Steel structure monitoring and early warning platform and method for offshore wind power booster station

Abstract

The invention relates to a steel structure monitoring and early warning platform and method for an offshore wind power booster station, and the safety state of the steel structure of the booster station is monitored and early warned in an omnibearing manner through a data communication collection module, a data analysis module, a visual display module, a mobile terminal display module, a data management module, a third-party interface module and the like. An intelligent configurable monitoring and early warning software architecture is adopted, the system is suitable for data monitoring and early warning of the offshore booster station, the limitation of a traditional monitoring system is solved, and the personalized monitoring requirement is met. The platform has system expansion and compatibility, and adapts to monitoring requirements of different booster stations. The visualization module comprises a 3D BIM model, and provides detailed and comprehensive data visualization experience in combination with monitoring data, sensor positions and early warning information. Multi-site management is supported, and steel structures of a plurality of booster stations are monitored and managed at the same time. A sensor technology, a user-defined data processing algorithm and user interface design are adopted, overall data are innovatively collected, analyzed and displayed, and the safety and reliability of the booster station steel structure are improved.

Description

Technical Field

[0001] The present invention belongs to the fields of structural monitoring, computer science and software engineering, and in particular relates to a steel structure monitoring and early warning platform and method for an offshore wind power booster station. Background Art

[0002] At present, with the rapid development of renewable energy, offshore wind power has become an important way to supply clean energy. In order to efficiently convert offshore wind energy into electrical energy and transmit it to the land power grid, offshore wind power systems usually use offshore wind power booster stations as key equipment, which are mainly responsible for boosting the electricity generated by offshore wind turbines so that it can be transmitted to the land power grid through cables. Therefore, the stability and reliability of the booster station are crucial to ensure the normal operation of the offshore wind power system. However, due to the extreme conditions and complexity of the offshore environment, the steel structure of the offshore wind power booster station is susceptible to factors such as seawater erosion, wind impact, climate change and marine biological corrosion, resulting in deformation, fatigue and damage of the structure. Therefore, it is critical to ensure the safety and reliability of the booster station structure.

[0003] Traditional steel structure monitoring mostly relies on manual inspections, regular testing and periodic offline data collection, which is inefficient, difficult to obtain data and unable to timely detect anomalies and potential risks in the structure. In recent years, with the continuous development of data communication, sensor technology and early warning algorithms, the application of monitoring and early warning systems in the field of industrial monitoring has become increasingly important to ensure the safety of equipment and structures, improve work efficiency and reduce accident risks. Especially in complex engineering environments such as offshore booster stations, the monitoring of stress and strain, uneven settlement, corrosion potential and vibration of steel structures is also of great significance to ensure the stability of equipment operation and extend its service life.

[0004] There are still some problems with the traditional monitoring system in the early warning monitoring of offshore substation steel structures. Traditional systems usually process data and make early warning judgments based on fixed algorithms and preset rules, lacking flexibility and customizability. This makes it difficult for the system to adapt to the needs of different scenarios and applications, and configuration and customization usually involve and require professional knowledge and complex operations. The customization process is cumbersome, which limits the scalability and adaptability of the system. In addition, the scalability of traditional systems is limited, and it is difficult to adapt to the introduction of new parameters and algorithms. It cannot keep up with the rapid development and innovation of monitoring technology, and cannot easily adapt to new monitoring requirements and algorithms. Summary of the invention

[0005] In order to solve the above-mentioned technical problems, the present invention provides a steel structure monitoring and early warning platform and method for an offshore wind power booster station, which aims to introduce advanced sensor technology, data algorithm and user interface design to realize real-time monitoring, data analysis and early warning functions of the offshore wind power booster station steel structure, so as to improve the safety, reliability and operation efficiency of the booster station steel structure.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A steel structure monitoring and early warning platform for an offshore wind power booster station, comprising: The data communication acquisition module collects data from various sensors installed on the offshore booster station’s steel structure in real time through Modbus TCP and MQTT communication protocols, and customizes the configuration and management of the collector; Data analysis module, which performs noise reduction, calibration, feature value extraction, health assessment and prediction, and statistical analysis on the collected data; The visualization display module is used to display monitoring data and analysis results in an intuitive and interactive way. It provides real-time and historical data query, data trend analysis, heat map display, multi-dimensional data comparison, real-time warning display, report generation and export, and customized configuration of visualization. It also provides a 3D BIM model of the entire booster station, integrates monitoring data, warning information and sensor information into the model, and can use the built-in health assessment algorithm to more intuitively display the health status of the steel structure of each module through color, and supports free rotation, zooming and roaming operations on the page and mobile devices; The mobile terminal display module provides a mobile terminal display for the visualization display module for more convenient access and real-time monitoring of data. On the basis of the visualization display module, it adds real-time warning notification and binding, report preview and quick sharing functions; Data management module supports data backup, recovery, and archiving, and has reserved interfaces for data analysis and mining; Early warning module, including abnormal data monitoring, intelligent early warning model management, real-time early warning notification and real-time monitoring diagnosis, and real-time health assessment of steel structures; Intelligent and configurable monitoring and early warning software architecture, used to provide architectural support for the early warning module; The third-party interface module is used to provide data query, real-time warning notification, data analysis, export report, interface security certification and technical support.

[0007] A further improvement of the present invention is that the various sensors installed on the steel structure include position vibration sensors, corrosion potential sensors, tilt sensors, stress strain sensors and uneven settlement sensors.

[0008] A further improvement of the present invention is that the data analysis function realizes the removal of noise interference and calibration of real-time data, the extraction of useful structural features from real-time data, real-time evaluation and analysis of the health status of steel structures, statistical analysis of the collected data, and generation of detailed reports and visual charts.

[0009] A further improvement of the present invention is that the visualization function is used to display real-time monitoring data and interactive operation logic; provide real-time data curves, structural deformation images and temperature distribution visualization; support user-defined chart configuration and display options.

[0010] A further improvement of the present invention is that the early warning module monitors and analyzes real-time data through the configuration algorithm model and the built-in algorithm model to detect whether there are abnormalities in the steel structure and the sensor. When the structural data approaches or reaches the warning condition and the critical state of the abnormality, the system will automatically generate abnormal warning information; when abnormal warning information occurs, an alarm will be immediately displayed on the interface, and the warning information will be sent to subscribed users; the platform records the historical abnormal warning information and the response processing measures; while issuing the warning notification, the platform will provide some recommended automated operations.

[0011] A further improvement of the present invention is that the notification method of sending the warning information to the subscribed users includes text messages, APP and email.

[0012] A further improvement of the present invention is that the data management module has a data backup and recovery mechanism; supports data archiving and long-term storage; and reserves a data analysis and mining interface.

[0013] A further improvement of the present invention is that the intelligent configurable monitoring and early warning software architecture provides a more flexible early warning configuration for the early warning module through the implementation of a variable library, an early warning library, a diagnostic library, an algorithm library, a driving engine and an execution engine.

[0014] A steel structure monitoring and early warning method for an offshore wind power booster station comprises: The data communication acquisition module collects data from various sensors installed on the offshore booster station’s steel structure in real time through Modbus TCP and MQTT communication protocols, and performs custom configuration and management of the collector; The data analysis module performs noise reduction, calibration, feature value extraction, health assessment and prediction, and statistical analysis on the collected data; The visualization module displays monitoring data and analysis results in an intuitive and interactive way, providing real-time and historical data query, data trend analysis, heat map display, multi-dimensional data comparison, real-time warning display, report generation and export, and customized visualization configuration. It also provides a 3D BIM model of the entire booster station, integrating monitoring data, warning information, and sensor information into the model. It can use the built-in health assessment algorithm to more intuitively display the health status of the steel structure of each module through color, and supports free rotation, zooming, and roaming operations on the page and mobile devices; The mobile terminal display module provides mobile terminal display for the visualization display module for more convenient access and real-time monitoring of data. On the basis of the visualization display module, it adds early warning real-time notification and binding, report preview and quick sharing functions; The data management module supports data backup, recovery, and archiving, and has reserved interfaces for data analysis and mining; The early warning module provides real-time early warning notifications, real-time monitoring and diagnosis, and real-time health assessment of the steel structure; The intelligent and configurable monitoring and early warning software architecture provides architectural support for the early warning module; The third-party interface module provides data query, real-time warning notification, data analysis, export report, interface security certification and technical support.

[0015] A further improvement of the present invention is that the various sensors installed on the steel structure include position vibration sensors, corrosion potential sensors, tilt sensors, stress strain sensors and uneven settlement sensors.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: The present invention provides a steel structure monitoring and early warning platform and method for an offshore wind power booster station, which mainly include a data communication acquisition module, a data analysis module, a visualization display module, a mobile terminal display module, a data management module, and a third-party interface module. The innovation of the present invention lies in the integration of multiple functional modules to realize comprehensive and real-time monitoring, data analysis, early warning and other functions of the steel structure. In the data communication acquisition module, by installing relevant sensors at key positions of the steel structure of the booster station, the stress, deformation, temperature, corrosion potential, vibration and other parameters of the offshore wind power booster station steel structure are monitored in real time. These sensors can accurately sense changes in the structural state, and the platform will monitor the data of these sensors in real time for processing and analysis. The module is compatible with multiple communication protocols to ensure that it can adapt to different types of sensor equipment.

[0017] In the data analysis module, advanced signal processing algorithms and machine learning technologies are applied, and advanced data processing algorithms and user-defined algorithm logic are used to filter, reduce noise, calibrate and extract eigenvalues ​​of real-time sensor data, ensuring the acquisition of high-precision structural status information, and timely analysis and early warning of abnormal data and equipment. At the same time, targeted health assessment algorithms are developed for the steel structure characteristics of the booster station, and the health status of the steel structure and sensors is monitored in real time. The machine learning algorithm is used to establish a structural health status model for fault diagnosis and prediction analysis.

[0018] The visualization display module provides a variety of icon types, such as line charts, bar charts, pie charts and other charts to more intuitively display monitoring data and analyze structures. It supports data trend analysis and generation of trend charts to help users understand the changing trends of structural parameters and observe the changes of structural parameters over time. It supports custom report generation, and supports users to configure specific time periods, structural parameters, report formats, etc. of monitoring data to generate reports that meet personalized needs. It supports report export in multiple formats, such as common PDF, Word and Excel. It provides a thermal map display module, including stress distribution maps (stress distribution of steel structures, including tension and pressure), deformation distribution maps (deformation distribution of steel structures, including displacement, tilt and deformation), temperature distribution maps (temperature distribution of steel structures, including temperature, thermal expansion and thermal stress of the structure) and other thermal distribution maps, which are convenient for users to analyze and identify abnormal areas more intuitively.

[0019] The mobile terminal display module provides users with convenient mobile terminal display, which enables users to view the monitoring data and 3D BIM model of the booster station through mobile devices anytime and anywhere, and obtain early warning information in a timely manner. The main functions include: visual display of real-time monitoring data, 3D BIM of the booster station, and real-time early warning notification. Through mobile devices, users are no longer restricted to a fixed working environment and can understand the status of the booster station in a timely manner, which improves the accessibility and convenience of the platform.

[0020] The data management module provides storage, management and query of detection data to ensure data security, integrity and reliability. The main functions include storing the collected data after being processed by the algorithm of the data analysis module, providing data backup, and recovering data in case of system failure or data loss. It provides data archiving and compresses data to facilitate long-term data storage. It provides flexible data query, authority management and data sharing.

[0021] The early warning module is based on the results of data processing and analysis, and it realizes the real-time early warning function. Users can configure the early warning module through the interface, including the early warning level, subsequent processing of early warning and notification method. Once the system detects an abnormality, it will notify in real time through the platform interface, SMS, email, etc. according to the specific early warning level, so that the operation and maintenance staff can take timely measures to avoid potential failures and damages.

[0022] In view of the technical problems such as insufficient flexibility and limited customization capability of traditional monitoring systems, a software framework for intelligent configurable monitoring and early warning is provided. The software framework allows the customization of early warning and monitoring details by writing and implementing Python algorithms, and realizes the customization of flexible processing of collected data and early warning rules. Users only need to write specific algorithm logic according to actual needs, and associate and configure with variable library, early warning library and diagnosis library to meet the personalized requirements in different scenarios and needs. The software framework provides flexible mapping of data points, allowing users to map data points collected by data collectors to user-defined variables, and configure the time window and data processing method of variables according to actual needs to realize customized management of variables. This is also a key innovation of the present invention. By realizing the intelligent configurable monitoring and early warning software framework, a flexible algorithm management mechanism is realized. Therefore, the software framework proposed by the present invention provides a solution for intelligent configurable monitoring and early warning. Through flexible algorithm configuration definition and data management mechanism, the function of customizing monitoring and early warning details according to actual needs and business scenarios is realized, making the monitoring system more flexible and efficient, and being able to timely discover and handle abnormal situations, improving the flexibility and intelligence level of steel structure system monitoring, and providing a reliable solution for monitoring in complex engineering environments such as offshore booster stations.

[0023] In the third-party interface module, by providing clear interface documents and technical support, third-party systems or applications are allowed to query and call the functions and data of the system, as well as early warning information, through API, to achieve data interaction, sharing and integration with other systems. The main functions include: data query, real-time early warning, data upload and sharing, security authentication and authority control, interface documents and technical support. The third-party module enhances the openness and scalability of the platform, and provides more application possibilities for steel structure monitoring and early warning of offshore booster stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The figure is a schematic diagram of the overall system structure of a steel structure monitoring and early warning platform of an offshore wind power booster station of the present invention.

[0025] Figure 2This is a schematic diagram of the early warning system architecture of the present invention, which includes a user layer, a data layer, an execution layer, and middleware. The user layer is a view layer for users to perform visual configuration, and is used to configure variables, early warnings, diagnoses, algorithms, and other information. The data layer is mainly used to cache and persist data. The execution layer includes two key engines - the driver engine and the execution engine. The middleware layer mainly refers to the message queue, which is used to realize asynchronous processing of tasks and message transmission, as well as decoupling between task modules.

[0026] Figure 3 A detailed flowchart for variable parsing. DETAILED DESCRIPTION

[0027] 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.

[0028] 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.

[0029] 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, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0030] 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.

[0031] 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.

[0032] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0033] Example 1 The present invention provides a steel structure monitoring and early warning platform for an offshore wind power booster station, which is used to monitor the safety status of the steel structure of the offshore booster station in real time, and to evaluate the health and safety status and issue early warnings in a timely manner. The monitoring and early warning platform includes: The data communication acquisition module collects data from various sensors installed on the offshore booster station’s steel structure in real time through Modbus TCP and MQTT communication protocols, and customizes the configuration and management of the collector; Data analysis module, which performs noise reduction, calibration, feature value extraction, health assessment and prediction, and statistical analysis on the collected data; The visualization display module is used to display monitoring data and analysis results in an intuitive and interactive way. It provides real-time and historical data query, data trend analysis, heat map display, multi-dimensional data comparison, real-time warning display, report generation and export, and customized configuration of visualization. It also provides a 3D BIM model of the entire booster station, integrates monitoring data, warning information and sensor information into the model, and can use the built-in health assessment algorithm to more intuitively display the health status of the steel structure of each module through color, and supports free rotation, zooming and roaming operations on the page and mobile devices; The mobile terminal display module provides a mobile terminal display for the visualization display module for more convenient access and real-time monitoring of data. On the basis of the visualization display module, it adds real-time warning notification and binding, report preview and quick sharing functions; Data management module supports data backup, recovery, and archiving, and has reserved interfaces for data analysis and mining; Early warning module, including abnormal data monitoring, intelligent early warning model management, real-time early warning notification and real-time monitoring diagnosis, and real-time health assessment of steel structures; Intelligent and configurable monitoring and early warning software architecture provides architectural support for flexible early warning modules; The third-party interface module is a separate module provided by the platform for external integration with other systems, including data query, real-time warning notification, data analysis, export report, interface security certification and technical support.

[0034] The present invention is described in detail below: The first step is to deploy hardware equipment, deploy sensor equipment and networks, and arrange high-precision stress sensors, deformation sensors and temperature sensors at key parts of the steel structure of the booster station, such as support structures and connecting rods. The sensors are connected to the data acquisition equipment through wired or wireless means. Dedicated data acquisition equipment is configured to collect sensor data in real time. The equipment should have high-performance data processing modules and storage capabilities, and be able to interact with the platform through various protocols.

[0035] To implement the data communication module, it is necessary to design and develop a data collector based on the Internet of Things technology, supporting multiple communication protocols (including but not limited to Modbus TCP, MQTT, etc.), and realizing communication connection and data collection with various sensors and monitoring equipment. The collector needs to have high concurrent processing capabilities and data caching functions, and be able to collect real-time data stably and efficiently. The collector should also implement an automatic fault recovery and reconnection mechanism, be able to deal with communication anomalies and collection equipment failures, and ensure the stability and continuity of collection.

[0036] To realize the data analysis module, it is necessary to process the real-time collected data by implementing filtering and denoising algorithms, remove noise interference, calibrate the data, and eliminate possible errors to obtain more accurate structural status data; by implementing feature extraction algorithms, useful structural features such as vibration frequency, strain peak, etc. are extracted from real-time data for structural anomaly detection; structural health assessment algorithms are applied to conduct real-time assessment and analysis of the health status of steel structures, including assessments of the strength, stiffness, stability, etc. of the structure; by implementing built-in and configurable early warning algorithms, real-time data is analyzed to detect and diagnose abnormal conditions of steel structures; historical data is used to implement trend analysis algorithms to predict the future behavior and performance of steel structures, detect possible faults and damage in advance, and take corresponding preventive measures; statistical analysis is performed on the collected data to generate detailed reports and visual charts to understand the overall status and trend changes of the substation structure.

[0037] For the implementation of the visualization module, the front-end development uses HTML, CSS, JavaScript, VUE and other front-end development technologies to build the user interface. By introducing popular visualization libraries (such as Echarts, D3.js, Highcharts, etc.), a rich variety of icon types and interactive functions are realized. The SSE communication protocol is used to receive warnings or other notifications pushed by the server in real time to achieve real-time notification of warnings. By using the Threejs framework to introduce the display of the booster station 3D BIM model, interactive operations such as rotation, zooming in, zooming out and roaming of the booster station model are realized, and the real-time data of sensors at various locations of the booster station and the results of various algorithm analyses are combined. They are displayed in the booster station model in real time in a visual way, realizing a comprehensive understanding of the details and status of the booster station.

[0038] For the implementation of the mobile display module, React Native, Flutter, etc. are used to develop mobile apps for Android and IOS systems. In the interface design, a responsive design solution should be provided to adapt to mobile devices of different sizes, and a friendly and simple interactive layout and operation method should be provided to ensure that users can quickly get started. In the introduction of the 3D BIM model of the booster station on the mobile terminal, WebGL technology and mobile graphics library are used to load and interact with the model. By implementing the subscription notification method, notifications for subscription messages can be quickly received on mobile devices.

[0039] For the implementation of the data management module, the first step is to design the database. The MySQL relational database and the Redis non-relational database are used. The data table structure is designed according to the structure of the actual monitoring data and the characteristics of the implementation function. The real-time monitoring data and the structure of the algorithm analysis are cached and persisted in real time. Real-time backup of the database is provided to ensure the security and persistence of the data. Timed archiving of data is provided. The archived data can compress and save historical monitoring data to facilitate subsequent data query and analysis. Data permission control is provided, including user authentication and role management, to ensure that different users or roles have different permissions, and only authorized users can access and operate the corresponding data.

[0040] A simple implementation of the proposed intelligent configurable monitoring and early warning software architecture. Specifically, the software architecture includes the following elements: Variable library: used for custom variables. Variables are divided into three types: common variables, mapping variables and calculated variables. For the implementation of the variable library, a flexible and extensible variable database table needs to be established to store user-defined variables and the mapping relationship with the data points collected by the collector. Specifically, it includes variable ID, name, type (common variable, mapping variable, calculated variable), data type (integer, floating point, set, string), default value, variable action mapping type (average, maximum, minimum, quantity value and set value), mapping field name, calculation cycle, remarks and other basic information. The creation, editing and deletion of variables can be realized through the interface. In addition, a management module for the variable library needs to be implemented, which is responsible for the operation, verification and maintenance of variables, ensuring the accuracy and completeness of the mapping relationship and configuration of variables and that the collected data can correctly correspond to the defined variables. Common variables can define constant data. Users can define some warning thresholds or constants as variables in the variable library, so as to dynamically modify the warning thresholds and constants without modifying the algorithm source file to meet the needs of flexibility. Mapping variables can map the data points collected by the data collector into a variable in the variable library, which obtains the real-time data of the mapped data points. Calculation variables are preprocessed on the basis of mapping variables. The basic preprocessing logic of the mapped data points can be defined by specifying the calculation time range (specifying the number of recently collected data or the data within a recent period of time) and the calculation type (maximum value, minimum value, average value, set value, etc.), so as to obtain data variables with more practical value.

[0041] Warning library: used to customize the warning information output by the algorithm, including the definition of the warning level, title, and warning information. A database table for storing warning information is required to store warning information and related information. Specifically, it includes warning name, warning level, value list that triggers warning records, related systems (corrosion, uneven settlement, vibration, stress and strain, etc.), notes, and other information.

[0042] Diagnostic library: used to customize the matching diagnostic information output by the algorithm, including the correlation factor analysis and statistical factor analysis of the early warning trigger. It is necessary to establish a database table to store diagnostic information, which is used to store the metadata data of the early warning diagnosis, including the diagnosis name, the correlation factor analysis of the diagnosis, the statistical factor analysis of the diagnosis, and the notes.

[0043] Algorithm library: used for custom algorithms. By importing specific algorithm files, they are associated with the information configuration defined in the variable library, warning library, and diagnostic library. Flexible algorithm configuration options are provided to enable the configuration of the algorithm's input variables and output warning and diagnostic information according to specific needs, and to achieve intelligent data analysis and warning judgment. An algorithm table is designed to store custom algorithm configuration information, mainly used to store algorithm-related configurations, including model number, model name, associated system, input variable list, output warning list, output diagnostic list, algorithm file, enable, description, and other information.

[0044] Driving engine: The engine that is mainly responsible for driving the execution of the entire early warning framework needs to implement real-time scanning and grabbing of algorithm configurations from the database, obtain algorithms that meet the triggering conditions, and analyze the variable list in the algorithm configuration to calculate and obtain the actual data value. Then create a specific algorithm execution task, including the algorithm to be executed and the actual variable list data, and push the algorithm execution task to the message queue to wait for the execution engine to obtain and execute. The driving engine also needs to implement real-time monitoring of the result data returned by the execution engine after executing the algorithm task in the message queue, and use these data to analyze whether the algorithm has newly triggered warnings or warnings that need to be restored. If there is a newly triggered warning, warning information will be generated according to the warning list and diagnosis list configured in the algorithm, and the warning event will be published. If there is a warning that needs to be restored (restoration means that it was in the triggered state before, and it is no longer triggered in the current result), find the currently triggered warning and update the warning status; design the execution engine, which is mainly responsible for real-time monitoring and pulling the algorithm execution tasks in the message queue. Once a task enters the message queue, the variables in the algorithm task are parsed and processed and the corresponding Python algorithm is executed. The execution result is obtained and then packaged and returned to the message queue.

[0045] Execution engine: parses variables and executes specified algorithms by listening to algorithm tasks in the message queue, and returns the execution results to the message queue.

[0046] Example 2 This embodiment provides a steel structure monitoring and early warning method for an offshore wind power booster station, comprising: The data communication acquisition module collects data from various sensors installed on the offshore booster station’s steel structure in real time through Modbus TCP and MQTT communication protocols, and performs custom configuration and management of the collector; The data analysis module performs noise reduction, calibration, feature value extraction, health assessment and prediction, and statistical analysis on the collected data; The visualization module displays monitoring data and analysis results in an intuitive and interactive way, providing real-time and historical data query, data trend analysis, heat map display, multi-dimensional data comparison, real-time warning display, report generation and export, and customized visualization configuration. It also provides a 3D BIM model of the entire booster station, integrating monitoring data, warning information, and sensor information into the model. It can use the built-in health assessment algorithm to more intuitively display the health status of the steel structure of each module through color, and supports free rotation, zooming, and roaming operations on the page and mobile devices; The mobile terminal display module provides mobile terminal display for the visualization display module for more convenient access and real-time monitoring of data. On the basis of the visualization display module, it adds early warning real-time notification and binding, report preview and quick sharing functions; The data management module supports data backup, recovery, and archiving, and has reserved interfaces for data analysis and mining; The early warning module provides real-time early warning notifications, real-time monitoring and diagnosis, and real-time health assessment of the steel structure; The intelligent and configurable monitoring and early warning software architecture provides architectural support for the early warning module; The third-party interface module provides data query, real-time warning notification, data analysis, export report, interface security certification and technical support.

[0047] 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.

[0048] 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 steel structure monitoring and early warning platform for an offshore wind power booster station, characterized in that: include: The data communication acquisition module collects data from various sensors installed on the offshore booster station’s steel structure in real time through Modbus TCP and MQTT communication protocols, and customizes the configuration and management of the collector; Data analysis module, which performs noise reduction, calibration, feature value extraction, health assessment and prediction, and statistical analysis on the collected data; The visualization display module is used to display monitoring data and analysis results in an intuitive and interactive way. It provides real-time and historical data query, data trend analysis, heat map display, multi-dimensional data comparison, real-time warning display, report generation and export, and customized configuration of visualization. It also provides a 3D BIM model of the entire booster station, integrates monitoring data, warning information and sensor information into the model, and can use the built-in health assessment algorithm to more intuitively display the health status of the steel structure of each module through color, and supports free rotation, zooming and roaming operations on the page and mobile devices; The mobile terminal display module provides a mobile terminal display for the visualization display module for more convenient access and real-time monitoring of data. On the basis of the visualization display module, it adds real-time warning notification and binding, report preview and quick sharing functions; Data management module supports data backup, recovery, and archiving, and has reserved interfaces for data analysis and mining; Early warning module, including abnormal data monitoring, intelligent early warning model management, real-time early warning notification and real-time monitoring diagnosis, and real-time health assessment of steel structures; Intelligent and configurable monitoring and early warning software architecture, used to provide architectural support for the early warning module; The third-party interface module is used to provide data query, real-time warning notification, data analysis, export report, interface security certification and technical support.

2. The steel structure monitoring and early warning platform for an offshore wind power booster station according to claim 1 is characterized in that: Various types of sensors installed on steel structures include position vibration sensors, corrosion potential sensors, tilt sensors, stress strain sensors and uneven settlement sensors.

3. The steel structure monitoring and early warning platform for an offshore wind power booster station according to claim 1 is characterized in that: The data analysis function can remove noise interference and calibrate real-time data, extract useful structural features from real-time data, evaluate and analyze the health status of steel structures in real time, perform statistical analysis on the collected data, and generate detailed reports and visual charts.

4. The steel structure monitoring and early warning platform for an offshore wind power booster station according to claim 1, characterized in that: The visualization function is used to display real-time monitoring data and interactive operation logic; provide real-time data curves, structural deformation images and temperature distribution visualization; support user-defined chart configuration and display options.

5. The steel structure monitoring and early warning platform for an offshore wind power booster station according to claim 1, characterized in that: The early warning module monitors and analyzes real-time data through the configuration algorithm model and the built-in algorithm model to detect whether there are abnormalities in the steel structure and sensors. When the structural data approaches or reaches the early warning condition and abnormal critical state, the system will automatically generate abnormal early warning information; When abnormal warning information occurs, an alarm will be immediately displayed on the interface, and warning information will be sent to subscribed users; the platform records historical abnormal warning information and response measures; while issuing a warning notification, the platform will provide some recommended automated operations.

6. A steel structure monitoring and early warning platform for an offshore wind power booster station according to claim 5, characterized in that: The notification methods for sending warning information to subscribed users include SMS, APP and email.

7. The steel structure monitoring and early warning platform for an offshore wind power booster station according to claim 1, characterized in that: The data management module has a data backup and recovery mechanism; supports data archiving and long-term storage; and reserves data analysis and mining interfaces.

8. The steel structure monitoring and early warning platform for an offshore wind power booster station according to claim 1, characterized in that: The intelligent and configurable monitoring and early warning software architecture provides more flexible early warning configuration for the early warning module through the implementation of variable library, early warning library, diagnosis library, algorithm library, driving engine and execution engine.

9. A steel structure monitoring and early warning method for an offshore wind power booster station, characterized in that: include: The data communication acquisition module collects data from various sensors installed on the offshore booster station’s steel structure in real time through Modbus TCP and MQTT communication protocols, and performs custom configuration and management of the collector; The data analysis module performs noise reduction, calibration, feature value extraction, health assessment and prediction, and statistical analysis on the collected data; The visualization module displays monitoring data and analysis results in an intuitive and interactive way, providing real-time and historical data query, data trend analysis, heat map display, multi-dimensional data comparison, real-time warning display, report generation and export, and customized visualization configuration. It also provides a 3D BIM model of the entire booster station, integrating monitoring data, warning information, and sensor information into the model. It can use the built-in health assessment algorithm to more intuitively display the health status of the steel structure of each module through color, and supports free rotation, zooming, and roaming operations on the page and mobile devices; The mobile terminal display module provides mobile terminal display for the visualization display module for more convenient access and real-time monitoring of data. On the basis of the visualization display module, it adds early warning real-time notification and binding, report preview and quick sharing functions; The data management module supports data backup, recovery, and archiving, and has reserved interfaces for data analysis and mining; The early warning module provides real-time early warning notifications, real-time monitoring and diagnosis, and real-time health assessment of the steel structure; The intelligent and configurable monitoring and early warning software architecture provides architectural support for the early warning module; The third-party interface module provides data query, real-time warning notification, data analysis, export report, interface security certification and technical support.

10. A steel structure monitoring and early warning method for an offshore wind power booster station according to claim 9, characterized in that: Various types of sensors installed on steel structures include position vibration sensors, corrosion potential sensors, tilt sensors, stress strain sensors and uneven settlement sensors.

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