A production process optimization method and system for anti-corrosion pipelines

Abstract

The present invention relates to the technical field of production process optimization, and in particular to a production process optimization method and system for anti-corrosion pipelines. The method comprises the following steps: obtaining pipeline application environment data; performing environmental impact analysis on the pipeline application environment data to generate pipeline application environment impact data; performing pipeline anti-corrosion performance analysis on the anti-corrosion pipeline through the pipeline application environment impact data to generate pipeline anti-corrosion environmental impact data; performing corrosion mechanism analysis on the anti-corrosion pipeline using the pipeline anti-corrosion environmental impact data to generate pipeline corrosion mechanism data; performing secondary anti-corrosion candidate material screening on the pipeline corrosion mechanism data through a preset material database to generate second pipeline anti-corrosion candidate material data; performing material performance analysis based on the second pipeline anti-corrosion candidate material data to generate production optimization material performance data. The present invention improves the comprehensiveness of the optimization of the production process of anti-corrosion pipelines through systematization, data support, comprehensive consideration and predictive strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of production process optimization, and in particular to a production process optimization method and system for anti-corrosion pipelines. Background Art

[0002] With the rapid development of the chemical industry and the oil and gas industry, the requirements for pipeline anti-corrosion performance are constantly increasing, and traditional anti-corrosion methods can no longer meet the needs. In the mid-20th century, new anti-corrosion technologies began to appear, such as polyethylene film coating and epoxy resin coating. These technologies greatly improved the corrosion resistance of pipelines, but some problems were also exposed during the application process, such as complex construction and high cost. With the development of materials science, chemical engineering and other fields, new anti-corrosion technologies continue to emerge. In recent years, coating technology has been widely used, such as epoxy coal tar coating and two-component polyurethane coating. These technologies have high corrosion resistance and long service life, and have gradually become the mainstream technology for pipeline anti-corrosion. With the development of industrial automation technology, the production process of anti-corrosion pipelines has also been improved in intelligence and automation. From material selection, coating construction to quality inspection, digital and intelligent management has been realized, which greatly improves production efficiency and product quality and reduces production costs. However, current traditional methods often only focus on a single factor, such as material selection or coating quality, while ignoring the combined impact of multiple factors such as pipeline application environment, corrosion mechanism, production structure, etc. At the same time, there is a lack of sufficient data support in the production process of anti-corrosion pipelines, and decisions often rely on experience and subjective judgment, resulting in the low comprehensiveness of the optimization of the anti-corrosion pipeline production process. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for optimizing the production process of anti-corrosion pipelines to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for optimizing the production process of anti-corrosion pipelines is provided, the method comprising the following steps:

[0005] Step S1: Acquire pipeline application environment data; perform environmental impact analysis on the pipeline application environment data to generate pipeline application environment impact data; perform pipeline anti-corrosion performance analysis on the anti-corrosion pipeline using the pipeline application environment impact data to generate pipeline anti-corrosion environmental impact data;

[0006] Step S2: using the pipeline anti-corrosion environmental impact data to analyze the corrosion mechanism of the anti-corrosion pipeline, and generating pipeline corrosion mechanism data; performing secondary anti-corrosion candidate material screening on the pipeline corrosion mechanism data through a preset material database, and thereby generating second pipeline anti-corrosion candidate material data; performing material performance analysis based on the second pipeline anti-corrosion candidate material data, and generating production optimization material performance data;

[0007] Step S3: Based on the production optimization material performance data, the pipeline production structure analysis is performed on the anti-corrosion pipeline to generate the anti-corrosion pipeline production structure data; the pipeline buried area exposure analysis is performed on the anti-corrosion pipeline production structure data to generate the pipeline buried exposure area data; the corrosion area is divided according to the pipeline buried exposure area data to generate the pipeline prone to corrosion area data and the pipeline non-corrosion area data; hierarchical anti-corrosion treatment is performed through the pipeline prone to corrosion area data and the pipeline non-corrosion area data to generate the pipeline hierarchical anti-corrosion coating strategy; the anti-corrosion coating optimization quality assessment is performed on the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate the pipeline anti-corrosion coating optimization quality assessment data;

[0008] Step S4: Based on the preset temperature and time, the anti-corrosion pipeline is subjected to curing temperature stability evaluation according to the pipeline anti-corrosion coating optimization quality evaluation data, and curing temperature gradient stability data is generated; the anti-corrosion pipeline is subjected to coating cooling treatment according to the curing temperature gradient stability data, and coating cooling data is generated;

[0009] Step S5: Perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data; perform production optimization life prediction on the quality inspection data to generate anti-corrosion pipeline optimized production life prediction data; construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimized production life prediction data to generate an anti-corrosion pipeline production process optimized maintenance strategy.

[0010] The present invention can understand the influence of the environmental conditions of the pipeline on its anti-corrosion performance by obtaining pipeline application environment data and performing environmental impact analysis. The generated pipeline application environment impact data can provide basic data support. By performing corrosion mechanism analysis and material screening on the anti-corrosion pipeline, the candidate material most suitable for the anti-corrosion pipeline can be determined. The data generated by the material performance analysis helps to optimize the production process and material performance. Performing anti-corrosion pipeline production structure analysis, exposure analysis and corrosion area division can help understand the exposure of the pipeline in the buried area and the location of the corrosion-prone area. Through hierarchical anti-corrosion treatment and anti-corrosion coating optimization quality evaluation, anti-corrosion coating strategies for different areas can be formulated to improve the anti-corrosion performance of the pipeline. Curing temperature stability evaluation and coating cooling treatment help to ensure the quality and stability of the anti-corrosion coating. The generated curing temperature gradient stability data and coating cooling data provide a basis for the subsequent treatment of the pipeline. Through quality inspection and production optimization life prediction, pipeline quality problems can be discovered in time and their optimized production life can be predicted. Based on these data, a pipeline maintenance strategy can be constructed to extend the service life of the pipeline and optimize the production process. Therefore, the present invention improves the comprehensiveness of the optimization of the anti-corrosion pipeline production process through systematization, data support, comprehensive consideration and predictive strategy.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: using the IoT sensor network to obtain pipeline application environment data;

[0013] Step S12: performing data preprocessing on the pipeline application environment data to generate standard pipeline application environment data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization;

[0014] Step S13: Performing environmental impact analysis based on standard pipeline application environment data to generate pipeline application environment impact data;

[0015] Step S14: performing pipeline anti-corrosion performance analysis on the anti-corrosion pipeline using pipeline application environmental impact data to generate pipeline anti-corrosion environmental impact data.

[0016] The present invention obtains pipeline application environment data through the Internet of Things sensor network, and can realize real-time and comprehensive monitoring of the pipeline environment. The sensor network can cover a wide area and provide a variety of data types to improve the richness and accuracy of the data. Through data preprocessing (including data cleaning, missing value filling and data standardization), the quality and consistency of the data can be guaranteed. This not only improves the reliability of the data, but also provides a high-quality data basis for subsequent analysis. Based on the standardized pipeline application environment data, environmental impact analysis can be carried out to comprehensively evaluate the impact of environmental factors on the pipeline. This analysis helps to identify potential risks, provide early warnings, and formulate corresponding preventive measures. By analyzing the anti-corrosion performance of the pipeline, the anti-corrosion effect of the pipeline under different environmental conditions can be evaluated and predicted. This helps to optimize anti-corrosion materials and processes, extend the service life of the pipeline, and reduce maintenance costs.

[0017] Preferably, step S2 comprises the following steps:

[0018] Step S21: Analyze the corrosion mechanism of the anti-corrosion pipeline using the pipeline anti-corrosion environmental impact data to generate pipeline corrosion mechanism data;

[0019] Step S22: screening the pipeline corrosion mechanism data for first pipeline anti-corrosion candidate materials through a preset material database to obtain first pipeline anti-corrosion candidate material data;

[0020] Step S23: performing material performance simulation on the first pipeline anti-corrosion candidate material data to generate material simulation performance data; performing material formula optimization on the pipeline anti-corrosion candidate material data based on the material simulation performance data to generate material optimization formula data;

[0021] Step S24: Perform sample experiment verification according to the material optimization formula data to generate material optimization sample test data; screen the first pipeline anti-corrosion candidate material data for the second pipeline anti-corrosion candidate material through the material optimization sample test data, thereby generating the second pipeline anti-corrosion candidate material data; perform material performance analysis based on the second pipeline anti-corrosion candidate material data to generate production optimization material performance data.

[0022] The present invention analyzes the corrosion mechanism through pipeline anti-corrosion environmental impact data and generates pipeline corrosion mechanism data, which helps to deeply understand the causes and processes of corrosion and provide a scientific basis for material selection and anti-corrosion strategies. The pipeline corrosion mechanism data is screened in the first round using a preset material database to quickly obtain the first pipeline anti-corrosion candidate material data, improve screening efficiency, and reduce test time and cost. The candidate materials are simulated for performance and material simulation performance data is generated, which can virtually evaluate the material performance before the actual test, reduce experimental risks, and save costs. The material formula is optimized based on the simulated performance data, and the material optimization formula data is generated to ensure the anti-corrosion effect and applicability of the material formula under a specific environment. The material optimization formula data is verified through sample experiments, and the material optimization sample test data is generated to ensure the effectiveness and reliability of the optimized formula in practical applications. The pipeline anti-corrosion candidate materials are screened in the second round using the material optimization sample test data to generate the second pipeline anti-corrosion candidate material data, further improving the accuracy of material selection.

[0023] Preferably, step S21 includes the following steps:

[0024] Step S211: using density functional theory to perform microscopic model simulation on pipeline anti-corrosion environmental impact data to generate a pipeline corrosion microscopic model;

[0025] Step S212: performing macro-model simulation on pipeline anti-corrosion environmental impact data using a finite element analysis tool to generate a pipeline corrosion macro-model;

[0026] Step S213: simulate the corrosion reaction path of the pipeline corrosion micromodel to generate corrosion reaction path simulation data; analyze the corrosion energy change of the pipeline corrosion micromodel through the corrosion reaction path simulation data to generate corrosion energy change data; confirm the corrosion reaction mechanism based on the corrosion energy change data and the corrosion reaction path simulation data to obtain corrosion reaction mechanism data;

[0027] Step S214: simulate the stress distribution of the pipeline corrosion macro model to obtain pipeline corrosion stress distribution data; perform corrosion extension analysis on the pipeline corrosion macro model based on the pipeline corrosion stress distribution data to generate pipeline corrosion extension data; perform corrosion mechanism verification on the pipeline corrosion extension data and the corrosion reaction mechanism data to generate pipeline corrosion mechanism data.

[0028] The present invention reveals the reaction mechanism and behavior of materials in a corrosive environment by providing accurate atomic-level corrosion process analysis. Through density functional theory (DFT) simulation, detailed electronic structure information is obtained, which helps to understand the nature of corrosion reactions. Through finite element analysis (FEA), the stress distribution and deformation of the pipeline in the actual use environment can be obtained. The macro model provides a system-level perspective, so that the microscopic simulation results can be better applied to actual engineering scenarios. The corrosion reaction path simulation provides detailed reaction steps and mechanisms to help identify key corrosion reactions. Energy change analysis can reveal the changes in energy during the corrosion process and identify the most likely corrosion reactions. The comprehensive reaction path and energy change data confirm the corrosion mechanism, thereby providing a theoretical basis for anti-corrosion measures. The corrosion reaction path simulation provides detailed reaction steps and mechanisms to help identify key corrosion reactions. Energy change analysis can reveal the changes in energy during the corrosion process and identify the most likely corrosion reactions. The comprehensive reaction path and energy change data confirm the corrosion mechanism, thereby providing a theoretical basis for anti-corrosion measures.

[0029] Preferably, step S3 comprises the following steps:

[0030] Step S31: performing pipeline production structure analysis on the anti-corrosion pipeline based on the production optimization material performance data to generate anti-corrosion pipeline production structure data;

[0031] Step S32: performing pipeline buried area exposure analysis on the anti-corrosion pipeline production structure data to generate pipeline buried exposed area data; performing corrosion area division on the anti-corrosion pipeline production structure data according to the pipeline buried exposed area data to generate pipeline prone to corrosion area data and pipeline non-corrosion area data, wherein the pipeline prone to corrosion area data includes the bottom area, the joint area, the high temperature area and the damaged area, and the pipeline non-corrosion area data includes the middle area and the upper area;

[0032] Step S33: performing hierarchical anti-corrosion treatment on the anti-corrosion pipeline according to the pipeline prone-corrosion area data and the pipeline non-corrosion area data, and generating a pipeline hierarchical anti-corrosion coating strategy, wherein the pipeline hierarchical anti-corrosion coating strategy includes a pipeline single-layer anti-corrosion coating strategy and a pipeline double-layer anti-corrosion coating strategy;

[0033] Step S34: Optimize the anti-corrosion coating of the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate pipeline anti-corrosion coating optimization data; perform coating quality evaluation on the pipeline anti-corrosion coating optimization data to generate pipeline anti-corrosion coating optimization quality evaluation data.

[0034] The present invention can design an anti-corrosion pipeline structure that best suits actual working conditions by analyzing production optimization material performance data. Detailed production structure data is provided to lay the foundation for subsequent exposure analysis and anti-corrosion treatment. Through detailed exposure analysis of buried areas, the corrosion risk of pipelines under different buried conditions can be accurately identified. According to the corrosion area division data, the corrosion-prone and non-corrosion-prone areas of the pipeline can be targeted to improve the accuracy of anti-corrosion measures. Hierarchical anti-corrosion treatment makes the anti-corrosion strategy more targeted and effective. Different anti-corrosion coating strategies are adopted in different areas to maximize the anti-corrosion effect. The flexible application of single-layer and double-layer anti-corrosion coating strategies ensures the best anti-corrosion protection without increasing excessive costs. Anti-corrosion coating optimization ensures the best combination of coating thickness and material selection to provide lasting anti-corrosion protection. Coating quality assessment can identify and improve potential defects in the coating to ensure the long-term effectiveness and reliability of the coating.

[0035] Preferably, step S33 includes the following steps:

[0036] Step S331: performing double-layer anti-corrosion coating on the pipeline prone-corrosion area according to the pipeline prone-corrosion area data, and generating double-layer anti-corrosion coating data for the pipeline prone-corrosion area, wherein the double-layer anti-corrosion coating includes coating primer coating and coating intermediate layer coating;

[0037] Step S332: performing a single-layer anti-corrosion coating on the pipeline non-corrosion area according to the pipeline non-corrosion area data, and generating pipeline non-corrosion area single-layer anti-corrosion coating data;

[0038] Step S333: When it is confirmed that the data of the pipeline prone to corrosion area is the bottom area and the joint area, the coating thickness of the bottom area and the joint area is increased to generate ordinary coating thickness adjustment data; when it is confirmed that the data of the pipeline prone to corrosion area is the high temperature area, the high temperature coating thickness of the high temperature area is increased to generate high temperature coating thickness adjustment data; when it is confirmed that the data of the pipeline prone to corrosion area is the damaged area, the touch-up coating thickness of the damaged area is increased to generate touch-up coating thickness adjustment data;

[0039] Step S334: when it is confirmed that the pipeline corrosion-resistant area data is the middle area, the first coating thickness is reduced in the middle area to obtain the first coating thickness adjustment data; when it is confirmed that the pipeline corrosion-resistant area data is the upper area, the second coating thickness is reduced in the upper area to obtain the second coating thickness adjustment data, and the first coating thickness adjustment data is always greater than the second coating thickness adjustment data;

[0040] Step S335: Integrate the double-layer anti-corrosion coating data of the pipeline's prone-to-corrosion area with the ordinary coating thickness adjustment data, the high-temperature coating thickness adjustment data and the touch-up coating thickness adjustment data to generate a double-layer anti-corrosion coating strategy for the pipeline; integrate the single-layer anti-corrosion coating data of the pipeline's non-easy-to-corrode area with the first coating thickness adjustment data and the second coating thickness adjustment data to generate a single-layer anti-corrosion coating strategy for the pipeline.

[0041] The present invention provides multiple protections for corrosion-prone areas and enhances the anti-corrosion effect through a double-layer anti-corrosion coating (including a primer and an intermediate layer). The primer coating provides basic protection and adhesion, and the intermediate layer coating further improves the anti-corrosion performance. The single-layer anti-corrosion coating simplifies the treatment of non-corrosion-prone areas, reduces costs, and ensures sufficient anti-corrosion protection. Reasonable allocation of resources improves the balance between economy and anti-corrosion effect. Thickness adjustment is carried out for different corrosion-prone areas (bottom, joint, high temperature, damaged), and targeted anti-corrosion measures are provided to improve the anti-corrosion effect and durability. A special high-temperature coating is used in the high-temperature area to ensure the anti-corrosion performance under high temperature conditions. By reducing the coating thickness in the non-corrosion-prone area, materials and costs are saved, while ensuring the effectiveness of the anti-corrosion coating. The coating thickness of the middle and upper areas is adjusted differently, and the coating distribution is optimized to make the use of anti-corrosion resources more reasonable. The anti-corrosion coating data of the corrosion-prone and non-corrosion-prone areas are integrated to form a complete coating strategy to ensure that the anti-corrosion needs of each area are met. The integration of double-layer and single-layer coating strategies provides a systematic anti-corrosion solution, improving the overall efficiency and effectiveness of anti-corrosion treatment.

[0042] Preferably, step S4 comprises the following steps:

[0043] Step S41: curing the anti-corrosion pipeline based on the preset temperature and time according to the pipeline anti-corrosion coating optimization quality assessment data, and using the temperature sensor to collect real-time temperature, thereby obtaining the pipeline coating curing temperature data;

[0044] Step S42: performing coating curing temperature distribution analysis on the pipeline coating curing temperature data to generate a coating curing temperature distribution diagram; performing temperature gradient analysis on the coating curing temperature distribution diagram to generate coating curing temperature gradient data;

[0045] Step S43: performing stability evaluation on the coating curing temperature gradient data to generate curing temperature gradient stability data; performing curing completeness detection on the anti-corrosion pipeline according to the curing temperature gradient stability data to generate curing completeness data of the anti-corrosion pipeline;

[0046] Step S44: performing coating cooling treatment on the anti-corrosion pipeline according to the anti-corrosion pipeline curing completeness data, thereby generating coating cooling data.

[0047] The present invention ensures that the anti-corrosion coating has the required mechanical properties and chemical stability through curing treatment. Real-time temperature acquisition combined with preset parameters ensures temperature control and stability during the curing process. Through temperature distribution analysis, the uniformity and change trend of temperature during the coating curing process are understood, providing a basis for subsequent quality evaluation. Temperature gradient data analysis helps to evaluate the temperature changes during the coating curing process and further optimize the curing parameters. Through stability evaluation, the temperature gradient during the coating curing process is ensured to be stable, thereby ensuring the quality and performance of the coating. Complete curing detection ensures that the coating is fully cured on the entire pipeline surface to avoid the influence of uncured parts. Cooling treatment ensures that the coating can be cooled quickly after curing to avoid thermal stress and quality defects. The generated coating cooling data provides a record and analysis of the cooling process, providing a reference for further process optimization.

[0048] Preferably, step S5 comprises the following steps:

[0049] Step S51: using a machine vision method to perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data;

[0050] Step S52: dividing the quality inspection data into data sets to generate a model training set and a model test set;

[0051] Step S53: using the random forest algorithm to perform model training on the model training set to generate an anti-corrosion pipeline life training model; using the model test set to perform model test iteration on the anti-corrosion pipeline life training model to generate an anti-corrosion pipeline life prediction model;

[0052] Step S54: Import the quality inspection data into the anti-corrosion pipeline life prediction model to perform production optimization life prediction, thereby generating anti-corrosion pipeline optimized production life prediction data; construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimized production life prediction data to generate an anti-corrosion pipeline production process optimization maintenance strategy.

[0053] The present invention realizes automated quality inspection by using machine vision technology, thereby improving the efficiency and accuracy of inspection. The generated quality inspection data provides a basis for subsequent model training. Data set division ensures the independence and reliability of the training model and the test model. The generalization ability and prediction accuracy of the model are improved. The life prediction model is constructed through the machine learning algorithm to achieve accurate prediction of the life of the anti-corrosion pipeline. Model test iteration ensures the robustness and reliability of the model and improves the credibility of the prediction results. The production process is optimized through the life prediction model, which improves the production life and quality of the anti-corrosion pipeline. The constructed maintenance strategy is based on the prediction data, which provides a scientific basis for the maintenance and management of the pipeline and extends the service life and performance of the pipeline.

[0054] Preferably, step S51 includes the following steps:

[0055] Step S511: using the camera on the production line to collect images of the anti-corrosion pipeline to obtain an image of the anti-corrosion pipeline coating;

[0056] Step S512: segmenting the anti-corrosion pipeline coating image by the image edge region to generate the anti-corrosion pipeline coating core image;

[0057] Step S513: performing defect area recognition on the core image of the anti-corrosion pipeline coating to generate defect area data of the anti-corrosion pipeline coating;

[0058] Step S514: Associating the coating cooling data with the quality defects through the anti-corrosion pipeline coating defect area data to generate quality inspection data.

[0059] The present invention collects coating images in real time through the camera on the production line, realizes non-contact quality inspection, and avoids destructive inspection of the pipeline. The obtained image data provides a basis for subsequent image processing and analysis. Image edge area segmentation can accurately extract the core area of ​​the coating, remove irrelevant background information in the image, and is conducive to subsequent defect identification and analysis. The generated coating core image provides a clear image basis for defect identification. By identifying defects in the coating image, such as bubbles, cracks, etc., through image processing technology, the area of ​​the defects is quantitatively measured, and accurate defect data is provided. The defect area data reflects the quality of the coating and provides an important basis for subsequent quality defect association. By correlating and analyzing the defect area data with the coating cooling data, abnormal conditions related to the coating quality, such as abnormal temperature at the defect, can be identified. The generated quality inspection data can be used for subsequent quality evaluation and production optimization, improve the monitoring and control efficiency of the anti-corrosion pipeline coating quality, and ensure the stability and reliability of the pipeline quality.

[0060] In this specification, a production process optimization system for anti-corrosion pipelines is provided, which is used to execute the above-mentioned production process optimization method for anti-corrosion pipelines. The production process optimization system for anti-corrosion pipelines includes:

[0061] The pipeline anti-corrosion impact analysis module is used to obtain pipeline application environment data; perform environmental impact analysis on pipeline application environment data to generate pipeline application environment impact data; perform pipeline anti-corrosion performance analysis on anti-corrosion pipelines based on pipeline application environment impact data to generate pipeline anti-corrosion environmental impact data;

[0062] The pipeline material optimization module is used to analyze the corrosion mechanism of the anti-corrosion pipeline using the pipeline anti-corrosion environmental impact data to generate pipeline corrosion mechanism data; perform secondary anti-corrosion candidate material screening on the pipeline corrosion mechanism data through a preset material database to generate second pipeline anti-corrosion candidate material data; perform material performance analysis based on the second pipeline anti-corrosion candidate material data to generate production optimization material performance data;

[0063] The pipeline anti-corrosion coating optimization module is used to analyze the pipeline production structure of the anti-corrosion pipeline based on the production optimization material performance data to generate the anti-corrosion pipeline production structure data; perform pipeline buried area exposure analysis on the anti-corrosion pipeline production structure data to generate pipeline buried exposed area data; divide the corrosion area according to the pipeline buried exposed area data to generate pipeline prone to corrosion area data and pipeline non-corrosion area data; perform hierarchical anti-corrosion treatment through pipeline prone to corrosion area data and pipeline non-corrosion area data to generate pipeline hierarchical anti-corrosion coating strategy; perform anti-corrosion coating optimization quality assessment on the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate pipeline anti-corrosion coating optimization quality assessment data;

[0064] The pipeline curing analysis module is used to evaluate the curing temperature stability of the anti-corrosion pipeline based on the preset temperature and time according to the pipeline anti-corrosion coating optimization quality evaluation data, and generate curing temperature gradient stability data; the anti-corrosion pipeline is subjected to coating cooling treatment based on the curing temperature gradient stability data, thereby generating coating cooling data;

[0065] The pipeline optimization life prediction module is used to perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data; to perform production optimization life prediction on the quality inspection data to generate anti-corrosion pipeline optimization production life prediction data; to construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimization production life prediction data to generate an anti-corrosion pipeline production process optimization maintenance strategy.

[0066] The beneficial effect of the present invention is that through environmental impact analysis and corrosion mechanism analysis, the influence of the environment in which the pipeline is located on its corrosion can be better understood, so as to select suitable anti-corrosion materials and coatings, and improve the adaptability of the pipeline in various environments. By using production optimization material performance data and pipeline production structure analysis, corresponding anti-corrosion coating strategies can be designed for different pipeline parts, thereby optimizing the anti-corrosion performance and improving the corrosion resistance of the pipeline. By optimizing the production process of anti-corrosion pipelines and predicting their lifespan, production efficiency can be effectively improved, resource waste and production costs can be reduced, and efficient operation of the pipeline production process can be ensured. Through quality inspection data and optimized production life prediction data, an effective pipeline maintenance strategy can be established to promptly discover and repair problems in the pipeline, ensuring the long-term stable operation and safety of the pipeline. Therefore, the present invention improves the comprehensiveness of the optimization of the production process of anti-corrosion pipelines through systematization, data support, comprehensive consideration and predictive strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of the steps of a production process optimization method for anti-corrosion pipelines;

[0068] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0069] Figure 3 for Figure 2 Detailed implementation steps of step S21;

[0070] Figure 4 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0071] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0072] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0073] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0074] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0075] To achieve this, please refer to Figures 1 to 4 , a method for optimizing the production process of an anti-corrosion pipeline, the method comprising the following steps:

[0076] Step S1: Acquire pipeline application environment data; perform environmental impact analysis on the pipeline application environment data to generate pipeline application environment impact data; perform pipeline anti-corrosion performance analysis on the anti-corrosion pipeline using the pipeline application environment impact data to generate pipeline anti-corrosion environmental impact data;

[0077] Step S2: using the pipeline anti-corrosion environmental impact data to perform corrosion mechanism analysis on the anti-corrosion pipeline, and generate pipeline corrosion mechanism data; performing secondary anti-corrosion candidate material screening on the pipeline corrosion mechanism data through a preset material database, and thus generating second pipeline anti-corrosion candidate material data; performing material performance analysis based on the second pipeline anti-corrosion candidate material data, and generating production optimization material performance data;

[0078] Step S3: Based on the production optimization material performance data, the pipeline production structure analysis is performed on the anti-corrosion pipeline to generate the anti-corrosion pipeline production structure data; the pipeline buried area exposure analysis is performed on the anti-corrosion pipeline production structure data to generate the pipeline buried exposure area data; the corrosion area is divided according to the pipeline buried exposure area data to generate the pipeline prone to corrosion area data and the pipeline non-corrosion area data; hierarchical anti-corrosion treatment is performed through the pipeline prone to corrosion area data and the pipeline non-corrosion area data to generate the pipeline hierarchical anti-corrosion coating strategy; the anti-corrosion coating optimization quality assessment is performed on the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate the pipeline anti-corrosion coating optimization quality assessment data;

[0079] Step S4: Based on the preset temperature and time, the anti-corrosion pipeline is subjected to curing temperature stability evaluation according to the pipeline anti-corrosion coating optimization quality evaluation data, and curing temperature gradient stability data is generated; the anti-corrosion pipeline is subjected to coating cooling treatment according to the curing temperature gradient stability data, and coating cooling data is generated;

[0080] Step S5: Perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data; perform production optimization life prediction on the quality inspection data to generate anti-corrosion pipeline optimized production life prediction data; construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimized production life prediction data to generate an anti-corrosion pipeline production process optimized maintenance strategy.

[0081] The present invention can understand the influence of the environmental conditions of the pipeline on its anti-corrosion performance by obtaining pipeline application environment data and performing environmental impact analysis. The generated pipeline application environment impact data can provide basic data support. By performing corrosion mechanism analysis and material screening on the anti-corrosion pipeline, the candidate material most suitable for the anti-corrosion pipeline can be determined. The data generated by the material performance analysis helps to optimize the production process and material performance. Performing anti-corrosion pipeline production structure analysis, exposure analysis and corrosion area division can help understand the exposure of the pipeline in the buried area and the location of the corrosion-prone area. Through hierarchical anti-corrosion treatment and anti-corrosion coating optimization quality evaluation, anti-corrosion coating strategies for different areas can be formulated to improve the anti-corrosion performance of the pipeline. Curing temperature stability evaluation and coating cooling treatment help to ensure the quality and stability of the anti-corrosion coating. The generated curing temperature gradient stability data and coating cooling data provide a basis for the subsequent treatment of the pipeline. Through quality inspection and production optimization life prediction, pipeline quality problems can be discovered in time and their optimized production life can be predicted. Based on these data, a pipeline maintenance strategy can be constructed to extend the service life of the pipeline and optimize the production process. Therefore, the present invention improves the comprehensiveness of the optimization of the anti-corrosion pipeline production process through systematization, data support, comprehensive consideration and predictive strategy.

[0082] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for optimizing the production process of an anti-corrosion pipeline of the present invention. In this example, the method for optimizing the production process of an anti-corrosion pipeline includes the following steps:

[0083] Step S1: Acquire pipeline application environment data; perform environmental impact analysis on the pipeline application environment data to generate pipeline application environment impact data; perform pipeline anti-corrosion performance analysis on the anti-corrosion pipeline using the pipeline application environment impact data to generate pipeline anti-corrosion environmental impact data;

[0084] In the embodiment of the present invention, by collecting data such as the temperature, humidity, chemical composition, pH value, climatic conditions, etc. of the environment in which the pipeline is located, it can be obtained through on-site investigation, monitoring instruments, meteorological data, etc. The collected pipeline application environment data is analyzed to evaluate the impact of different environmental factors on the corrosion, wear, structural stability, etc. of the pipeline, and determine the importance and degree of influence of the environment on the anti-corrosion performance of the pipeline. The results of the environmental impact analysis are organized into a data form, including the specific values, degree of influence, mechanism of action, etc. of various environmental factors, to form pipeline application environment impact data. Using the pipeline application environment impact data, the anti-corrosion performance of the pipeline is analyzed, including predicting the form, degree, frequency of occurrence of corrosion suffered by the pipeline, and evaluating the effectiveness and potential risks of the current anti-corrosion measures of the pipeline. The results of the pipeline anti-corrosion performance analysis are organized into a data form, including data such as various environmental impacts, corrosion mechanisms, corrosion rates, etc. faced by the pipeline, to form pipeline anti-corrosion environmental impact data, which provides a basis for subsequent anti-corrosion process optimization.

[0085] Step S2: using the pipeline anti-corrosion environmental impact data to perform corrosion mechanism analysis on the anti-corrosion pipeline, and generate pipeline corrosion mechanism data; performing secondary anti-corrosion candidate material screening on the pipeline corrosion mechanism data through a preset material database, and thus generating second pipeline anti-corrosion candidate material data; performing material performance analysis based on the second pipeline anti-corrosion candidate material data, and generating production optimization material performance data;

[0086] In an embodiment of the present invention, various mechanisms leading to pipeline corrosion are analyzed based on pipeline anti-corrosion environmental impact data, including electrochemical corrosion, chemical corrosion, microbial corrosion, etc., and their impact and frequency in the actual environment are evaluated, and pipeline corrosion mechanism data is generated. A database containing various anti-corrosion materials is established, including coating materials, cladding materials, pipeline materials, etc., which includes information such as performance data, corrosion resistance, and use conditions of various materials. In combination with pipeline corrosion mechanism data, anti-corrosion materials suitable for preventing various corrosion mechanisms are screened out from a preset material database to form second pipeline anti-corrosion candidate material data. Material performance analysis is performed on the second pipeline anti-corrosion candidate material, including evaluation of the material's corrosion resistance, mechanical properties, wear resistance, etc., as well as the degree of matching with pipeline environmental conditions. The results of the material performance analysis are organized into data form to form production optimization material performance data, providing a basis for the subsequent optimization of the anti-corrosion pipeline production process.

[0087] Step S3: Based on the production optimization material performance data, the pipeline production structure analysis is performed on the anti-corrosion pipeline to generate the anti-corrosion pipeline production structure data; the pipeline buried area exposure analysis is performed on the anti-corrosion pipeline production structure data to generate the pipeline buried exposure area data; the corrosion area is divided according to the pipeline buried exposure area data to generate the pipeline prone to corrosion area data and the pipeline non-corrosion area data; hierarchical anti-corrosion treatment is performed through the pipeline prone to corrosion area data and the pipeline non-corrosion area data to generate the pipeline hierarchical anti-corrosion coating strategy; the anti-corrosion coating optimization quality assessment is performed on the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate the pipeline anti-corrosion coating optimization quality assessment data;

[0088] In the embodiment of the present invention, the production structure of the anti-corrosion pipeline is analyzed based on the production optimization material performance data, including various components such as pipeline materials, anti-corrosion coatings, and coating materials, and its structural stability, corrosion resistance, etc. are evaluated, and the anti-corrosion pipeline production structure data is generated. The buried area where the anti-corrosion pipeline is located is exposed to analysis, and the exposure of the pipeline in the buried environment is evaluated considering factors such as soil type, humidity, and oxygen content, and the pipeline buried exposure area data is generated. According to the pipeline buried exposure area data, the pipeline is divided into corrosion-prone areas and non-corrosion areas, the corrosion risk degree of different parts of the pipeline is determined, and the pipeline corrosion-prone area data and pipeline non-corrosion area data are generated. Combined with the pipeline corrosion-prone area and non-corrosion area data, a hierarchical anti-corrosion treatment plan is formulated, and more stringent anti-corrosion measures are taken for corrosion-prone areas, such as increasing the coating thickness, selecting materials with better corrosion resistance, etc., to form a pipeline hierarchical anti-corrosion coating strategy. Based on the pipeline hierarchical anti-corrosion coating strategy, the coating optimization quality evaluation of the anti-corrosion pipeline is carried out, considering the coating adhesion, uniformity, corrosion resistance and other indicators to ensure that the coating quality meets the requirements, and generate pipeline anti-corrosion coating optimization quality evaluation data.

[0089] Step S4: Based on the preset temperature and time, the anti-corrosion pipeline is subjected to curing temperature stability evaluation according to the pipeline anti-corrosion coating optimization quality evaluation data, and curing temperature gradient stability data is generated; the anti-corrosion pipeline is subjected to coating cooling treatment according to the curing temperature gradient stability data, and coating cooling data is generated;

[0090] In an embodiment of the present invention, the curing temperature stability of the anti-corrosion pipeline is evaluated by combining the preset curing temperature and time with the optimization quality evaluation data of the pipeline anti-corrosion coating. This includes considering factors such as the curing temperature range, curing time, and curing reaction rate of the coating material to determine the temperature change during the curing process and generate curing temperature gradient stability data. According to the curing temperature gradient stability data, the anti-corrosion pipeline is subjected to coating cooling treatment. This includes adjusting the curing temperature, strengthening ventilation and heat dissipation, controlling the curing time, and other measures to ensure that the temperature of the coating is stable during the curing process, and to prevent excessive temperature gradients from causing coating quality problems, thereby generating coating cooling data.

[0091] Step S5: Perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data; perform production optimization life prediction on the quality inspection data to generate anti-corrosion pipeline optimized production life prediction data; construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimized production life prediction data to generate an anti-corrosion pipeline production process optimized maintenance strategy.

[0092] In an embodiment of the present invention, the quality of the anti-corrosion pipeline is inspected according to the coating cooling data. This includes inspecting the thickness, uniformity, adhesion, surface flatness, etc. of the coating to verify whether the coating quality meets the requirements and generate corresponding quality inspection data. Based on the quality inspection data, the production optimization material performance data, pipeline structure data, etc. are used to predict the production optimization life of the anti-corrosion pipeline. This includes considering factors such as the use environment, corrosion conditions, and material properties of the pipeline, predicting the life of the pipeline through mathematical models or professional software, and generating the production optimization life prediction data of the anti-corrosion pipeline. Based on the production optimization life prediction data of the anti-corrosion pipeline, a pipeline maintenance strategy is formulated. This includes determining the maintenance cycle, maintenance method, repair measures, etc. according to the predicted life situation to extend the service life of the pipeline, ensure its long-term stable operation, and generate the production process optimization maintenance strategy of the anti-corrosion pipeline.

[0093] Preferably, step S1 comprises the following steps:

[0094] Step S11: using the IoT sensor network to obtain pipeline application environment data;

[0095] Step S12: performing data preprocessing on the pipeline application environment data to generate standard pipeline application environment data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization;

[0096] Step S13: Performing environmental impact analysis based on standard pipeline application environment data to generate pipeline application environment impact data;

[0097] Step S14: performing pipeline anti-corrosion performance analysis on the anti-corrosion pipeline using pipeline application environmental impact data to generate pipeline anti-corrosion environmental impact data.

[0098] In the embodiment of the present invention, IoT sensors, including temperature sensors, humidity sensors, pressure sensors, etc., are arranged around the pipeline to monitor the environmental parameters around the pipeline in real time. The sensors collect various parameter data of the environment around the pipeline regularly or in real time, such as temperature, humidity, pressure, etc., and transmit them to the data center or cloud storage through IoT technology. The collected data is cleaned to remove erroneous data such as abnormal values ​​and duplicate values ​​to ensure the accuracy and reliability of the data. The missing data can be filled by interpolation method, mean method, etc. to ensure the integrity and continuity of the data. The processed data is standardized to unify the data of different dimensions to the same scale for subsequent analysis and comparison. The pre-processed data is statistically analyzed, including mean, variance, correlation analysis, etc., to understand the characteristics and change trends of the environment around the pipeline. According to the statistical analysis results, the influence of the pipeline application environment on the pipeline is evaluated, such as the influence of temperature change on the pipeline material, the influence of humidity on the coating, etc. According to the environmental impact data, combined with the performance parameters of the anti-corrosion material, the anti-corrosion performance of the pipeline under different environmental conditions is evaluated, including corrosion resistance, durability, etc. Integrate the pipeline anti-corrosion performance assessment results with the pipeline application environmental impact data to generate pipeline anti-corrosion environmental impact data, providing a basis for the formulation of subsequent anti-corrosion measures and pipeline maintenance.

[0099] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0100] Step S21: Analyze the corrosion mechanism of the anti-corrosion pipeline using the pipeline anti-corrosion environmental impact data to generate pipeline corrosion mechanism data;

[0101] Step S22: screening the pipeline corrosion mechanism data for first pipeline anti-corrosion candidate materials through a preset material database to obtain first pipeline anti-corrosion candidate material data;

[0102] Step S23: performing material performance simulation on the first pipeline anti-corrosion candidate material data to generate material simulation performance data; performing material formula optimization on the pipeline anti-corrosion candidate material data based on the material simulation performance data to generate material optimization formula data;

[0103] Step S24: Perform sample experiment verification according to the material optimization formula data to generate material optimization sample test data; screen the first pipeline anti-corrosion candidate material data for the second pipeline anti-corrosion candidate material through the material optimization sample test data, thereby generating the second pipeline anti-corrosion candidate material data; perform material performance analysis based on the second pipeline anti-corrosion candidate material data to generate production optimization material performance data.

[0104] In the embodiment of the present invention, by integrating pipeline anti-corrosion environmental impact data, including temperature, humidity, chemical composition, etc., the corrosion mechanism of the pipeline is analyzed, such as corrosion type, rate, etc. The corrosion theory and related models are used to simulate and deduce the corrosion mechanism of the pipeline under different environmental conditions to determine the corrosion process and mechanism. The preset material database is used to screen out candidate materials that meet the requirements according to the requirements of the pipeline corrosion mechanism data, such as materials with good corrosion resistance. According to the analysis results of the corrosion mechanism data, the first batch of candidate materials are screened out from the material database as possible anti-corrosion material options. The performance of the candidate materials, such as corrosion resistance and mechanical properties, are simulated and predicted using technologies such as computational chemistry and materials science. According to the simulated performance data, the formula of the candidate materials is optimized and adjusted to improve its anti-corrosion performance and adaptability. According to the optimized formula data, material samples are prepared, and experimental verification is carried out to obtain the actual performance data of the material. According to the experimental data and performance evaluation, the material candidates suitable for anti-corrosion pipelines are further screened. The performance data of the second batch of candidate materials are analyzed to determine their feasibility and advantages and disadvantages in practical applications, providing a basis for production selection.

[0105] As an example of the present invention, refer to Figure 3 As shown, in this example, step S21 includes:

[0106] Step S211: using density functional theory to perform microscopic model simulation on pipeline anti-corrosion environmental impact data to generate a pipeline corrosion microscopic model;

[0107] Step S212: performing macro-model simulation on pipeline anti-corrosion environmental impact data using a finite element analysis tool to generate a pipeline corrosion macro-model;

[0108] Step S213: simulate the corrosion reaction path of the pipeline corrosion micromodel to generate corrosion reaction path simulation data; analyze the corrosion energy change of the pipeline corrosion micromodel through the corrosion reaction path simulation data to generate corrosion energy change data; confirm the corrosion reaction mechanism based on the corrosion energy change data and the corrosion reaction path simulation data to obtain corrosion reaction mechanism data;

[0109] Step S214: simulate the stress distribution of the pipeline corrosion macro model to obtain pipeline corrosion stress distribution data; perform corrosion extension analysis on the pipeline corrosion macro model based on the pipeline corrosion stress distribution data to generate pipeline corrosion extension data; perform corrosion mechanism verification on the pipeline corrosion extension data and the corrosion reaction mechanism data to generate pipeline corrosion mechanism data.

[0110] In the embodiment of the present invention, by collecting pipeline anti-corrosion environmental impact data, including temperature, humidity, chemical composition, etc., as well as the physical and chemical properties of the material. Using density functional theory (DFT) and other quantum chemical calculation methods, the electronic structure and atomic interaction of pipeline materials under different environmental conditions are simulated to reveal the microscopic process of corrosion mechanism. According to the DFT simulation results, a microscopic model of pipeline corrosion is constructed, including atomic interaction, bonding process of corrosion reaction, etc. The pipeline anti-corrosion environmental impact data is imported into the finite element analysis software, including the geometric structure of the pipeline, material parameters, external environment, etc. Using the finite element analysis tool, a macroscopic model of pipeline corrosion is established, considering the complexity of the pipeline structure and the influence of the external environment, so as to predict the corrosion of the pipeline under different conditions. Using quantum chemical calculation methods, the corrosion reaction path of the pipeline material is simulated, including intermediate products, transition states, etc., to reveal the key steps in the corrosion process. Based on the reaction path simulation data, the energy change in the corrosion reaction process is calculated, the energy barrier of each reaction path is analyzed, and the most likely corrosion pathway is determined. Comprehensive energy change analysis and reaction path simulation data are used to confirm the main reaction mechanism of pipeline corrosion, including electrochemical process, ion transport, etc. Finite element analysis tools are used to simulate the stress distribution of pipelines in different environments, taking into account factors such as the external environment and pipeline materials. Based on the stress distribution data and combined with the corrosion mechanism data, the expansion trend and rate of pipeline corrosion are analyzed to predict the degree of corrosion of the pipeline during its service life. The corrosion expansion data is compared and verified with the corrosion reaction mechanism data to confirm the accuracy and reliability of the model.

[0111] As an example of the present invention, refer to Figure 4 As shown, in this example, step S3 includes:

[0112] Step S31: performing pipeline production structure analysis on the anti-corrosion pipeline based on the production optimization material performance data to generate anti-corrosion pipeline production structure data;

[0113] Step S32: performing pipeline buried area exposure analysis on the anti-corrosion pipeline production structure data to generate pipeline buried exposed area data; performing corrosion area division on the anti-corrosion pipeline production structure data according to the pipeline buried exposed area data to generate pipeline prone to corrosion area data and pipeline non-corrosion area data, wherein the pipeline prone to corrosion area data includes the bottom area, the joint area, the high temperature area and the damaged area, and the pipeline non-corrosion area data includes the middle area and the upper area;

[0114] Step S33: performing hierarchical anti-corrosion treatment on the anti-corrosion pipeline according to the pipeline prone-corrosion area data and the pipeline non-corrosion area data, and generating a pipeline hierarchical anti-corrosion coating strategy, wherein the pipeline hierarchical anti-corrosion coating strategy includes a pipeline single-layer anti-corrosion coating strategy and a pipeline double-layer anti-corrosion coating strategy;

[0115] Step S34: Optimize the anti-corrosion coating of the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate pipeline anti-corrosion coating optimization data; perform coating quality evaluation on the pipeline anti-corrosion coating optimization data to generate pipeline anti-corrosion coating optimization quality evaluation data.

[0116] In the embodiment of the present invention, the performance data of the production optimization material is collected, including corrosion resistance, mechanical properties, etc. Combined with the material performance data, the production structure of the anti-corrosion pipeline is analyzed, including the characteristics of the material, thickness, manufacturing process, etc. of the pipeline. According to the layout location of the pipeline and soil conditions and other factors, the exposure of the pipeline in the buried area is analyzed, including soil type, moisture content, etc. According to the buried exposure area data, the corrosion-prone area and the non-corrosion-prone area of ​​the pipeline are divided, such as the bottom, joints, high temperature and damaged areas. According to the above division results, the characteristics and corrosion risks of each corrosion area are analyzed. Corresponding anti-corrosion coating strategies are formulated for different corrosion areas, including the determination of parameters such as coating type and thickness. According to the hierarchical anti-corrosion coating strategy, the anti-corrosion coating is optimized and designed, including the selection of coating, construction process, etc. The quality of the optimized anti-corrosion coating is evaluated, including the detection and evaluation of indicators such as the adhesion and corrosion resistance of the coating, and more specifically, a detailed performance report of the production optimization material is obtained from the supplier, including data on corrosion resistance, mechanical strength, wear resistance, etc. The production structure of the anti-corrosion pipeline is modeled and analyzed using computer-aided design software (CAD) or professional pipeline design software. Considering factors such as the material, thickness, and connection method of the pipeline, draw a three-dimensional structure diagram of the pipeline and extract key parameters for further analysis. Using tools such as the Geographic Information System (GIS) and combined with field survey data, determine factors such as the buried depth of the pipeline, soil type, and water level changes, and evaluate the corrosion effect of the soil on the pipeline. Based on the buried exposure area data, combined with the pipeline structure diagram, use professional corrosion simulation software to divide the pipeline into corrosion areas and determine the prone and non-corrosion areas. According to the results of the corrosion area division, detailed analysis is carried out for the prone and non-corrosion areas to determine the characteristics and corrosion risks of each area. According to the characteristics of the prone and non-corrosion areas, select the appropriate anti-corrosion coating type, coating thickness, and construction method, and formulate a hierarchical anti-corrosion coating strategy for the pipeline. Use coating design software or a team of professional engineers to optimize the design of the anti-corrosion coating, including selecting the best coating material, optimizing the coating thickness, and designing the construction process. Through laboratory tests or on-site inspections, quantitative evaluation of key indicators such as coating adhesion, corrosion resistance, and wear resistance is carried out, and compared with relevant standards to ensure that the coating quality meets the requirements.

[0117] Preferably, step S33 includes the following steps:

[0118] Step S331: performing double-layer anti-corrosion coating on the pipeline prone-corrosion area according to the pipeline prone-corrosion area data, and generating double-layer anti-corrosion coating data for the pipeline prone-corrosion area, wherein the double-layer anti-corrosion coating includes coating primer coating and coating intermediate layer coating;

[0119] Step S332: performing a single-layer anti-corrosion coating on the pipeline non-corrosion area according to the pipeline non-corrosion area data, and generating pipeline non-corrosion area single-layer anti-corrosion coating data;

[0120] Step S333: When it is confirmed that the data of the pipeline prone to corrosion area is the bottom area and the joint area, the coating thickness of the bottom area and the joint area is increased to generate ordinary coating thickness adjustment data; when it is confirmed that the data of the pipeline prone to corrosion area is the high temperature area, the high temperature coating thickness of the high temperature area is increased to generate high temperature coating thickness adjustment data; when it is confirmed that the data of the pipeline prone to corrosion area is the damaged area, the touch-up coating thickness of the damaged area is increased to generate touch-up coating thickness adjustment data;

[0121] Step S334: when it is confirmed that the pipeline corrosion-resistant area data is the middle area, the first coating thickness is reduced in the middle area to obtain the first coating thickness adjustment data; when it is confirmed that the pipeline corrosion-resistant area data is the upper area, the second coating thickness is reduced in the upper area to obtain the second coating thickness adjustment data, and the first coating thickness adjustment data is always greater than the second coating thickness adjustment data;

[0122] Step S335: Integrate the double-layer anti-corrosion coating data of the pipeline's prone-to-corrosion area with the ordinary coating thickness adjustment data, the high-temperature coating thickness adjustment data and the touch-up coating thickness adjustment data to generate a double-layer anti-corrosion coating strategy for the pipeline; integrate the single-layer anti-corrosion coating data of the pipeline's non-easy-to-corrode area with the first coating thickness adjustment data and the second coating thickness adjustment data to generate a single-layer anti-corrosion coating strategy for the pipeline.

[0123] In the embodiment of the present invention, suitable primer and intermediate layer coating are selected according to the characteristics of the corrosion-prone area. The primer should have good adhesion and anti-corrosion performance, and the intermediate layer coating should have good corrosion resistance and wear resistance. Determine the coating scheme, including coating method, coating thickness and coating sequence. According to the properties of the primer and intermediate layer coating, formulate a suitable coating process flow. Use professional coating equipment and technicians to apply double-layer anti-corrosion coating to the corrosion-prone area. Ensure that the coating is evenly applied to avoid missing coating and uneven coating. According to the characteristics of the non-corrosion-prone area, select a suitable single-layer anti-corrosion coating. The coating should have good corrosion resistance and wear resistance. Determine the coating scheme, including coating method, coating thickness and coating sequence. According to the properties of the coating, formulate a suitable coating process flow. Use professional coating equipment and technicians to apply single-layer anti-corrosion coating to the non-corrosion-prone area. Ensure that the coating is evenly applied to avoid missing coating and uneven coating. Confirm the bottom area and joint area based on the data of the pipeline's prone-to-corrosion area, increase the coating thickness to improve the anti-corrosion performance, and record the coating thickness adjustment data. Confirm that the pipeline's prone-to-corrosion area data is a high-temperature area, increase the thickness of the high-temperature coating to improve the temperature resistance, and record the coating thickness adjustment data. Confirm that the pipeline's prone-to-corrosion area data is a damaged area, increase the thickness of the touch-up paint coating to repair the damage, and record the coating thickness adjustment data. Integrate the double-layer anti-corrosion coating data with the coating thickness adjustment data to form a complete double-layer anti-corrosion coating strategy for pipelines. Integrate the single-layer anti-corrosion coating data with the first coating thickness adjustment data and the second coating thickness adjustment data to form a complete single-layer anti-corrosion coating strategy for pipelines.

[0124] Preferably, step S4 comprises the following steps:

[0125] Step S41: curing the anti-corrosion pipeline based on the preset temperature and time according to the pipeline anti-corrosion coating optimization quality assessment data, and using the temperature sensor to collect real-time temperature, thereby obtaining the pipeline coating curing temperature data;

[0126] Step S42: performing coating curing temperature distribution analysis on the pipeline coating curing temperature data to generate a coating curing temperature distribution diagram; performing temperature gradient analysis on the coating curing temperature distribution diagram to generate coating curing temperature gradient data;

[0127] Step S43: performing stability evaluation on the coating curing temperature gradient data to generate curing temperature gradient stability data; performing curing completeness detection on the anti-corrosion pipeline according to the curing temperature gradient stability data to generate curing completeness data of the anti-corrosion pipeline;

[0128] Step S44: performing coating cooling treatment on the anti-corrosion pipeline according to the anti-corrosion pipeline curing completeness data, thereby generating coating cooling data.

[0129] In the embodiment of the present invention, the curing temperature and curing time are set according to the requirements of the anti-corrosion coating material. These parameters are usually based on the technical specifications and production experience of the coating material. The coated pipe is sent to a curing chamber or curing equipment and cured at a preset temperature. The surface temperature of the pipe is monitored in real time by a temperature sensor to ensure that the curing temperature meets the requirements. The surface temperature data of the pipe is collected in real time by the temperature sensor and recorded. The collected coating curing temperature data is sorted and processed to obtain the curing temperature information of each area. Using the processed data, a temperature distribution diagram of the pipe surface is drawn to clearly show the temperature conditions of different areas during the coating curing process. Through the temperature distribution diagram, the temperature gradient between different areas is analyzed to understand the temperature change law during the coating curing process. Based on the curing temperature gradient data, the temperature stability of the pipe coating curing process is evaluated. By comparing the temperature changes in different areas, it is determined whether the curing temperature is stable. According to the evaluation results, the curing of the pipe coating is tested. Ensure that the coating reaches the expected hardness and stability during the curing process. The fully cured pipe is sent to a cooling chamber or a cooling device is used for cooling. This step is intended to stabilize the coating structure and ensure the coating quality. The temperature changes during the cooling process are recorded to obtain the coating cooling data. These data can be used for subsequent quality control and analysis.

[0130] Preferably, step S5 comprises the following steps:

[0131] Step S51: using a machine vision method to perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data;

[0132] Step S52: dividing the quality inspection data into data sets to generate a model training set and a model test set;

[0133] Step S53: using the random forest algorithm to perform model training on the model training set to generate an anti-corrosion pipeline life training model; using the model test set to perform model test iteration on the anti-corrosion pipeline life training model to generate an anti-corrosion pipeline life prediction model;

[0134] Step S54: Import the quality inspection data into the anti-corrosion pipeline life prediction model to perform production optimization life prediction, thereby generating anti-corrosion pipeline optimized production life prediction data; construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimized production life prediction data to generate an anti-corrosion pipeline production process optimization maintenance strategy.

[0135] In an embodiment of the present invention, the surface of the anti-corrosion pipeline is inspected by using machine vision technology, and the quality problems of the coating surface, such as bubbles, cracks, and uneven coating thickness, are identified by analyzing the features in the coating cooling data. The inspection results are converted into quality inspection data, including information such as the type, location, and quantity of the coating quality problems. The quality inspection data is divided into a model training set and a model test set according to a certain ratio, usually by cross-validation or random sampling. Ensure that the data distribution of the training set and the test set is uniform and can cover various quality problems and scenarios. Select a suitable machine learning algorithm, such as random forest, to train the anti-corrosion pipeline life prediction model. The model parameters are obtained by using the model training set for algorithm training. The trained model is tested using the model test set to evaluate the performance of the model. The model is iteratively tuned according to the test results until a satisfactory prediction effect is achieved. The quality inspection data is input into the trained anti-corrosion pipeline life prediction model for production optimization life prediction. The model will predict the service life of the pipeline based on the coating quality and other factors. Based on the predicted production life data of anti-corrosion pipelines, a maintenance strategy for the pipeline is formulated, including regular inspections, coating repairs, pipeline replacement and other measures to extend the service life of the pipeline and ensure its safe operation.

[0136] Preferably, step S51 includes the following steps:

[0137] Step S511: using the camera on the production line to collect images of the anti-corrosion pipeline to obtain an image of the anti-corrosion pipeline coating;

[0138] Step S512: segmenting the anti-corrosion pipeline coating image by the image edge region to generate the anti-corrosion pipeline coating core image;

[0139] Step S513: performing defect area recognition on the core image of the anti-corrosion pipeline coating to generate defect area data of the anti-corrosion pipeline coating;

[0140] Step S514: Associating the coating cooling data with the quality defects through the anti-corrosion pipeline coating defect area data to generate quality inspection data.

[0141] In the embodiment of the present invention, a high-resolution camera is set on the anti-corrosion pipeline production line to ensure that the fine details of the pipeline surface can be captured. The camera is used to continuously capture images of the anti-corrosion pipeline to ensure that the entire pipeline surface is covered, and the timestamp and location information during the acquisition process are recorded. The captured image is preprocessed, including denoising, brightness adjustment, color correction, etc., to enhance the image quality. The edge detection algorithm (such as Canny edge detection) is used to detect the edge of the image to find the edge of the anti-corrosion coating. According to the edge information, the anti-corrosion pipeline coating image is segmented, and the image is segmented into a coating area and a background area. Features such as texture features and shape features are extracted from the coating core image. The extracted features are trained using a machine learning algorithm (such as a support vector machine and a convolutional neural network) to identify coating defects. According to the defects identified by the algorithm, the coating image is marked or a binary image is generated for subsequent defect area calculation. Using the marked defect area, the area of ​​each defect is calculated to obtain the anti-corrosion pipeline coating defect area data. The coating defect area data is associated with the coating cooling data to determine the degree of influence of the coating defect on the coating cooling quality. Based on the results of the correlation analysis, quality inspection data is generated, including information such as defect type, location, size, etc., for subsequent quality assessment and processing.

[0142] In this specification, a production process optimization system for anti-corrosion pipelines is provided, which is used to execute the above-mentioned production process optimization method for anti-corrosion pipelines. The production process optimization system for anti-corrosion pipelines includes:

[0143] The pipeline anti-corrosion impact analysis module is used to obtain pipeline application environment data; perform environmental impact analysis on pipeline application environment data to generate pipeline application environment impact data; perform pipeline anti-corrosion performance analysis on anti-corrosion pipelines based on pipeline application environment impact data to generate pipeline anti-corrosion environmental impact data;

[0144] The pipeline material optimization module is used to analyze the corrosion mechanism of the anti-corrosion pipeline using the pipeline anti-corrosion environmental impact data to generate pipeline corrosion mechanism data; perform secondary anti-corrosion candidate material screening on the pipeline corrosion mechanism data through a preset material database to generate second pipeline anti-corrosion candidate material data; perform material performance analysis based on the second pipeline anti-corrosion candidate material data to generate production optimization material performance data;

[0145] The pipeline anti-corrosion coating optimization module is used to analyze the pipeline production structure of the anti-corrosion pipeline based on the production optimization material performance data to generate the anti-corrosion pipeline production structure data; to analyze the pipeline buried area exposure of the anti-corrosion pipeline production structure data to generate the pipeline buried exposure area data; to divide the corrosion area according to the pipeline buried exposure area data to generate the pipeline prone to corrosion area data and pipeline non-corrosion area data; to perform hierarchical anti-corrosion treatment through the pipeline prone to corrosion area data and pipeline non-corrosion area data to generate the pipeline hierarchical anti-corrosion coating strategy; to perform anti-corrosion coating optimization quality assessment on the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate the pipeline anti-corrosion coating optimization quality assessment data;

[0146] The pipeline curing analysis module is used to evaluate the curing temperature stability of the anti-corrosion pipeline based on the preset temperature and time according to the pipeline anti-corrosion coating optimization quality evaluation data, and generate curing temperature gradient stability data; the anti-corrosion pipeline is subjected to coating cooling treatment based on the curing temperature gradient stability data, thereby generating coating cooling data;

[0147] The pipeline optimization life prediction module is used to perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data; to perform production optimization life prediction on the quality inspection data to generate anti-corrosion pipeline optimization production life prediction data; to construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimization production life prediction data to generate an anti-corrosion pipeline production process optimization maintenance strategy.

[0148] The beneficial effect of the present invention is that through environmental impact analysis and corrosion mechanism analysis, the influence of the environment in which the pipeline is located on its corrosion can be better understood, so as to select suitable anti-corrosion materials and coatings, and improve the adaptability of the pipeline in various environments. By using production optimization material performance data and pipeline production structure analysis, corresponding anti-corrosion coating strategies can be designed for different pipeline parts, thereby optimizing the anti-corrosion performance and improving the corrosion resistance of the pipeline. By optimizing the production process of anti-corrosion pipelines and predicting their lifespan, production efficiency can be effectively improved, resource waste and production costs can be reduced, and efficient operation of the pipeline production process can be ensured. Through quality inspection data and optimized production life prediction data, an effective pipeline maintenance strategy can be established to promptly discover and repair problems in the pipeline, ensuring the long-term stable operation and safety of the pipeline. Therefore, the present invention improves the comprehensiveness of the optimization of the production process of anti-corrosion pipelines through systematization, data support, comprehensive consideration and predictive strategies.

[0149] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0150] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for optimizing the production process of anti-corrosion pipelines, characterized in that: The following steps are involved: Step S1: Acquire pipeline application environment data; perform environmental impact analysis on the pipeline application environment data to generate pipeline application environment impact data; perform pipeline anti-corrosion performance analysis on the anti-corrosion pipeline using the pipeline application environment impact data to generate pipeline anti-corrosion environmental impact data; Step S2: using the pipeline anti-corrosion environmental impact data to analyze the corrosion mechanism of the anti-corrosion pipeline, and generating pipeline corrosion mechanism data; performing secondary anti-corrosion candidate material screening on the pipeline corrosion mechanism data through a preset material database, and thereby generating second pipeline anti-corrosion candidate material data; Perform material performance analysis based on the second pipeline anti-corrosion candidate material data to generate production optimization material performance data; step S2 includes the following steps: Step S21: Analyze the corrosion mechanism of the anti-corrosion pipeline using the pipeline anti-corrosion environmental impact data to generate pipeline corrosion mechanism data; Step S21 includes the following steps: Step S211: using density functional theory to perform microscopic model simulation on pipeline anti-corrosion environmental impact data to generate a pipeline corrosion microscopic model; Step S212: performing macro-model simulation on pipeline anti-corrosion environmental impact data using a finite element analysis tool to generate a pipeline corrosion macro-model; Step S213: simulate the corrosion reaction path of the pipeline corrosion micromodel to generate corrosion reaction path simulation data; analyze the corrosion energy change of the pipeline corrosion micromodel through the corrosion reaction path simulation data to generate corrosion energy change data; confirm the corrosion reaction mechanism based on the corrosion energy change data and the corrosion reaction path simulation data to obtain corrosion reaction mechanism data; Step S214: simulate the stress distribution of the pipeline corrosion macro model to obtain pipeline corrosion stress distribution data; perform corrosion extension analysis on the pipeline corrosion macro model based on the pipeline corrosion stress distribution data to generate pipeline corrosion extension data; perform corrosion mechanism verification on the pipeline corrosion extension data and the corrosion reaction mechanism data to generate pipeline corrosion mechanism data; Step S22: screening the pipeline corrosion mechanism data for first pipeline anti-corrosion candidate materials through a preset material database to obtain first pipeline anti-corrosion candidate material data; Step S23: performing material performance simulation on the first pipeline anti-corrosion candidate material data to generate material simulation performance data; performing material formula optimization on the pipeline anti-corrosion candidate material data based on the material simulation performance data to generate material optimization formula data; Step S24: Perform sample experiment verification according to the material optimization formula data to generate material optimization sample test data; screen the first pipeline anti-corrosion candidate material data for the second pipeline anti-corrosion candidate material through the material optimization sample test data, thereby generating the second pipeline anti-corrosion candidate material data; perform material performance analysis based on the second pipeline anti-corrosion candidate material data to generate production optimization material performance data; Step S3: Based on the production optimization material performance data, the pipeline production structure analysis is performed on the anti-corrosion pipeline to generate the anti-corrosion pipeline production structure data; the pipeline buried area exposure analysis is performed on the anti-corrosion pipeline production structure data to generate the pipeline buried exposure area data; the corrosion area is divided according to the pipeline buried exposure area data to generate the pipeline prone to corrosion area data and the pipeline non-corrosion area data; hierarchical anti-corrosion treatment is performed through the pipeline prone to corrosion area data and the pipeline non-corrosion area data to generate the pipeline hierarchical anti-corrosion coating strategy; the anti-corrosion coating optimization quality assessment is performed on the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate the pipeline anti-corrosion coating optimization quality assessment data; Step S4: Based on the preset temperature and time, the anti-corrosion pipeline is subjected to curing temperature stability evaluation according to the pipeline anti-corrosion coating optimization quality evaluation data, and curing temperature gradient stability data is generated; the anti-corrosion pipeline is subjected to coating cooling treatment according to the curing temperature gradient stability data, and coating cooling data is generated; Step S5: Perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data; perform production optimization life prediction on the quality inspection data to generate anti-corrosion pipeline optimized production life prediction data; construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimized production life prediction data to generate an anti-corrosion pipeline production process optimization maintenance strategy; Step S5 includes the following steps: Step S51: using a machine vision method to perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data; Step S51 includes the following steps: Step S511: using the camera on the production line to collect images of the anti-corrosion pipeline to obtain an image of the anti-corrosion pipeline coating; Step S512: segmenting the anti-corrosion pipeline coating image by the image edge region to generate the anti-corrosion pipeline coating core image; Step S513: performing defect area recognition on the core image of the anti-corrosion pipeline coating to generate defect area data of the anti-corrosion pipeline coating; Step S514: Associating the coating cooling data with the quality defects through the anti-corrosion pipeline coating defect area data to generate quality inspection data; Step S52: dividing the quality inspection data into data sets to generate a model training set and a model test set; Step S53: using the random forest algorithm to perform model training on the model training set to generate an anti-corrosion pipeline life training model; using the model test set to perform model test iteration on the anti-corrosion pipeline life training model to generate an anti-corrosion pipeline life prediction model; Step S54: Import the quality inspection data into the anti-corrosion pipeline life prediction model to perform production optimization life prediction, thereby generating anti-corrosion pipeline optimized production life prediction data; construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimized production life prediction data to generate an anti-corrosion pipeline production process optimization maintenance strategy.

2. The method for optimizing the production process of anti-corrosion pipelines according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using the IoT sensor network to obtain pipeline application environment data; Step S12: performing data preprocessing on the pipeline application environment data to generate standard pipeline application environment data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S13: Performing environmental impact analysis based on standard pipeline application environment data to generate pipeline application environment impact data; Step S14: performing pipeline anti-corrosion performance analysis on the anti-corrosion pipeline using pipeline application environmental impact data to generate pipeline anti-corrosion environmental impact data.

3. The method for optimizing the production process of anti-corrosion pipelines according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing pipeline production structure analysis on the anti-corrosion pipeline based on the production optimization material performance data to generate anti-corrosion pipeline production structure data; Step S32: performing pipeline buried area exposure analysis on the anti-corrosion pipeline production structure data to generate pipeline buried exposed area data; performing corrosion area division on the anti-corrosion pipeline production structure data according to the pipeline buried exposed area data to generate pipeline prone to corrosion area data and pipeline non-corrosion area data, wherein the pipeline prone to corrosion area data includes the bottom area, the joint area, the high temperature area and the damaged area, and the pipeline non-corrosion area data includes the middle area and the upper area; Step S33: performing hierarchical anti-corrosion treatment on the anti-corrosion pipeline according to the pipeline prone-corrosion area data and the pipeline non-corrosion area data, and generating a pipeline hierarchical anti-corrosion coating strategy, wherein the pipeline hierarchical anti-corrosion coating strategy includes a pipeline single-layer anti-corrosion coating strategy and a pipeline double-layer anti-corrosion coating strategy; Step S34: Optimize the anti-corrosion coating of the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate pipeline anti-corrosion coating optimization data; perform coating quality evaluation on the pipeline anti-corrosion coating optimization data to generate pipeline anti-corrosion coating optimization quality evaluation data.

4. The method for optimizing the production process of anti-corrosion pipelines according to claim 3, characterized in that: Step S33 includes the following steps: Step S331: performing double-layer anti-corrosion coating on the pipeline prone-corrosion area according to the pipeline prone-corrosion area data, and generating double-layer anti-corrosion coating data for the pipeline prone-corrosion area, wherein the double-layer anti-corrosion coating includes coating primer coating and coating intermediate layer coating; Step S332: performing a single-layer anti-corrosion coating on the pipeline non-corrosion area according to the pipeline non-corrosion area data, and generating pipeline non-corrosion area single-layer anti-corrosion coating data; Step S333: When it is confirmed that the data of the pipeline prone to corrosion area is the bottom area and the joint area, the coating thickness of the bottom area and the joint area is increased to generate ordinary coating thickness adjustment data; when it is confirmed that the data of the pipeline prone to corrosion area is the high temperature area, the high temperature coating thickness of the high temperature area is increased to generate high temperature coating thickness adjustment data; when it is confirmed that the data of the pipeline prone to corrosion area is the damaged area, the touch-up coating thickness of the damaged area is increased to generate touch-up coating thickness adjustment data; Step S334: when it is confirmed that the pipeline corrosion-resistant area data is the middle area, the first coating thickness is reduced in the middle area to obtain the first coating thickness adjustment data; when it is confirmed that the pipeline corrosion-resistant area data is the upper area, the second coating thickness is reduced in the upper area to obtain the second coating thickness adjustment data, and the first coating thickness adjustment data is always greater than the second coating thickness adjustment data; Step S335: Integrate the double-layer anti-corrosion coating data of the pipeline's prone-to-corrosion area with the ordinary coating thickness adjustment data, the high-temperature coating thickness adjustment data and the touch-up coating thickness adjustment data to generate a double-layer anti-corrosion coating strategy for the pipeline; integrate the single-layer anti-corrosion coating data of the pipeline's non-easy-to-corrode area with the first coating thickness adjustment data and the second coating thickness adjustment data to generate a single-layer anti-corrosion coating strategy for the pipeline.

5. The method for optimizing the production process of anti-corrosion pipelines according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: curing the anti-corrosion pipeline based on the preset temperature and time according to the pipeline anti-corrosion coating optimization quality assessment data, and using the temperature sensor to collect real-time temperature, thereby obtaining the pipeline coating curing temperature data; Step S42: performing coating curing temperature distribution analysis on the pipeline coating curing temperature data to generate a coating curing temperature distribution diagram; performing temperature gradient analysis on the coating curing temperature distribution diagram to generate coating curing temperature gradient data; Step S43: performing stability evaluation on the coating curing temperature gradient data to generate curing temperature gradient stability data; performing curing completeness detection on the anti-corrosion pipeline according to the curing temperature gradient stability data to generate curing completeness data of the anti-corrosion pipeline; Step S44: performing coating cooling treatment on the anti-corrosion pipeline according to the anti-corrosion pipeline curing completeness data, thereby generating coating cooling data.

6. A system for executing the production process optimization method of anti-corrosion pipeline according to claim 1, characterized in that: Used to execute the production process optimization method of the anti-corrosion pipeline according to claim 1, the production process optimization system of the anti-corrosion pipeline comprises: The pipeline anti-corrosion impact analysis module is used to obtain pipeline application environment data; perform environmental impact analysis on pipeline application environment data to generate pipeline application environment impact data; perform pipeline anti-corrosion performance analysis on anti-corrosion pipelines based on pipeline application environment impact data to generate pipeline anti-corrosion environmental impact data; The pipeline material optimization module is used to analyze the corrosion mechanism of the anti-corrosion pipeline using the pipeline anti-corrosion environmental impact data to generate pipeline corrosion mechanism data; perform secondary anti-corrosion candidate material screening on the pipeline corrosion mechanism data through a preset material database to generate second pipeline anti-corrosion candidate material data; perform material performance analysis based on the second pipeline anti-corrosion candidate material data to generate production optimization material performance data; The pipeline anti-corrosion coating optimization module is used to analyze the pipeline production structure of the anti-corrosion pipeline based on the production optimization material performance data to generate the anti-corrosion pipeline production structure data; perform pipeline buried area exposure analysis on the anti-corrosion pipeline production structure data to generate pipeline buried exposed area data; divide the corrosion area according to the pipeline buried exposed area data to generate pipeline prone to corrosion area data and pipeline non-corrosion area data; perform hierarchical anti-corrosion treatment through pipeline prone to corrosion area data and pipeline non-corrosion area data to generate pipeline hierarchical anti-corrosion coating strategy; perform anti-corrosion coating optimization quality assessment on the anti-corrosion pipeline through the pipeline hierarchical anti-corrosion coating strategy to generate pipeline anti-corrosion coating optimization quality assessment data; The pipeline curing analysis module is used to evaluate the curing temperature stability of the anti-corrosion pipeline based on the preset temperature and time according to the pipeline anti-corrosion coating optimization quality evaluation data, and generate curing temperature gradient stability data; the anti-corrosion pipeline is subjected to coating cooling treatment based on the curing temperature gradient stability data, thereby generating coating cooling data; The pipeline optimization life prediction module is used to perform quality inspection on the anti-corrosion pipeline according to the coating cooling data to generate quality inspection data; to perform production optimization life prediction on the quality inspection data to generate anti-corrosion pipeline optimization production life prediction data; to construct a pipeline maintenance strategy based on the anti-corrosion pipeline optimization production life prediction data to generate an anti-corrosion pipeline production process optimization maintenance strategy.

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