Structural safety assessment method and system for offshore booster station

Abstract

The invention provides an offshore booster station structure safety monitoring and evaluating method and system, and the method comprises the steps: monitoring the structure corrosion, inclination, settlement, stress strain, vibration and other parameters of an offshore booster station in real time, training the preprocessed data through a machine learning algorithm, and building a structure safety monitoring and evaluating model; and the structural safety of the offshore booster station is evaluated. And according to an evaluation result, performing fault prediction, and making a maintenance strategy to ensure safe operation of the offshore booster station. The monitoring and evaluating method has real-time performance, accuracy and reliability, the safety management level of the offshore booster station is improved, the risk of safety accidents is reduced, and the method is suitable for complexity and severity of the marine environment.

Description

Technical Field

[0001] The present invention belongs to the field of offshore wind power booster station structure safety, and in particular to an offshore wind power booster station structure safety assessment method and system. Background Art

[0002] With the continuous growth of global energy demand and the active development of renewable energy, offshore wind power, as an important form of renewable energy, has developed rapidly. As the core component of offshore wind farms, offshore booster stations undertake the important tasks of power collection, boosting and transmission. Their structural safety is of great significance to the stable operation of the entire offshore wind power system.

[0003] However, the offshore environment is complex and harsh, and the offshore booster station structure faces many risk factors such as corrosion, tilting, settlement, stress strain, vibration, etc., which may lead to structural damage, failure or even accidents. Therefore, structural safety assessment of offshore booster stations has become a key link to ensure their safe and stable operation.

[0004] The structural safety assessment aims to identify the safety hazards and potential risks of the structure through comprehensive analysis and evaluation of the structural integrity, strength, stability, durability and other aspects of the offshore booster station, and provide a scientific basis for formulating corresponding repair, reinforcement and maintenance measures. Summary of the invention

[0005] The present invention provides a method and system for monitoring and evaluating the structural safety of an offshore substation, which monitors and evaluates the structural safety of an offshore substation in real time so as to timely discover potential safety hazards and provide a basis for fault prediction and formulation of maintenance strategies.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A method for safety assessment of offshore booster station structure, comprising: Real-time monitoring of offshore booster station structure corrosion, tilt, settlement and stress-strain parameters to obtain monitoring data of offshore booster station; Preprocess the monitoring data of offshore booster stations, including filtering, denoising and data calibration; Train the preprocessed data to establish a structural safety monitoring and assessment model to assess the structural safety of offshore booster stations; Based on the evaluation results, fault prediction is carried out and maintenance strategies are formulated to ensure the safe operation of the offshore booster station.

[0007] A further improvement of the present invention is that the real-time monitoring of the corrosion, tilt, settlement and stress-strain parameters of the offshore booster station structure to obtain monitoring data of the offshore booster station includes: Use sensors to monitor the corrosion, tilt, settlement and stress-strain parameters of offshore booster station structures in real time; The sensors include ultrasonic corrosion sensors, gyroscope sensors, displacement sensors, strain gauges and vibration sensors; The sensors are installed at various key positions of the steel structure of the booster station to extract parameter information related to the booster station structure; The monitoring data collected by the sensor is transmitted to the intelligent data collector.

[0008] A further improvement of the present invention is that the preprocessing of the monitoring data acquired from the offshore booster station includes filtering, denoising and data calibration, including: The intelligent data collector receives monitoring data from various sensors on the offshore booster station structure, including corrosion degree, tilt status, settlement, stress-strain data and vibration parameters; The received data is aggregated and integrated in the intelligent data collector to form a complete data set with multiple parameter dimensions; then, the data set enters the deep processing stage, including data filtering, calibration and denoising operations; The deeply processed data set is sent to the data center by the intelligent data collector through the data transmission protocol; the data center, as the core of data processing and analysis, has computing power and storage resources; in the data center, the data set is used to train the structural safety monitoring and assessment model.

[0009] A further improvement of the present invention is that the sensor transmits information to the data collector in real time via wired or wireless means.

[0010] A further improvement of the present invention is that data filtering removes noise and interference signals and improves the signal-to-noise ratio of data by selecting a filtering algorithm, including limiting filtering, median filtering or arithmetic mean filtering; the calibration process adjusts the filtered data to eliminate sensor errors and deviations to ensure the accuracy of the data; finally, the noise level in the data is reduced through a denoising operation.

[0011] A further improvement of the present invention is that the pre-processed data is trained to establish a structural safety monitoring and evaluation model to evaluate the structural safety of the offshore booster station, including: Collect and organize the data of structural safety monitoring, perform data cleaning and feature engineering, and then divide the data set into training set, validation set and test set; Select support vector machine as the machine learning algorithm, adjust the parameters of SVM according to the characteristics of the data, and use cross-validation method to optimize the performance of the model; The SVM model is trained and evaluated. The training set is used to train the model, and the validation set is used to evaluate the performance of the model to ensure that the model has good generalization ability. The test set is used to evaluate the final trained SVM model to verify the model's predictive ability.

[0012] A further improvement of the present invention is that, according to the evaluation results, fault prediction and maintenance strategy are carried out to ensure the safe operation of the offshore booster station, including: Use machine learning models to predict possible fault types and take maintenance measures in advance; For the diagnosed faults, the faulty parts are repaired or replaced to ensure that the booster station resumes normal operation as soon as possible.

[0013] A structural safety assessment system for an offshore booster station, comprising: The data acquisition unit monitors the corrosion, tilt, settlement and stress-strain parameters of the offshore booster station structure in real time to obtain monitoring data of the offshore booster station; The data processing unit pre-processes the monitoring data acquired from the offshore booster station, including filtering, denoising and data calibration; The evaluation unit trains the preprocessed data, establishes a structural safety monitoring and evaluation model, and evaluates the structural safety of the offshore booster station; The execution unit predicts faults and formulates maintenance strategies based on the evaluation results to ensure the safe operation of the offshore booster station.

[0014] A further improvement of the present invention is that in the data acquisition unit, the corrosion, tilt, settlement and stress-strain parameters of the offshore booster station structure are monitored in real time to obtain monitoring data of the offshore booster station, including: Use sensors to monitor the corrosion, tilt, settlement and stress-strain parameters of offshore booster station structures in real time; The sensors include ultrasonic corrosion sensors, gyroscope sensors, displacement sensors, strain gauges and vibration sensors; The sensors are installed at various key positions of the steel structure of the booster station to extract parameter information related to the booster station structure; The monitoring data collected by the sensor is transmitted to the intelligent data collector.

[0015] A further improvement of the present invention is that, in the data processing unit, the monitoring data of the offshore booster station is preprocessed, including filtering, denoising and data calibration, including: The intelligent data collector receives monitoring data from various sensors on the offshore booster station structure, including corrosion degree, tilt status, settlement, stress-strain data and vibration parameters; The received data is aggregated and integrated in the intelligent data collector to form a complete data set with multiple parameter dimensions; then, the data set enters the deep processing stage, including data filtering, calibration and denoising operations; The deeply processed data set is sent to the data center by the intelligent data collector through the data transmission protocol; the data center, as the core of data processing and analysis, has computing power and storage resources; in the data center, the data set is used to train the structural safety monitoring and assessment model.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: Real-time monitoring and data preprocessing: Solve the problems of data lag and low quality in traditional monitoring methods. By integrating advanced sensors, the key structural parameters of offshore booster stations are captured in real time to ensure the immediacy and accuracy of data. At the same time, data is preprocessed to improve data quality and provide solid support for subsequent analysis.

[0017] Machine learning algorithm and assessment model: This solves the problem of insufficient adaptability and limited prediction accuracy of traditional safety assessment methods. By using machine learning algorithm training data, an assessment model is established that automatically identifies and learns the structural response characteristics of the substation, improving the intelligence level of the assessment and the prediction accuracy.

[0018] Fault prediction and maintenance strategy: Based on the output of the evaluation model, the problem of untimely discovery of potential safety hazards and lack of scientific basis for maintenance is solved. It can achieve early prediction of potential faults in offshore substations and provide strong support for the formulation of maintenance strategies.

[0019] In summary, the present invention successfully solves multiple key dimensions in the safety monitoring and assessment of offshore substation structures by adopting real-time monitoring and data preprocessing, real-time monitoring and data preprocessing, fault prediction and formulation of maintenance strategies, and improves the real-time, accuracy and reliability of the assessment, as well as the safety management level of offshore wind power facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0021] Figure 1 This is a flow chart of a method for monitoring and evaluating the structural safety of an offshore booster station according to the present invention; Figure 2 Processing analysis graphs for the machine learning support vector machine (SVM) algorithm; Figure 3 Radar model diagram for offshore booster station structural safety monitoring and assessment.

[0022] Figure 4 The present invention is a structural block diagram of an offshore booster station structural safety assessment system. DETAILED DESCRIPTION

[0023] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0024] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0025] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0026] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0027] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0028] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] Example 1 The present invention provides a method for safety assessment of offshore booster station structure, comprising: Real-time monitoring of offshore booster station structure corrosion, tilt, settlement and stress-strain parameters to obtain monitoring data of offshore booster station; Preprocess the monitoring data of offshore booster stations, including filtering, denoising and data calibration; Train the preprocessed data to establish a structural safety monitoring and assessment model to assess the structural safety of offshore booster stations; Based on the evaluation results, fault prediction is carried out and maintenance strategies are formulated to ensure the safe operation of the offshore booster station.

[0030] Example 2 like Figures 1 to 3 As shown, a method for monitoring and evaluating the structural safety of an offshore substation in this embodiment is specifically described as follows.

[0031] In this embodiment, in step S1, the specific implementation steps are as follows: According to the monitoring requirements, ultrasonic corrosion sensors, gyroscope sensors, displacement sensors, strain gauges and vibration sensors were selected. These sensors were installed in key parts of the structure such as towers and foundations to capture the actual conditions of corrosion, tilt, settlement, stress, strain and vibration of the structure in real time. During the installation process, the manufacturer's installation guidelines were strictly followed to ensure the close coupling of the sensor with the monitoring surface, and preliminary debugging was carried out to verify the normality of its output signal.

[0032] In this embodiment, in step S2, the specific implementation steps are as follows: Configuration and debugging phase of the data acquisition unit. The data acquisition unit is installed in a location that is easy to maintain and less affected by the environment, and is firmly connected through fixings. Check the power supply and indicator status of the data acquisition unit to ensure that it is in normal working mode. Then, through the supporting software or driver, set the sampling frequency, data format and other parameters of the data acquisition unit, and conduct real-time monitoring tests to verify that it can accurately receive and display sensor data.

[0033] In order to ensure the real-time transmission of monitoring data, network facilities such as optical fiber and wireless communication modules were configured to establish a data transmission path between the data acquisition unit and the data processing unit. A network stability test was conducted to simulate the data transmission conditions under different network environments to ensure that the monitoring data can be stably and reliably transmitted to the data processing unit.

[0034] In the data processing stage, the received monitoring data is preprocessed. First, digital filtering technology is used to remove high-frequency noise and interference components in the signal; second, the denoising algorithm is used to further eliminate random fluctuations and outliers in the data; finally, the measurement data is calibrated according to the sensor calibration curve or standard reference data to improve the accuracy of the data.

[0035] The preprocessed data is stored in the selected storage medium, and a reasonable data storage format and structure are designed to facilitate subsequent data analysis and use. At the same time, the stored data is backed up regularly to ensure the security and reliability of the data. In terms of data management, a convenient data query interface or tool is provided to support data retrieval by time range, monitoring parameters and other conditions, and a data update and deletion mechanism is established to maintain the timeliness of the data and the rational use of storage space for subsequent data analysis and use. The stored data is backed up regularly to ensure the security and reliability of the data. The stored data is managed, including operations such as data query, update, and deletion.

[0036] The deeply processed data set is sent to the data center by the intelligent data collector through an efficient data transmission protocol; the data center, as the core of data processing and analysis, has computing power and storage resources. In the data center, the data set is used to train the structural safety monitoring and assessment model. This process involves multiple links such as feature extraction, model selection, and parameter optimization, aiming to build a predictive model that can accurately reflect the safety status of the offshore booster station structure. The trained model can monitor and evaluate the safety status of the structure in real time, providing a scientific basis for subsequent maintenance, repairs, and decision-making. At the same time, the data center is also responsible for the long-term storage and backup of data to ensure data security and reliability.

[0037] In this embodiment, in step S3, the specific implementation steps are as follows: 1. Data collection and preprocessing: Collect monitoring data of offshore booster stations in five dimensions, including corrosion, vibration, tilt, stress-strain, and settlement. Perform data cleaning, including processing missing values, outliers, and duplicate values, perform feature engineering, and extract features that are helpful for structural safety assessment, such as corrosion rate, vibration frequency, tilt angle, stress level, settlement rate, etc.

[0038] 2. Data division: It is a crucial step to properly divide the preprocessed dataset into training set, validation set, and test set. This division helps evaluate the performance of the model, avoid overfitting, and ensure the generalization ability of the model on different data. Usually, the ratio of 70% training set, 15% validation set, and 15% test set is a compromise based on experience and practice, but this ratio is not absolute and can be adjusted according to the complexity of the specific problem and the size of the data.

[0039] 3. SVM model establishment Support vector machine (SVM) is a powerful classification and regression tool. Its core idea is to find an optimal hyperplane in the feature space to maximize the interval between sample points of different categories. In the classification task, SVM finds this optimal hyperplane by solving a convex quadratic programming problem, thereby achieving accurate classification of samples. The decision boundary of SVM is determined by only a few support vectors, which makes it have high generalization ability and robustness.

[0040] Linear SVM vs Nonlinear SVM Linear SVM: Applicable to the case where the feature space is linearly separable. It finds the optimal hyperplane by solving linear equations to achieve linear classification of samples.

[0041] Nonlinear SVM: When the feature space is linearly inseparable, a kernel function needs to be introduced to map the samples into a higher-dimensional space to make them linearly separable. Commonly used kernel functions include polynomial kernel, radial basis function (RBF) kernel, and sigmoid kernel. Among them, the RBF kernel is highly favored due to its good performance and wide applicability.

[0042] 4. Parameter setting and optimization Kernel function type: Choose an appropriate kernel function based on the characteristics of the data and the requirements of the problem. For example, for nonlinearly separable data sets, the RBF kernel is usually a good choice.

[0043] Penalty parameter C: controls the trade-off between model complexity and misclassified samples. The larger the C value, the more the model tends to reduce the number of misclassified samples, but it may lead to overfitting; the smaller the C value, the lower the model complexity, but it may increase the number of misclassified samples.

[0044] Kernel function parameters (such as γ of RBF kernel): affect the distribution of samples in high-dimensional space and the shape of decision boundaries. The larger the γ value, the greater the influence of a single training sample, which may lead to overfitting of the model; the smaller the γ value, the smoother the model, but may reduce the classification accuracy of the model.

[0045] 5. Cross-validation: In order to find the best parameter configuration, cross-validation methods such as k-fold cross-validation are usually used. This method divides the data set into k subsets of equal size, uses k-1 subsets as training sets each time, and the remaining subset is used as a validation set to evaluate the performance of the model. This process is repeated k times, and a different subset is selected as the validation set each time. Finally, the parameter configuration that performs best on all validation sets is selected as the parameters of the final model.

[0046] 6. Model training and adjustment: Use the training set to train the selected SVM model to obtain preliminary model parameters. Then, use the validation set to adjust the model by adjusting model parameters or using other regularization techniques to prevent overfitting and ensure that the model has good generalization ability. The model with the best performance on the validation set will be selected as the final model and the final performance evaluation will be performed on the test set.

[0047] 7. Model evaluation: Use the test set to evaluate the trained SVM model and evaluate the model's predictive ability. Calculate the model's evaluation indicators, such as accuracy, recall, F1 score, etc.

[0048] 8. Model deployment and application: Deploy the trained SVM model to actual applications to conduct real-time or regular assessments of the structural safety of offshore booster stations. Classify the structural safety levels based on the model output and make corresponding maintenance and reinforcement recommendations.

[0049] Throughout the process, the model needs to be continuously iterated and optimized to improve the accuracy and reliability of the evaluation. In addition, considering the possible uncertainties and dynamic changes in practical applications, it is also necessary to regularly update the model parameters and feature sets.

[0050] In this embodiment, in step S4, the specific implementation steps are as follows: According to step S4, the specific implementation steps are as follows: Obtain a structural safety monitoring and assessment model trained by a machine learning algorithm to assess the structural safety of the offshore booster station. Analyze the assessment results and identify potential safety hazards. Use the prediction model to predict possible failures of the offshore booster station. Determine the type and impact of the predicted safety hazards. And formulate targeted maintenance strategies based on the assessment results and failure predictions.

[0051] Through this embodiment, the structural safety of offshore wind farm booster stations can be effectively monitored and evaluated, which has important application value and promotion significance.

[0052] Example 3 like Figure 4 As shown, the present invention provides a structural safety assessment system for an offshore substation, comprising: The data acquisition unit monitors the corrosion, tilt, settlement and stress-strain parameters of the offshore booster station structure in real time to obtain monitoring data of the offshore booster station; The data processing unit pre-processes the monitoring data acquired from the offshore booster station, including filtering, denoising and data calibration; The evaluation unit trains the preprocessed data, establishes a structural safety monitoring and evaluation model, and evaluates the structural safety of the offshore booster station; The execution unit predicts faults and formulates maintenance strategies based on the evaluation results to ensure the safe operation of the offshore booster station.

[0053] In this embodiment, in the data acquisition unit, the corrosion, tilt, settlement and stress-strain parameters of the offshore booster station structure are monitored in real time to obtain monitoring data of the offshore booster station, including: Use sensors to monitor the corrosion, tilt, settlement and stress-strain parameters of offshore booster station structures in real time; The sensors include ultrasonic corrosion sensors, gyroscope sensors, displacement sensors, strain gauges and vibration sensors; The sensors are installed at various key positions of the steel structure of the booster station to extract parameter information related to the booster station structure; The monitoring data collected by the sensor is transmitted to the intelligent data collector.

[0054] In this embodiment, in the data processing unit, the monitoring data of the offshore booster station is preprocessed, including filtering, denoising and data calibration, including: The intelligent data collector receives monitoring data from various sensors on the offshore booster station structure, including corrosion degree, tilt status, settlement, stress-strain data and vibration parameters; The received data is aggregated and integrated in the intelligent data collector to form a complete data set with multiple parameter dimensions; then, the data set enters the deep processing stage, including data filtering, calibration and denoising operations; The deeply processed data set is sent to the data center by the intelligent data collector through the data transmission protocol; the data center, as the core of data processing and analysis, has computing power and storage resources; in the data center, the data set is used to train the structural safety monitoring and assessment model.

[0055] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0056] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for safety assessment of offshore booster station structure, characterized in that: include: Real-time monitoring of offshore booster station structure corrosion, tilt, settlement and stress-strain parameters to obtain monitoring data of offshore booster station; Preprocess the monitoring data of offshore booster stations, including filtering, denoising and data calibration; Train the preprocessed data to establish a structural safety monitoring and assessment model to assess the structural safety of offshore booster stations; Based on the evaluation results, fault prediction is carried out and maintenance strategies are formulated to ensure the safe operation of the offshore booster station.

2. A method for safety assessment of offshore booster station structure according to claim 1, characterized in that: The real-time monitoring of the corrosion, tilt, settlement and stress-strain parameters of the offshore booster station structure to obtain monitoring data of the offshore booster station includes: Use sensors to monitor the corrosion, tilt, settlement and stress-strain parameters of offshore booster station structures in real time; The sensors include ultrasonic corrosion sensors, gyroscope sensors, displacement sensors, strain gauges and vibration sensors; The sensors are installed at various key positions of the steel structure of the booster station to extract parameter information related to the booster station structure; The monitoring data collected by the sensor is transmitted to the intelligent data collector.

3. A method for safety assessment of offshore booster station structure according to claim 1, characterized in that: The preprocessing of the monitoring data of the offshore booster station, including filtering, denoising and data calibration, includes: The intelligent data collector receives monitoring data from various sensors on the offshore booster station structure, including corrosion degree, tilt status, settlement, stress-strain data and vibration parameters; The received data is aggregated and integrated in the intelligent data collector to form a complete data set with multiple parameter dimensions; then, the data set enters the deep processing stage, including data filtering, calibration and denoising operations; The deeply processed data set is sent to the data center by the intelligent data collector through the data transmission protocol; the data center, as the core of data processing and analysis, has computing power and storage resources; in the data center, the data set is used to train the structural safety monitoring and assessment model.

4. A method for safety assessment of offshore booster station structure according to claim 3, characterized in that: The sensor transmits the information to the data collector in real time via wired or wireless means.

5. A method for safety assessment of offshore booster station structure according to claim 3, characterized in that: Data filtering removes noise and interference signals and improves the signal-to-noise ratio of the data by selecting filtering algorithms, including limiting filtering, median filtering or arithmetic mean filtering; the calibration process adjusts the filtered data to eliminate sensor errors and deviations to ensure data accuracy; finally, the noise level in the data is reduced through denoising operations.

6. A method for safety assessment of offshore booster station structure according to claim 1, characterized in that: The pre-processed data is trained to establish a structural safety monitoring and evaluation model to evaluate the structural safety of the offshore booster station, including: Collect and organize the data of structural safety monitoring, perform data cleaning and feature engineering, and then divide the data set into training set, validation set and test set; Select support vector machine as the machine learning algorithm, adjust the parameters of SVM according to the characteristics of the data, and use cross-validation method to optimize the performance of the model; The SVM model is trained and evaluated. The training set is used to train the model, and the validation set is used to evaluate the performance of the model to ensure that the model has good generalization ability. The test set is used to evaluate the final trained SVM model to verify the model's predictive ability.

7. A method for safety assessment of offshore booster station structure according to claim 1, characterized in that: According to the evaluation results, fault prediction and maintenance strategy are formulated to ensure the safe operation of the offshore booster station, including: Use machine learning models to predict possible fault types and take maintenance measures in advance; For the diagnosed faults, the faulty parts are repaired or replaced to ensure that the booster station resumes normal operation as soon as possible.

8. An offshore booster station structure safety assessment system, characterized in that: include: The data acquisition unit monitors the corrosion, tilt, settlement and stress-strain parameters of the offshore booster station structure in real time to obtain monitoring data of the offshore booster station; The data processing unit pre-processes the monitoring data acquired from the offshore booster station, including filtering, denoising and data calibration; The evaluation unit trains the preprocessed data, establishes a structural safety monitoring and evaluation model, and evaluates the structural safety of the offshore booster station; The execution unit predicts faults and formulates maintenance strategies based on the evaluation results to ensure the safe operation of the offshore booster station.

9. The offshore booster station structure safety assessment system according to claim 8, characterized in that: In the data acquisition unit, the corrosion, tilt, settlement and stress-strain parameters of the offshore booster station structure are monitored in real time to obtain the monitoring data of the offshore booster station, including: Use sensors to monitor the corrosion, tilt, settlement and stress-strain parameters of offshore booster station structures in real time; The sensors include ultrasonic corrosion sensors, gyroscope sensors, displacement sensors, strain gauges and vibration sensors; The sensors are installed at various key positions of the steel structure of the booster station to extract parameter information related to the booster station structure; The monitoring data collected by the sensor is transmitted to the intelligent data collector.

10. The offshore booster station structure safety assessment system according to claim 9, characterized in that: In the data processing unit, the monitoring data of the offshore booster station is preprocessed, including filtering, denoising and data calibration, including: The intelligent data collector receives monitoring data from various sensors on the offshore booster station structure, including corrosion degree, tilt status, settlement, stress-strain data and vibration parameters; The received data is aggregated and integrated in the intelligent data collector to form a complete data set with multiple parameter dimensions; then, the data set enters the deep processing stage, including data filtering, calibration and denoising operations; The deeply processed data set is sent to the data center by the intelligent data collector through the data transmission protocol; the data center, as the core of data processing and analysis, has computing power and storage resources; in the data center, the data set is used to train the structural safety monitoring and assessment model.

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