s, T=503K, and edge computing yields CR=0.62mm / year, the predicted remaining safe time Tr=(8.9-8.0) / 0.62≈1.45 years, or approximately 17.4 months. Although this result does not reach the 12-month warning threshold, it is close to the Level 1 warning range. The system can mark this monitoring point as a key focus and increase the upload frequency. If subsequent calculations show a decrease to within 12 months, a Level 1 warning will be triggered immediately.

[0197] In this implementation, the early warning information generated at the edge can be directly sent to the field control system via a local interface or industrial communication protocol, thereby enabling rapid response to abnormal operating conditions and reducing system response latency. In one implementation, the total processing time from receiving monitoring data to completing the initial early warning output at the edge can be controlled within 3 seconds.

[0198] Meanwhile, the cloud-based intelligent layer, as the core analysis unit of the system, is used to perform high-precision processing on the uploaded data. The cloud-based intelligent layer fuses and analyzes multi-source data, and constructs a complete corrosion rate prediction model based on the flow rate-accelerated corrosion mechanism, accurately calculating the corrosion rate. Simultaneously, the cloud-based intelligent layer adaptively updates the model parameters based on historical monitoring data, enabling the model to dynamically adjust according to changes in operating conditions.

[0199] In one implementation, the cloud-based intelligent layer employs a parameter fitting method based on time-series data to periodically update key parameters in the model. The update cycle can be set to 24 hours, and the rolling time window length can be set to 15 or 30 days. The cloud-based intelligent layer then distributes the updated model parameters to the edge computing module to achieve model collaboration between the cloud and the edge.

[0200] For example, for a certain monitoring point, the initial value on the edge side is K1=0.0108. Rapid calculations were performed using K1=1.75 and Q1=5000J / mol; after refitting based on historical data from the last 30 days, the updated parameters K2=0.0115 were obtained. The values ​​were set to 1.82 and Q2 = 5250 J / mol, and were distributed to the edge computing module in the early morning of the following day. After the update, the edge computing module can more accurately reflect the corrosion development trend under the current operating conditions.

[0201] Furthermore, the system employs a two-tiered early warning mechanism. Rapid early warnings are achieved at the edge, while high-precision early warning results and analysis reports are generated in the cloud. The structured early warning messages generated in the cloud include not only basic monitoring data but also analysis results of the dominant corrosion influencing factors and suggestions for optimizing process parameters, thus providing a basis for operation and maintenance decisions.

[0202] Regarding control linkage, the control linkage layer supports multiple control paths. In one implementation, when a high-risk warning is detected at the edge, control commands can be directly sent to the distributed control system for rapid intervention. In another implementation, the cloud-based intelligent layer, based on comprehensive analysis results, sends optimized control strategies to the distributed control system through a standardized industrial communication interface for refined regulation.

[0203] For example, when the system identifies high flow velocity as the dominant corrosion factor in a pipe section, it can output the following suggestions to the distributed control system: reduce the speed of the relevant pump sets by 2% to 5%, so that the flow velocity in the pipe is reduced from 16.5 m / s to below 14.5 m / s; or switch to a standby parallel pipeline to reduce the local scouring intensity of the target pipe section. When the system identifies temperature as the dominant influencing factor, it can recommend controlling the medium temperature below 260℃.

[0204] Regarding data transmission, the system supports multiple data transmission strategies. Under normal operating conditions, monitoring data is uploaded according to preset cycles, such as wall thickness data every 5 minutes and flow rate and temperature data every 1 minute. When an anomaly is detected, the system automatically switches to a high-frequency upload mode, such as uploading key parameter data at 30-second intervals to improve data real-time performance. In the event of a communication interruption, the edge computing module can cache the data locally and re-transmit it after communication is restored. In one implementation, the gateway's local cache capacity can store at least 72 hours of monitoring data.

[0205] In terms of implementation, this method includes the following steps: collecting pipe wall thickness, flow velocity and temperature data through monitoring nodes; processing the data in real time in the edge computing module and calculating the preliminary corrosion rate and remaining safe time; performing high-precision corrosion prediction and parameter updates in the cloud intelligent layer; generating early warning information based on the prediction results; and sending the early warning information to the control system through the control linkage layer and executing the corresponding control response.

[0206] Through the above technical solution, this implementation method realizes the coordinated operation of edge computing and cloud intelligent analysis, which significantly improves the response speed and operational reliability of the system while ensuring computing accuracy. It is particularly suitable for industrial application scenarios with high real-time requirements or limited network environment.

[0207] Example 4: Abnormal Operating Condition Identification and Diagnosis Example In one specific embodiment, the intelligent pipeline monitoring and early warning system provided by the present invention further has the functions of abnormal working condition identification and corrosion mechanism diagnosis, which is used to identify atypical corrosion behavior and analyze its causes, thereby improving the system's response capability to sudden risks.

[0208] In this embodiment, the system includes a field perception layer, a data transmission layer, a cloud intelligence layer, and a control linkage layer, wherein the abnormal operating condition identification and diagnosis function is mainly implemented by the cloud intelligence layer.

[0209] Specifically, the on-site sensing layer collects pipeline operation data through multi-parameter intelligent monitoring nodes, including pipeline wall thickness, fluid flow velocity, fluid temperature, and optional water quality parameters (such as pH value or electrochemical noise signal). In one embodiment, the wall thickness data sampling period is 5 minutes, the flow velocity and temperature sampling period is 1 minute, and the water quality parameter sampling period is 5 minutes.

[0210] After receiving the aforementioned multi-source data, the cloud-based intelligent layer first performs data preprocessing, including time alignment, initial screening of outliers, and compensation for missing values. In one implementation, the system uses a sliding time window for data processing, with a window length that can be set to 30 minutes or 60 minutes, to construct a continuous time series dataset.

[0211] In this implementation, the system constructs an abnormal operating condition identification model to identify operating modes that deviate significantly from normal operating conditions. The abnormality identification model can be implemented using a combination of statistical analysis methods and machine learning methods.

[0212] In one implementation, the system establishes a baseline model based on historical normal operation data and calculates the statistical characteristics of each parameter, including the mean μ, standard deviation σ, and rate of change. Real-time data is deemed abnormal when it meets any of the following conditions: The current flow velocity deviates from the historical average by more than ±3σ; The temperature changed by more than ±15℃ within 10 minutes; The corrosion rate increased by more than 30% over three consecutive sampling periods; Furthermore, in one implementation, the system employs an anomaly detection algorithm based on Isolation Forest or clustering analysis (such as K-means) to analyze multi-parameter joint features. When the anomaly score exceeds a preset threshold (e.g., 0.7–0.9), it is determined to be an abnormal operating condition.

[0213] In one specific example, the historical average flow velocity at a certain monitoring point was 14.5 m / s with a standard deviation of 1.2 m / s. When the real-time monitored flow velocity increased to 18.2 m / s, exceeding the mean + 3σ (i.e., 17.1 m / s), the system determined it to be an abnormal flow velocity. Simultaneously, if the corrosion rate increased from 0.45 mm / year to 0.70 mm / year during this period, an increase exceeding 50%, then an abnormal acceleration in corrosion was further confirmed.

[0214] After identifying abnormal operating conditions, the system enters the diagnostic phase. This diagnostic process is based on multi-parameter correlation analysis to identify the dominant influencing factors of corrosion rate changes.

[0215] In one implementation, the system calculates the contribution of each parameter to the change in corrosion rate, for example, through correlation coefficients or characteristic importance analysis methods: Flow rate contribution Temperature contribution Contribution of water quality parameters When the contribution of a certain parameter exceeds 60%, it is determined to be the dominant corrosion factor.

[0216] For example, in the example above, the system calculated that the contribution of flow velocity was 72%, the contribution of temperature was 18%, and the contribution of water quality was 10%, so it was determined that the anomaly was mainly caused by a sudden increase in flow velocity.

[0217] Furthermore, in one implementation, the system can combine digital twin models or historical data comparisons to further analyze the causes of anomalies, such as determining whether they are caused by the following factors: Changes in the pump unit's operating status led to a sudden increase in flow velocity; Abnormal valve opening leads to changes in the local flow field; Fluctuations in water quality lead to a deterioration of the corrosive environment; Sensor drift or malfunction can cause measurement anomalies.

[0218] After the diagnostic results are generated, the system outputs structured diagnostic information and sends it along with the early warning information to the control linkage layer. The diagnostic information includes: Anomaly type (e.g., "accelerated corrosion due to sudden increase in flow rate"); Dominant influencing factors; Parameter change trend; Recommended measures.

[0219] In one implementation, the system may output the following control recommendations: When flow rate is the dominant factor, it is recommended to reduce the pump speed by 2% to 5%, or control the flow rate below 15 m / s; When temperature is the dominant factor, it is recommended to reduce the operating temperature by 10℃ to 20℃. When water quality is the dominant factor, it is recommended to adjust the pH to the range of 9.0 to 9.5 or increase the dosage of corrosion inhibitor to 15 ppm to 25 ppm.

[0220] In terms of data transmission, when an abnormal operating condition is detected, the system automatically switches the data upload frequency from the normal mode (1 minute or 5 minutes) to a high-frequency mode, such as once every 30 seconds, in order to conduct fine tracking of the abnormal process.

[0221] In terms of implementation, this method includes the following steps: collecting multi-source operating data through monitoring nodes; performing data preprocessing and anomaly identification in the cloud intelligent layer; when an abnormal operating condition is identified, performing multi-parameter correlation analysis to determine the dominant factors; generating diagnostic results and early warning information; and sending the information to the distributed control system through the control linkage layer and executing the corresponding control response.

[0222] Through the above technical solution, this implementation method can achieve rapid identification and accurate diagnosis of abnormal operating conditions during pipeline operation, enabling the system to not only have corrosion prediction capabilities, but also the ability to explain and trace corrosion anomalies, thereby significantly improving the system's intelligence level and engineering application value.

[0223] Example 5: Example of Pipeline Corrosion Evolution Monitoring Based on Digital Twin In one specific embodiment, the intelligent pipeline monitoring and early warning system provided by the present invention further introduces digital twin technology to construct a virtual mapping model corresponding to the actual pipeline operating state, thereby realizing the visual expression and dynamic evolution analysis of pipeline corrosion state.

[0224] In this embodiment, the system includes a field perception layer, a data transmission layer, a cloud intelligence layer, and a control linkage layer, wherein the digital twin function is mainly implemented by the cloud intelligence layer.

[0225] Specifically, the cloud-based intelligent layer constructs a virtual pipeline model based on multi-source data collected by the field perception layer. The virtual pipeline model maintains the same spatial structure as the actual pipeline, including the pipeline geometry, the distribution of monitoring points, and information on key equipment nodes, and is correlated with the actual monitoring data in real time through a data mapping mechanism.

[0226] In one implementation, each monitoring node corresponds to a mapping node in the virtual model, and the mapping node is associated with at least the following parameters: Current measured wall thickness; Fluid velocity; Fluid temperature; Predicting corrosion rates; Predict the remaining safe time.

[0227] Regarding data updates, the virtual pipeline model is dynamically updated according to a cycle consistent with the field data. In one implementation: The flow rate and temperature data are updated every 1 minute. The wall thickness data is updated every 5 minutes; The corrosion rate and remaining safety time are updated every 5 minutes or synchronously with the calculation cycle.

[0228] In this way, the virtual pipeline model can reflect the actual pipeline operation status in real time.

[0229] Furthermore, in one implementation, the cloud-based intelligent layer performs time-dimensional evolution calculations on the virtual pipeline model based on a corrosion rate prediction model to achieve dynamic simulation of the corrosion process. In one implementation, the system can set the prediction time step to 1 day or 7 days to predict and calculate wall thickness changes over the next 3 months, 6 months, or 12 months.

[0230] Specifically, within each prediction time step, the system iteratively updates the wall thickness based on the current predicted corrosion rate to obtain the wall thickness distribution at future moments, and calculates the corresponding predicted remaining safe time accordingly.

[0231] In one implementation, the virtual pipeline model can use color mapping to visualize corrosion risk, for example: When the predicted remaining safe time is greater than 12 months, it is displayed in green; When the predicted remaining safe period is between 6 and 12 months, it is displayed in yellow; When the predicted remaining safe period is between 3 and 6 months, it is displayed in orange; When the predicted remaining safe time is less than or equal to 3 months, it is displayed in red.

[0232] Using the methods described above, maintenance personnel can intuitively identify high-risk areas and corrosion trends.

[0233] In one specific example, the initial measured wall thickness of a pipe segment is 12.0 mm, the preset limit safety wall thickness is 8.0 mm, and the predicted corrosion rate is 0.80 mm / year. The system performs evolution calculations with a time step of one month. After six months, the predicted wall thickness of this pipe segment is approximately 11.6 mm, and after 12 months, it is approximately 11.2 mm. This corresponds to a gradual decrease in the remaining safe time, and the color in the virtual model gradually changes from green to yellow.

[0234] Furthermore, in one implementation, the cloud-based intelligent layer can perform multi-parameter correlation analysis based on the virtual pipeline model to identify the dominant corrosion factors at different time stages. For example, during the flow rate increase stage, the system can identify the increased contribution of flow rate to the corrosion rate and mark the corresponding affected area in the virtual model.

[0235] In one implementation, the digital twin model can also be used to assist in the verification of control strategies. Specifically, before actually implementing process adjustments, the system can simulate the impact of different control strategies on the corrosion rate and remaining safety time in a virtual model, such as simulating corrosion changes after a 5% reduction in flow rate or a 10°C reduction in temperature, thereby providing a reference for control decisions.

[0236] Regarding data interaction, the analysis results generated by the virtual pipeline model can be associated with structured early warning messages for output. In one implementation, the early warning message may include the following extended information: Corrosion development trend prediction results; Future trends in early warning levels; The evolution of the dominant corrosion factors.

[0237] In one implementation, when the virtual model predicts that a certain pipeline segment will enter a higher-level warning range within the next month, the system can generate an early warning prompt in advance, thereby achieving proactive risk management.

[0238] In terms of implementation, this method includes the following steps: constructing a virtual pipeline model based on monitoring node data; mapping real-time data to the virtual model; performing time evolution calculations based on the corrosion rate prediction model; outputting a visualized corrosion distribution and risk level; and generating early warning and control recommendations based on the prediction results.

[0239] Through the above technical solution, this implementation method introduces digital twin modeling and dynamic evolution analysis capabilities on the basis of existing multi-source data fusion and corrosion mechanism prediction, so that the system can not only reflect the current corrosion state, but also make a visual prediction of the future corrosion development trend, thereby further improving the system's intelligence level and engineering application value.

[0240] In summary, this invention constructs an intelligent pipeline monitoring and early warning system comprising a field sensing layer, a data transmission layer, a cloud-based intelligent layer, and a control linkage layer. This system achieves multi-source data fusion sensing of pipeline corrosion status, dynamic prediction based on the velocity-accelerated corrosion mechanism, and closed-loop linkage control with a distributed control system. Compared with existing technologies, this invention not only enables real-time monitoring of the corrosion process but also accurately predicts corrosion development trends and remaining safe time, further driving process parameter adjustments. This transforms the system from passive detection to proactive prevention and control, demonstrating significant engineering application value and promising prospects for wider adoption.

[0241] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, various modifications, substitutions or equivalent improvements can be made to the technical solutions of the present invention without departing from the spirit and substance of the present invention, and such modifications, substitutions or improvements should all fall within the scope of protection of the present invention.

[0242] Furthermore, the functional modules described in this specification can be implemented in hardware, software, or a combination of hardware and software; the specific implementation of each module does not constitute a limitation on the scope of protection of this invention.

[0243] The terminology used in this specification is for describing particular embodiments only and is not intended to limit the invention. Unless otherwise expressly defined, expressions such as "comprising," "including," etc., should be understood to mean, but are not limited to, those meanings.

[0244] Finally, it should be understood that the scope of protection of this invention shall be determined by the appended claims, and the specification and drawings are only used to interpret the claims.

Claims

1. A pipeline intelligent monitoring and early warning system based on multi-source data fusion and flow velocity-accelerated corrosion mechanism, characterized in that, include: The field sensing layer includes multi-parameter intelligent monitoring nodes deployed at key parts of the pipeline; each monitoring node integrates: Electromagnetic ultrasonic thickness measurement module, used for continuous measurement of pipe wall thickness; Flow velocity sensors are used to measure the flow velocity of fluids in pipes in real time; and Temperature sensor for real-time measurement of fluid temperature; The cloud-based intelligent layer includes an intelligent analysis server and a data processing program running on it. The data processing program is used to receive and integrate wall thickness, flow rate and temperature data from the field sensing layer, establish a corrosion rate prediction model based on the flow rate-accelerated corrosion mechanism coupled with multiple parameters such as flow rate and temperature, dynamically calculate and predict the corrosion rate, and calculate and predict the remaining safe time based on the current measured wall thickness and the preset limit safe wall thickness. The control linkage layer establishes a communication connection with the factory's distributed control system through a standardized industrial communication interface. It is used to send a structured early warning message containing at least the predicted remaining safety time to the distributed control system, so that the distributed control system triggers the corresponding process control response, thereby forming a closed-loop linkage of corrosion monitoring, analysis, early warning and control.

2. The intelligent pipeline monitoring and early warning system according to claim 1, characterized in that, The corrosion rate prediction model satisfies the following expression relationship: Where CR is the predicted corrosion rate, K is the overall rate constant, and V is the fluid velocity. Where is the flow rate index, Q is the apparent activation energy, R is the ideal gas constant, T is the absolute temperature of the fluid, and M is the material factor.

3. The intelligent pipeline monitoring and early warning system according to claim 2, characterized in that, The cloud-based intelligent layer is used to adaptively update the model parameters of the corrosion rate prediction model based on historical monitoring data, so that the model parameters are dynamically adjusted according to changes in operating conditions.

4. The intelligent pipeline monitoring and early warning system according to claim 3, characterized in that, The adaptive update of the model parameters is achieved through a parameter fitting algorithm based on time series data.

5. The intelligent pipeline monitoring and early warning system according to claim 2, characterized in that, The corrosion rate prediction model also includes a water quality influence function f(pH) to characterize the effect of fluid pH on the corrosion rate; the monitoring nodes of the field sensing layer also include pH sensors or electrochemical noise sensors.

6. The intelligent pipeline monitoring and early warning system according to claim 1, characterized in that, The predicted remaining safety time is calculated based on the relationship between the current measured wall thickness, the preset limit safety wall thickness, and the predicted corrosion rate. The cloud-based intelligent layer is used to generate warning events of corresponding levels based on the comparison results between the predicted remaining safe time and the preset multi-level warning thresholds.

7. The intelligent pipeline monitoring and early warning system according to claim 1, characterized in that, The structured early warning message also includes one or more of the following information: unique identifier of the monitoring point, geographical location description, current measured wall thickness value, analysis results of dominant corrosion influencing factors, and suggested range of operating parameters based on model generation.

8. The intelligent pipeline monitoring and early warning system according to claim 1, characterized in that, The standardized industrial communication interface includes an OPC UA interface and / or a Modbus TCP interface; The distributed control system executes at least one of the following control responses based on the structured early warning message: generating an alarm signal, outputting a process control interface, or outputting control commands for adjusting fluid flow rate, switching pipelines, or adjusting fluid chemical parameters.

9. The intelligent pipeline monitoring and early warning system according to claim 1, characterized in that, The monitoring node also includes a local processing unit and a wireless transmission unit, wherein the wireless transmission unit adopts an NB-IoT communication module or a LoRa communication module. The system also includes a data transmission layer, which is used to upload the data of the monitoring node to the cloud-based intelligent layer in cooperation with the gateway through an NB-IoT network or a LoRa network.

10. The intelligent pipeline monitoring and early warning system according to claim 1, characterized in that, The electromagnetic ultrasonic thickness measurement module uses a non-contact electromagnetic ultrasonic transducer and is fixed to the outer wall of the pipe by a clamp structure. The monitoring node uses a hybrid power supply method of batteries and solar panels.

11. A closed-loop control method for pipeline corrosion based on the system according to any one of claims 1 to 10, characterized in that, include: Sensing steps: Simultaneously collect pipe wall thickness, fluid flow velocity, and fluid temperature data through the same monitoring node; Prediction steps: The collected data is fused and processed in the cloud-based intelligent layer, and the corrosion rate is calculated based on the flow rate-accelerated corrosion mechanism model, thereby calculating the remaining safe time. Decision-making steps: When the predicted remaining safety time meets the preset early warning conditions, a structured early warning message is generated; Control steps: The structured early warning message is sent to the distributed control system through a standardized industrial communication interface, so that the distributed control system executes the corresponding process control response.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the prediction step and the decision step in the method of claim 11.

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