CN117392824A - Steel structure monitoring and early warning platform of offshore wind power booster station - Google Patents
Steel structure monitoring and early warning platform of offshore wind power booster station Download PDFInfo
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Abstract
The invention relates to a steel structure monitoring and early warning platform of an offshore wind power booster station. The intelligent configurable monitoring and early warning software architecture is adopted, is suitable for data monitoring and early warning of the offshore booster station, solves the limitation of the traditional monitoring system, and meets the personalized monitoring requirement. The platform has system expansion and compatibility, and is suitable for monitoring requirements of different booster stations. The visualization module comprises a 3D BIM model, and provides fine and comprehensive data visualization experience by combining monitoring data, sensor positions and early warning information. And supporting multi-site management, and simultaneously monitoring and managing the steel structures of a plurality of booster stations. And by adopting a sensor technology, a custom data processing algorithm and a user interface design, the whole data is innovatively collected, analyzed and displayed, and the safety and reliability of the steel structure of the booster station are improved.
Description
Technical Field
The invention relates to the field of structural monitoring, computer science and software engineering, in particular to a steel structure monitoring and early warning platform of an offshore wind power booster station.
Background
At present, with the rapid development of renewable energy sources, offshore wind power has become an important clean energy supply mode. In order to efficiently convert offshore wind energy into electric energy and transmit the electric energy to a land grid, an offshore wind power system generally adopts an offshore wind power booster station as a key device and is mainly responsible for boosting electric energy generated by an offshore wind power generator set so as to be transmitted to the land grid through a cable. The stability and reliability of the booster station is therefore critical to ensure proper operation of the offshore wind power system. However, due to extreme conditions and complexity of the offshore environment, the steel structure of the offshore wind power booster station is susceptible to seawater erosion, wind impact, climate change, marine organism corrosion and other factors, resulting in deformation, fatigue and damage of the structure. Therefore, the safety and the reliability of the booster station structure are very critical.
Most of traditional steel structure monitoring relies on manual inspection, periodic detection and periodic offline data acquisition, and the mode is low in efficiency, and the data acquisition is difficult and abnormality and potential risks existing in the structure cannot be found in time. 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 is increasingly important to ensure the safety of equipment and structures, improve the working efficiency and reduce the risk of accidents. Particularly in complex engineering environments such as offshore booster stations, the monitoring of data such as stress strain, differential settlement, corrosion potential and vibration of a steel structure is of great significance in ensuring the running stability of equipment and prolonging the service life.
The traditional monitoring system still has some problems in the early warning and monitoring of the offshore booster station steel structure. Conventional systems generally perform data processing and early warning judgment based on fixed algorithms and preset rules, and lack flexibility and customizability. This results in a system that is difficult to accommodate for the needs of different scenarios and applications, and configuration and customization often involves and requires expertise and complex operations, and the customization process is cumbersome, limiting the scalability and adaptability of the system. In addition, the traditional system has limited expansibility, is difficult to adapt to the introduction of new parameters and algorithms, cannot keep pace with the rapid development and innovation of the monitoring technology, and cannot easily adapt to new monitoring requirements and algorithms.
Disclosure of Invention
In order to solve the technical problems, the invention provides a steel structure monitoring and early warning platform of an offshore wind power booster station, which aims to introduce advanced sensor technology, data algorithm and user interface design, realize the functions of real-time monitoring, data analysis and early warning of the steel structure of the offshore wind power booster station and improve the safety, reliability and operation efficiency of the steel structure of the booster station.
The invention is realized by adopting the following technical scheme:
the utility model provides a steel construction monitoring early warning platform of marine wind-powered electricity generation booster station for the safe state of real-time supervision marine booster station steel construction, and in time make the early warning to the health and safety state aassessment, this monitoring early warning platform includes:
the data communication acquisition module is used for acquiring various sensor data of the offshore booster station, which are installed on the detection steel structure, in real time through Modbus TCP and MQTT communication protocols, and carrying out custom configuration and management on the acquisition device;
the data analysis module is used for carrying out noise reduction, calibration, characteristic value extraction, health assessment and prediction and statistical analysis on the acquired data;
the visual display module is used for displaying monitoring data and analysis results in an intuitive and interactive mode, providing real-time and historical data inquiry, data trend analysis, thermodynamic diagram display, multidimensional data comparison, real-time early warning display, report generation and export and visual self-defining configuration, providing a 3D BIM model of the whole booster station, integrating the monitoring data, early warning information and sensor information into the model, and being capable of displaying the health state of the steel structure of each module more intuitively through a built-in health evaluation algorithm by colors, and supporting free rotation, scaling and roaming operation of equipment at a page and a mobile terminal;
the mobile terminal display module is used for providing display of the mobile terminal for the visual display module so as to access and monitor data in real time more conveniently, and early warning real-time notification and binding, report preview and quick sharing functions are added on the basis of the visual display module;
the data management module supports backup, recovery and archiving of data and reserves a data analysis and mining interface;
the early warning module comprises abnormal data monitoring, intelligent early warning model management, real-time early warning notification and real-time monitoring diagnosis, and carries out health assessment on the steel structure in real time;
an intelligent configurable monitoring and early warning software architecture provides architecture support for a flexible early warning module;
the third party interface module is an independent module provided by the platform for integrating locks with other systems, and comprises data inquiry, real-time early warning notification, data analysis, export report, interface security authentication and technical support.
The invention is further improved in that the various sensors mounted on the steel structure comprise a bit vibration sensor, a corrosion potential sensor, an inclination sensor, a stress strain sensor and an uneven settlement sensor.
The invention is further improved in that the data analysis function realizes noise interference removal and calibration of real-time data, extraction of useful structural features from the real-time data, real-time evaluation and analysis of the health state of the steel structure, statistical analysis of the acquired data, and generation of detailed reports and visual charts.
The invention is further improved in that the visual display function provides an intuitive and scientific interface for displaying real-time monitoring data and interactive operation logic; providing a real-time data curve, a structural deformation image and a visual display of temperature distribution; and supporting user-defined chart configuration and display options.
The invention is further improved in that the early warning module monitors and analyzes real-time data through the configuration algorithm model and the built-in algorithm model, detects whether the steel structure and the sensor are abnormal, and when the structural data are close to or reach the early warning condition and the abnormal critical state, the system automatically generates abnormal early warning information; when abnormal early warning information occurs, an alarm is immediately given out on an interface, and early warning information is sent to a subscribed user; the platform records historical abnormal early warning information and response processing measures; the platform will provide some suggested automation while issuing the alert notification.
The invention is further improved in that the notification mode for sending the early warning information to the subscribed user comprises a short message, an APP and a mail.
The invention is further improved in that the data management module is provided with a data backup and recovery mechanism; support data archiving and long-term storage; reserving a data analysis and mining interface.
The intelligent configurable monitoring and early warning software architecture provides more flexible early warning configuration for the early warning module through the implementation of a variable library, an early warning library, a diagnosis library, an algorithm library, a driving engine and an execution engine.
The invention has at least the following beneficial technical effects:
the invention provides a steel structure monitoring and early warning platform of an offshore wind power booster station, which mainly comprises a data communication acquisition 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.
The invention has the innovation point that a plurality of functional modules are integrated, and the functions of comprehensive real-time monitoring, data analysis, early warning and the like of the steel structure are realized. In the data communication acquisition module, parameters such as stress, deformation, temperature, corrosion potential, vibration and the like of the steel structure of the offshore wind power booster station are monitored in real time by installing related sensors at key positions of the steel structure of the booster station. The sensors can accurately sense the change of the structural state, and the platform monitors the data of the sensors in real time for processing and analyzing. The module is compatible with various communication protocols, and can be used for ensuring that different types of sensor equipment can be excessively adapted.
In the data analysis module, advanced signal processing algorithm and machine learning technology are applied, and advanced data processing algorithm and user-defined algorithm logic are utilized to realize filtering, noise reduction, calibration and characteristic value extraction of sensor real-time data, ensure that high-precision structural state information is obtained, and timely realize real-time analysis and early warning of abnormal data and equipment. Meanwhile, aiming at the steel structure characteristics of the booster station, a targeted health evaluation algorithm is developed, the health states of the steel structure and the sensor are monitored in real time, a machine learning algorithm is utilized to build a health state model of the structure, and fault diagnosis and predictive analysis are carried out.
The visual display module provides various icon types, such as a line graph, a bar graph, a pie chart and other chart modes, so as to more intuitively display the monitoring data and the analysis structure. The trend analysis of the data is supported, a trend graph is generated, a user is helped to know the change trend of the structural parameters, and the change of the structural parameters along with time is observed. The report generation method and the report generation system support custom report generation, and support a user to configure a specific time period, structural parameters, report formats and the like of the monitoring data, so as to generate a report meeting personalized requirements. Report exports in multiple formats are supported, such as common PDF, word, excel, and the like. Providing a thermodynamic diagram display module, which comprises a stress distribution diagram (the stress distribution situation of the steel structure comprises tension and pressure), a deformation distribution diagram (the deformation distribution situation of the steel structure comprises displacement, inclination and deformation), a temperature distribution diagram (the temperature distribution situation of the steel structure comprises temperature, thermal expansion and thermal stress of the structure) and other thermodynamic distribution diagrams, and is convenient for a user to more intuitively analyze and identify an abnormal region.
The mobile terminal display module provides convenient mobile terminal display for users, and can check monitoring data of the booster station and the 3D BIM model through mobile terminal equipment at any time and any place to acquire early warning information in time. The main functions are as follows: visual display of real-time monitoring data, 3D BIM of a booster station and real-time early warning notification. Through the mobile terminal equipment, a user is not limited by a fixed working environment any more, and timely knows the state of the booster station, so that the accessibility and convenience of the platform are improved.
The data management module is used for storing, managing and inquiring the detection data and guaranteeing the safety, the integrity and the reliability of the data. The main function is to store the collected data after the algorithm processing of the data analysis module, provide data backup, and can recover the data when the system fails or the data is recovered. And the data archiving is provided, the data is compressed, and the long-term data storage is facilitated. Flexible data querying, rights management and data sharing are provided.
The early warning module is based on the results of data processing and analysis, and the real-time early warning function is realized through the module. The user can configure the early warning module through the interface, including early warning grade, early warning subsequent processing, notification mode and the like. Once the system monitors abnormality, the system can inform the system in real time through a platform interface, a short message, a mail and the like according to the specific early warning level, so that operation and maintenance staff can take measures in time, and potential faults and damages are avoided.
Aiming at the technical problems of insufficient flexibility, limited customization capability and the like of the traditional monitoring system, the intelligent configurable monitoring and early warning software rack is provided. The software architecture allows customization of pre-warning rules and flexible processing of collected data by compiling and implementing Python algorithm custom pre-warning and monitoring details. The user only needs to write specific algorithm logic according to actual demands and perform associated configuration with a variable library, an early warning library and a diagnosis library, so that personalized requirements in different scenes and demands are met. The software architecture provides flexible mapping of data points, allows a user to map the data points acquired by the data acquisition device into user-defined variables, configures a time window of the variables according to actual requirements and a data processing mode, and achieves customized management of the variables. The intelligent configurable monitoring and early warning software architecture is a key innovation point, and a flexible algorithm management mechanism is realized by realizing the intelligent configurable monitoring and early warning software architecture. The software architecture provided by the invention provides an intelligent configurable monitoring and early warning solution, and the functions of self-defining the monitoring and early warning details according to actual demands and service scenes are realized through flexible algorithm configuration definition and data management mechanisms, so that the monitoring system is more flexible and efficient, abnormal conditions can be found and processed in time, the flexibility and the intelligent level of monitoring of the steel structure system are improved, and a reliable solution is provided for monitoring in complex engineering environments such as offshore booster stations.
And in the third party interface module, the function and data of the system and the early warning information are inquired and called by a third party system or an application program through an API by providing clear interface documents and technical support, so that the interaction, sharing and fusion of the data with other systems are realized. The main functions include: data query, real-time early warning, data uploading and sharing, security authentication and authority control, interface documents and technical support. The third party module enhances the openness and expansibility of the platform and provides more application possibilities for monitoring and early warning of the steel structure of the offshore booster station.
Drawings
Fig. 1 is a schematic diagram of an overall system structure of a steel structure monitoring and early warning platform of an offshore wind power booster station.
FIG. 2 is a schematic diagram of an early warning system architecture of the present invention, including a user layer, a data layer, an execution layer, and middleware. The user layer is a view layer for visual configuration of a user and is used for configuring information such as variables, early warning, diagnosis, algorithms and the like. The data layer primarily caches and persists data for the user. The execution layer contains two critical engines-the driver engine and the execution engine. The middleware layer is mainly used for realizing asynchronous processing of tasks and message transmission and decoupling among task modules.
Fig. 3 is a detailed flow chart for variable resolution.
Detailed Description
The following is a clear and complete description of specific embodiments of the present invention. It is apparent that the described examples are merely a summary of a particular implementation of the invention and that specific details need not be implemented in order to provide a complete description of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.
Firstly, deploying hardware equipment, deploying sensor equipment and a network, and arranging high-precision stress sensors, deformation sensors and temperature sensors at key positions of a steel structure of a booster station, such as a supporting structure, a connecting rod and the like. The sensor is connected with the data acquisition equipment in a wired or wireless mode. The special data acquisition equipment is configured for acquiring sensor data in real time, and the equipment is provided with a high-performance data processing module and a high-performance storage capacity and can interact with the platform through various protocols.
The realization of the data communication module requires the design and development of a data collector based on the technology of the Internet of things, supports various communication protocols (including Modbus TCP, MQTT and the like, and is not limited to Modbus TCP, MQTT and the like), and the communication connection and data collection between the data collector and various sensors and monitoring equipment are realized. The collector needs to have high concurrent processing capacity and a data caching function, and can stably and efficiently collect real-time data. The collector also realizes an automatic fault recovery and reconnection mechanism, can cope with the conditions of abnormal communication, faults of collecting equipment and the like, and ensures the stability and continuity of collection.
The realization of the data analysis module requires processing the data acquired in real time by realizing a filtering and denoising algorithm, removing noise interference, calibrating the data, and eliminating possible errors to acquire more accurate structural state data; extracting useful structural features such as vibration frequency, strain peak value and the like from real-time data by realizing a feature extraction algorithm, and detecting structural abnormality; the method comprises the steps of applying a structural health evaluation algorithm to evaluate and analyze the health state of a steel structure in real time, wherein the evaluation comprises the evaluation of the strength, the rigidity, the stability and the like of the structure; analyzing, detecting and diagnosing abnormal conditions of the steel structure by realizing a built-in and configurable early warning algorithm; utilizing historical data to realize an algorithm for trend analysis, predicting future behaviors and performances of the steel structure, discovering possible faults and damages in advance, and taking corresponding preventive measures; and carrying out statistical analysis on the acquired data to generate a detailed report and a visual chart so as to know the overall state and trend change of the booster station structure.
For the implementation of the visualization module, the front-end development uses HTML, CSS, javaScript and VUE front-end development technologies to build a user interface, and by introducing popular visualization libraries (such as echorts, d3.Js, highcharts, etc.), rich icon types and interactive functions are realized. And the SSE communication protocol is used for receiving early warning or other notification pushed by the server side in real time, so that real-time notification of the early warning is realized. Through the use of the Threejs frame to introduce the display of the 3D BIM model of the booster station, the interactive operations such as rotation, amplification, shrinkage, roaming and the like of the booster station model are realized, and the results of the sensor real-time data and the algorithm analysis of each position of the booster station are combined and displayed in the booster station model in real time in a visual mode, so that the details and the states of the booster station are comprehensively known.
For the implementation of the mobile terminal presentation module, real Native, router, etc. are used to develop mobile terminal apps suitable for Android and IOS systems. Responsive design schemes should be provided in the interface design to adapt to mobile devices of different sizes, and to provide friendly and simple interactive layout and operation modes, ensuring that users can quickly get hands on. In the 3D BIM model introduction of the booster station by the mobile terminal, a WebGL technology and a graphic library of the mobile terminal are used to realize the loading and interaction of the models. By means of the subscription notification, notification for the subscription message can be quickly received in the mobile terminal device.
For the realization of the data management module, firstly, designing a database, adopting a mysql relational database and a redis non-relational database, and designing a data table structure according to the structure of actual monitoring data and the characteristics of realization functions. And carrying out real-time caching and persistence on the data monitored in real time and the structure analyzed by the algorithm. And providing real-time backup of the database, and ensuring the safety and durability of the data. And the data is regularly archived, and the archived data can compress and store historical monitoring data, so that the subsequent data query and analysis are facilitated. And providing control of data authority, including user authentication and role management, ensuring that different users or roles possess different authorities, and only authorized users can access and operate corresponding data.
For the simple realization of the proposed intelligent configurable monitoring and early warning software architecture. Specifically, the software architecture includes the following elements:
variable library: the method is used for customizing the variables, and the variables are divided into three types, namely common variables, mapping variables and calculating variables. For realizing the variable library, a flexible and extensible variable database table is required to be established and used for storing user-defined variables and mapping relations with data points acquired by the acquirer, and the basic information such as a specific package expansion variable ID, a name, a type (common variable, a mapping variable and a calculation variable), a data type (integer, floating point type, set and character string), a default value, a variable action mapping type (average value, maximum value, minimum value, number value and set value), a mapping field name, a calculation period, remarks and the like. The operations of creating, editing and deleting the variable can be realized through an interface. In addition, a management module for the variable library is required to be realized, and is responsible for the operation, verification and maintenance of the variables, so that the accuracy and the integrity of the mapping relation and the configuration of the variables are ensured, and the acquired data can be correctly corresponding to the defined variables. The common variables can define constant data, and a user can define some early warning thresholds or constants as variables in a variable library, so that dynamic modification of the early warning thresholds and the constants is realized under the condition that an algorithm source file is not modified, and the requirement of flexibility is met. Mapping variables may be implemented to map data points collected by a data collector to a variable in a variable library that obtains real-time data of the mapped data points. The calculation variable is preprocessed on the basis of the mapping variable, and definition of preprocessing logic on the basis of the mapped data points can be realized by specifying a calculation time range (specifying how much data is collected or how long data is in), and a calculation type (maximum value, minimum value, average value, aggregate value and the like), so that the data variable with more practical value is obtained.
Early warning library: the early warning prompt information used for the self-defined algorithm output comprises grade, title and early warning information defining the early warning. A database table for storing the early warning information is required to be established and used for storing the early warning prompt information and the associated information. The method specifically comprises the information of early warning names, early warning grades, a value list triggering early warning records, associated systems (corrosion, differential settlement, vibration, stress strain and the like), remarks and the like.
Diagnostic library: the matched diagnosis information used for the custom algorithm output comprises correlation factor analysis and statistical factor analysis triggered by early warning. A database table for storing diagnosis information is needed to be established and used for storing meta information data of early warning diagnosis, and the meta information data specifically comprise diagnosis names, diagnosis association factor analysis, diagnosis statistical factor analysis, remarks and the like.
Algorithm library: the method is used for customizing an algorithm, and a specific algorithm file is imported to be related with information configurations defined in a variable library, an early warning library and a diagnosis library. The flexible algorithm configuration options are provided, so that the input variables and the output early warning and diagnosis information of the algorithm can be configured according to specific requirements, and intelligent data analysis and early warning judgment are realized. An algorithm table for storing configuration information of a custom algorithm is designed and is mainly used for storing related configuration of the algorithm, and the algorithm table comprises information such as model numbers, model names, associated systems, input variable lists, output early warning lists, output diagnosis lists, algorithm files, enabling and describing.
Driving engine: the method mainly aims at driving an engine executed by the whole early warning framework, and is required to realize real-time scanning and grabbing of algorithm configuration from a database, acquire an algorithm meeting a trigger condition and analyze a variable list in the algorithm configuration to calculate and acquire an actual data value. And then creating a specific algorithm execution task, including an algorithm to be executed and actual variable list data, and pushing the algorithm execution task to a message queue to wait for an execution engine to acquire and execute. The driving engine also needs to realize real-time monitoring of the result data returned by the message queue after the execution engine executes the algorithm task, and analyze whether the algorithm has a new triggered early warning or needs a recovered early warning according to the data. And if the new trigger early warning exists, early warning information is generated according to an early warning list and a diagnosis list configured in the algorithm, and the early warning event is issued. If the pre-warning to be recovered exists (the recovery indicates that the pre-warning is in a triggering state before the recovery, the current result is not triggered any more), the pre-warning triggered currently is found out, and the pre-warning state is updated; the design execution engine is mainly responsible for monitoring and pulling algorithm execution tasks in the message queue in real time, analyzing variables in the algorithm tasks and executing corresponding Python algorithm once the tasks enter the message queue, acquiring an execution result and packaging the execution result back into the message queue.
An execution engine: analyzing the variable and executing the appointed algorithm by monitoring the algorithm task in the message queue, and returning the executed result to the message queue.
While the invention has been described in detail in the foregoing general description and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (8)
1. The utility model provides a steel construction monitoring early warning platform of marine wind-powered electricity generation booster station which characterized in that for the safe state of real-time supervision marine booster station steel construction to in time make the early warning to the health security state aassessment, this monitoring early warning platform includes:
the data communication acquisition module is used for acquiring various sensor data of the offshore booster station, which are installed on the detection steel structure, in real time through Modbus TCP and MQTT communication protocols, and carrying out custom configuration and management on the acquisition device;
the data analysis module is used for carrying out noise reduction, calibration, characteristic value extraction, health assessment and prediction and statistical analysis on the acquired data;
the visual display module is used for displaying monitoring data and analysis results in an intuitive and interactive mode, providing real-time and historical data inquiry, data trend analysis, thermodynamic diagram display, multidimensional data comparison, real-time early warning display, report generation and export and visual self-defining configuration, providing a 3D BIM model of the whole booster station, integrating the monitoring data, early warning information and sensor information into the model, and being capable of displaying the health state of the steel structure of each module more intuitively through a built-in health evaluation algorithm by colors, and supporting free rotation, scaling and roaming operation of equipment at a page and a mobile terminal;
the mobile terminal display module is used for providing display of the mobile terminal for the visual display module so as to access and monitor data in real time more conveniently, and early warning real-time notification and binding, report preview and quick sharing functions are added on the basis of the visual display module;
the data management module supports backup, recovery and archiving of data and reserves a data analysis and mining interface;
the early warning module comprises abnormal data monitoring, intelligent early warning model management, real-time early warning notification and real-time monitoring diagnosis, and carries out health assessment on the steel structure in real time;
an intelligent configurable monitoring and early warning software architecture provides architecture support for a flexible early warning module;
the third party interface module is an independent module provided by the platform for integrating locks with other systems, and comprises data inquiry, real-time early warning notification, data analysis, export report, interface security authentication and technical support.
2. The steel structure monitoring and early warning platform of the offshore wind power booster station according to claim 1, wherein various sensors mounted on the steel structure comprise a vibration sensor, a corrosion potential sensor, an inclination sensor, a stress strain sensor and an uneven settlement sensor.
3. The steel structure monitoring and early warning platform of the offshore wind power booster station according to claim 1, wherein the data analysis function achieves noise interference removal and calibration of real-time data, extraction of useful structural features from the real-time data, real-time evaluation and analysis of health states of the steel structure, statistical analysis of collected data, and generation of detailed reports and visual charts.
4. The steel structure monitoring and early warning platform of the offshore wind power booster station according to claim 1, wherein a visual display function provides an intuitive and scientific interface for displaying real-time monitoring data and interactive operation logic; providing a real-time data curve, a structural deformation image and a visual display of temperature distribution; and supporting user-defined chart configuration and display options.
5. The steel structure monitoring and early warning platform of the offshore wind power booster station according to claim 1, wherein the early warning module monitors and analyzes real-time data through a configuration algorithm model and a built-in algorithm model, detects whether the steel structure and the sensor are abnormal, and when the structural data are close to or reach an early warning condition and an abnormal critical state, the system automatically generates abnormal early warning information; when abnormal early warning information occurs, an alarm is immediately given out on an interface, and early warning information is sent to a subscribed user; the platform records historical abnormal early warning information and response processing measures; the platform will provide some suggested automation while issuing the alert notification.
6. The steel structure monitoring and early warning platform of the offshore wind power booster station according to claim 5, wherein the notification mode for sending the early warning information to the subscribed users comprises short messages, APP and mails.
7. The steel structure monitoring and early warning platform of the offshore wind power booster station according to claim 1, wherein the data management module is provided with a data backup and recovery mechanism; support data archiving and long-term storage; reserving a data analysis and mining interface.
8. The steel structure monitoring and early warning platform of the offshore wind power booster station according to claim 1, wherein the intelligent configurable monitoring and early warning software architecture provides more flexible early warning configuration for the early warning module through the implementation of a variable library, an early warning library, a diagnosis library, an algorithm library, a driving engine and an execution engine.
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| CN117612358A (en) * | 2024-01-23 | 2024-02-27 | 山东恒迈信息科技有限公司 | Monitoring and early warning management method based on data analysis |
| CN117612358B (en) * | 2024-01-23 | 2024-04-16 | 山东恒迈信息科技有限公司 | Monitoring and early warning management method based on data analysis |
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