Offshore wind power monitoring system, method and device based on sea energy mutual fusion standard system

Abstract

The invention aims to provide an offshore wind power monitoring system, method and device based on a sea energy mutual fusion standard system. The offshore wind power monitoring system comprises a data acquisition layer and a digital twinborn service platform, wherein the data acquisition layer is used for acquiring dynamic signals and temperature and strain change data in real time; the digital twinning service platform performs data interaction with the data acquisition layer, and is used for performing simulation optimization on the data acquisition layer through standardized data model definition, high-concurrency communication middleware design and a virtual-real fusion simulation optimization mechanism based on a digital twinning system according to the digital twinning service platform; unified management, real-time interaction and intelligent decision making of multi-source equipment data are achieved, and the problems of data islands and response delay of a traditional centralized architecture are solved.

Description

Technical Field

[0001] The present disclosure relates to the field of heterogeneous device interconnection and system dynamic collaborative optimization, and specifically to an offshore wind power monitoring system, method and device based on the Haineng Interconnection Standard System. Background Art

[0002] An offshore wind farm involves various complex facilities such as wind turbines and booster station equipment (such as turbine systems, converter systems, cable systems, etc.). Due to the lack of a unified structured definition standard for its data model, there are significant obstacles in extracting and correlating the state parameters of key equipment, making the construction of a panoramic model rely on a large number of customized mapping rules, which not only increases the development complexity but also reduces the efficiency of data sharing and system integration. Secondly, in the face of the concurrent access requirements of multiple devices, the traditional communication architecture is difficult to efficiently process real-time signal transmission requests due to the limited channel capacity of the server interface and the lack of a dynamic scheduling mechanism. Especially in a harsh marine environment, the communication link is vulnerable to interference, and the centralized data processing mode is prone to network congestion, resulting in a delay in real-time communication between the unit master control system and onshore equipment, affecting the timeliness of monitoring and the accuracy of decision-making. In addition, when the existing system responds to equipment performance degradation or environmental dynamic changes (such as the coupled action of wind and waves, salt spray corrosion), it lacks the ability to update model parameters based on real-time data, making it difficult to achieve dynamic matching of equipment status and operation and maintenance strategies, resulting in a lag in operation and maintenance response and unable to meet the requirements of intelligent closed-loop management.

[0003] Therefore, a detection system that can achieve seamless integration and intelligent operation and maintenance of multi-source equipment in an offshore wind farm is particularly urgently needed. Summary of the Invention

[0004] The purpose of the present disclosure is to provide an offshore wind power monitoring system, method and device based on the Haineng Interconnection Standard System to solve at least one technical problem in the prior art.

[0005] The technical solution of the present disclosure is as follows:

[0006] An offshore wind power monitoring system based on the Haineng Interconnection Standard System includes:

[0007] A data acquisition layer for collecting real-time dynamic signals and temperature and strain change data;

[0008] A digital twin service platform that conducts data interaction with the data acquisition layer and is used to, based on the digital twin system, through standardized data model definition, high-concurrency communication middleware design, and virtual-real fusion simulation optimization mechanism, achieve unified management, real-time interaction, and intelligent decision-making of multi-source device data, breaking through the data island and response delay problems of the traditional centralized architecture.

[0009] The digital twin business platform includes:

[0010] A production operation supervision module, which is used to detect equipment anomalies in real time. The monitoring module combines current data and temperature changes to evaluate the risk of insulation aging.

[0011] An operation and maintenance data module, which conducts data interaction with the data acquisition layer and is used to store the dynamic signals, as well as temperature and strain change data.

[0012] An operation and maintenance evaluation module, which conducts data interaction with the operation and maintenance data module and is used to evaluate the health index of any equipment based on an adaptive decision-making algorithm, and at the same time quantitatively score the operation and maintenance effect.

[0013] An asset management module, which conducts data interaction with the operation and maintenance evaluation module and is used to track the full life cycle status of the equipment, record spare part replacement records in combination with blockchain technology, and prevent data tampering.

[0014] The digital twin system includes:

[0015] A data modeling layer, which conducts data interaction with the data acquisition layer and is used to perform object-oriented digital modeling on offshore wind farm equipment.

[0016] A data transmission layer, which conducts data interaction with the data modeling layer and is used to collect the measured data of any equipment point by using the OPC UA protocol and achieve the efficient transmission of structured data through the MQTT protocol.

[0017] A communication service layer, which communicates with the data acquisition layer and is used to generate independent object references for each device when at least two devices initiate service requests simultaneously, so as to achieve parallel response.

[0018] The object-oriented digital modeling of offshore wind farm equipment includes:

[0019] Abstracting the key equipment of the offshore wind farm into a wind farm model and a booster station model;

[0020] Defining attributes, measurement points and association relationships for any type of equipment model through object-oriented technology;

[0021] Eliminating the communication barriers of heterogeneous equipment through a built-in protocol conversion module to achieve unified data formats.

[0022] The collection of the measured data of any equipment point by using the OPC UA protocol and the efficient transmission of structured data through the MQTT protocol include:

[0023] Realizing the dynamic binding of the equipment model and real-time data through a standardized data bus, specifically:

[0024] The OPC UA protocol is used to collect equipment measurement point data, and the MQTT protocol is used to achieve efficient transmission of structured data;

[0025] A data verification module is built into the data bus to filter and issue alarms for abnormal values, ensure data quality, achieve unified access and sharing of multi-subsystem data, and solve the data island problem in traditional systems.

[0026] The generating of an independent object reference for each device to achieve parallel response includes:

[0027] Use dynamic request coordinator mechanism to build high-concurrency communication services;

[0028] When at least two devices initiate service requests simultaneously, the DRC module receives the requests in non-blocking I / O mode and caches them in the priority queue;

[0029] The server side dynamically allocates service threads and generates independent object references for any device to achieve parallel response.

[0030] An offshore wind power monitoring method, comprising:

[0031] Abstract the equipment used in offshore wind power as an object model, define equipment attributes, measurement point parameters and logical relationships;

[0032] Based on the object-oriented modeling technology of SMEIS, the equipment model library is constructed and a standardized description file in XML format is generated;

[0033] During data transmission, real-time data is mapped to the measurement point model through the OPC UA protocol, and data bus is used to achieve data sharing of any subsystem, solve the problem of incompatible data formats of heterogeneous devices, and support cross-platform data fusion;

[0034] The digital twin platform is used to receive equipment data in real time, update the corresponding virtual model status in the equipment model library, and evaluate the health index of the corresponding equipment based on ADA.

[0035] The offshore wind power monitoring method comprises:

[0036] When the health index of any device is abnormal, the digital twin platform automatically triggers near-physical simulation, generates an optimization plan, and sends the optimization plan to the on-site controller to execute it.

[0037] An electronic device, comprising:

[0038] Storage medium for storing a computer program,

[0039] A processing unit, which exchanges data with the storage medium, is configured to execute the computer program through the processing unit during offshore wind power monitoring to perform the steps of the offshore wind power monitoring method as described above.

[0040] A computer-readable storage medium:

[0041] The computer-readable storage medium stores a computer program;

[0042] When the computer program runs, it executes the steps of the offshore wind power monitoring method as described above.

[0043] The beneficial effects of the present disclosure at least include:

[0044] The system described in the present disclosure performs unified digital modeling on key equipment of offshore wind power, analyzes the equipment-measurement point association and communication requirements, proposes a three-layer communication architecture based on the SMEIS standard and a method for DRC concurrency optimization, and realizes interconnection and real-time dynamic communication of heterogeneous equipment. By abstracting the digital models of wind farms and booster stations, a panoramic core data system is constructed; a DRC cache mechanism is designed to expand the server interface capacity and support concurrent request responses from multiple devices; combined with industrial Internet and digital twin technologies, real-time state data such as fan movement and tower load are collected, and big data and AI technologies are integrated to optimize operation and maintenance decisions and fault handling. Finally, an intelligent operation and maintenance digital twin platform is established; by constructing the digital twin platform, deep integration of multi-source data and panoramic monitoring can be achieved, thereby enabling real-time collection, centralized storage, and correlation analysis of the equipment health index (DHI) and environmental variables. It can also realize driving intelligent closed-loop decision-making and dynamic optimization, online evaluation of equipment health using digital twin technology, and improve the operation and maintenance response ability in harsh environments. Through standardized modeling, high-concurrency communication, and digital twin closed-loop control, the present disclosure realizes seamless integration and intelligent operation and maintenance of multi-source equipment in offshore wind farms, and has significant advantages in protocol compatibility, real-time performance, optimization efficiency, etc., providing a highly reliable and scalable solution for the industry. Description of the Drawings

[0045] Figure 1 It is a schematic diagram of the interconnection middleware of the monitoring system based on SMEIS;

[0046] Figure 2 It is a schematic diagram of the structure of the offshore wind power equipment digital twin platform for intelligent operation and maintenance. Detailed Embodiments

[0047] The present disclosure will be further described below with reference to the accompanying drawings.

[0048] Term Explanation:

[0049] Digital Twin Platform: The digital twin platform is a comprehensive intelligent system built based on cutting-edge technologies such as the Internet of Things, artificial intelligence, and big data analysis. By integrating the full-life cycle data of physical entities, fusing multidisciplinary dynamic models and probabilistic algorithms, it constructs a high-precision mirror body with spatio-temporal consistency in the virtual space. Through a real-time data-driven two-way interaction mechanism, the platform realizes core functions such as physical entity status monitoring, performance prediction, fault diagnosis, and optimization control, and supports the full-element digital mapping and dynamic evolution simulation of complex systems.

[0050] Dynamic Request Coordinator (DRC): The Dynamic Request Coordinator (DRC) is a communication optimization module designed based on a lightweight thread pool and a non-blocking scheduling strategy, specifically for solving response latency and resource competition problems in multi-device high-concurrency real-time request scenarios. Its core function is to dynamically allocate service resources through a priority queue, and combine redundant verification and a double-buffer mechanism to achieve intelligent scheduling and parallel response to a large number of device requests.

[0051] Adaptive Decision Algorithm (ADA): The Adaptive Decision Algorithm (ADA) is a dynamic optimization algorithm driven by multi-source real-time data. By integrating device status monitoring data, environmental variables, and historical operation and maintenance records, it generates adaptive closed-loop control instructions. Its core functions include real-time data feature extraction, multi-objective optimization model iterative update, and dynamic policy issuance and execution.

[0052] Device Health Index (DHI): The Device Health Index (DHI) is a comprehensive index for quantitatively evaluating the operating status of offshore wind power equipment. By integrating multi-dimensional monitoring data and simulation prediction results, it dynamically reflects the device health, fault risk, and maintenance priority.

[0053] Standard for Marine Energy Integration System (SMEIS): The Standard for Marine Energy Integration System (SMEIS) is a comprehensive standard system specifically constructed for the offshore wind power field, aiming to achieve deep integration and efficient collaboration among all links of the offshore wind power full industry chain. With the core goal of promoting the sustainable development and utilization of offshore wind energy, it covers a series of specifications and requirements from hardware facilities such as wind turbines, booster station equipment, and cable systems to software levels such as data communication, operation and maintenance management, and safety assurance.

[0054] In view of the problems existing in the prior art, the present disclosure solves the following key problems through the design of a standardized middleware based on SMEIS: (1) The semantic and interoperability problems of heterogeneous devices, and the interoperability obstacle problem: In the existing offshore wind farms, the data models of wind turbines, step-up substation equipment, cable systems, etc. are not unified, resulting in complex system integration and low data sharing efficiency. The present disclosure constructs an object-oriented unified data modeling framework based on the SMEIS standard, abstracts heterogeneous devices into standardized data models, establishes the association mapping rules between the device model and the measurement point model, and realizes the seamless integration and efficient interaction of cross-system data. (2) The bottleneck of dynamic concurrent response: The traditional centralized communication architecture relies on a single server to process a large amount of real-time data. The channel capacity is limited and there is no dynamic scheduling mechanism, resulting in a delay in the response to concurrent requests from multiple devices. The present disclosure establishes a three-layer communication architecture through the dynamic request coordinator (DRC) mechanism, designs a DRC model that supports concurrent access, and expands the channel capacity of the server communication interface. When multiple devices send service requests simultaneously, DRC adopts a service request caching mechanism and dynamically allocates server object references, enabling the server to respond to multiple client requests simultaneously, effectively alleviating the bottleneck problem of the centralized architecture, and improving the data transmission efficiency and system scalability. (3) The defect of static operation and maintenance decision-making: The traditional system lacks the ability to update the dynamic model and cannot support intelligent operation and maintenance. By constructing a digital twin platform, it is possible to achieve deep integration of multi-source data and panoramic monitoring, so as to collect, centrally store, and perform correlation analysis on the real-time data of the device health index (DHI) and environmental variables. It is also possible to realize the drive of intelligent closed-loop decision-making and dynamic optimization, use digital twin technology to conduct online assessment of the device health, and improve the operation and maintenance response ability in harsh environments.

[0055] The core idea of the present disclosure is as follows: Based on the SMEIS standard, an object-oriented unified data modeling framework is constructed. Key parameters of heterogeneous facilities such as wind turbines and booster station equipment in an offshore wind farm, such as operating status and environmental variables, are abstracted into a standardized data model, and the association mapping rules between the equipment model and the measurement point model are established, thereby eliminating data semantic ambiguity and realizing the efficient integration and interaction of cross-system data. Secondly, by introducing a dynamic request coordinator (DRC), a three-layer communication architecture is designed to optimize the channel capacity and concurrent processing ability of the server interface. Specifically, a service request caching and dynamic object reference allocation strategy is adopted to ensure that when multiple devices send requests simultaneously, the server can respond in real time and complete data distribution. Combining redundant communication links and edge data preprocessing technology significantly improves the reliability and real-time performance of data transmission in harsh environments. In addition, the industrial Internet and digital twin technologies are deeply integrated to build a closed-loop of dynamic signal acquisition and model update. Real-time device status and environmental data, such as floating platform motion parameters, tower acceleration, and corrosion monitoring indicators, are obtained through multi-source sensors to drive the synchronous update of the digital twin model, and an adaptive decision-making algorithm (ADA), such as degradation trajectory similarity matching and remaining life prediction, is embedded to realize the online assessment of device health and the generation of predictive maintenance strategies, thereby dynamically optimizing operation and maintenance decisions and forming an adaptive intelligent closed-loop operation and maintenance system, ultimately achieving the efficient monitoring and full-life cycle management of offshore wind power equipment.

[0056] The core principle of the present disclosure is as follows: Based on the digital twin system (DTS), in response to the requirements of interconnection and intelligent operation and maintenance of heterogeneous equipment in an offshore wind farm, a comprehensive system integrating SMEIS standard modeling, dynamic request coordinator (DRC) dynamic scheduling, and digital twin closed-loop optimization is proposed. Through standardized data model definition, high-concurrency communication middleware design, and virtual-real fusion simulation optimization mechanism, the system realizes the unified management, real-time interaction, and intelligent decision-making of multi-source device data, breaking through the data island and response delay problems of traditional centralized architectures.

[0057] Specific Embodiment I:

[0058] The present disclosure provides an embodiment:

[0059] An offshore wind power monitoring system based on the Haineng Interconnection standard system, comprising: a data acquisition layer and a digital twin service platform; wherein, the data acquisition layer is used to collect dynamic signals in real time, as well as temperature and strain change data; the digital twin service platform interacts with the data acquisition layer and is used to, based on the digital twin system, through standardized data model definition, high-concurrency communication middleware design, and virtual-real fusion simulation optimization mechanism, realize the unified management, real-time interaction, and intelligent decision-making of multi-source device data, breaking through the data island and response delay problems of traditional centralized architectures.

[0060] As Figure 1 , the digital twin system includes: a data modeling layer, a data transmission layer, and a communication service layer; specifically, the digital transmission layer is based on the SMEIS standard, performs object-oriented digital modeling on the offshore wind farm equipment, and abstracts the key equipment of the offshore wind farm into a wind farm model and a booster station model. Each type of equipment model defines attributes, measurement points, and association relationships through object-oriented technology. By means of a built-in protocol conversion module, the communication barriers of heterogeneous devices are eliminated, and the data format is unified. The data transmission layer realizes the dynamic binding of the equipment model and real-time data through a standardized data bus. The OPC UA protocol is used to collect the equipment measurement point data, and the MQTT protocol is used to achieve efficient transmission of structured data. At the same time, the data bus is built with a data verification module to filter and alarm outliers, such as over-range temperature data, to ensure data quality. The core function of this layer is to realize the unified access and sharing of multi-subsystem data and solve the data island problem in traditional systems. The communication service layer constructs a high-concurrency communication service by means of a dynamic request coordinator (DRC) mechanism. When multiple devices, such as a fan main control system and a shore-based server, initiate service requests simultaneously, the DRC module receives the requests in a non-blocking I / O mode and caches them in a priority queue. The server side dynamically allocates service threads to generate independent object references for each device to achieve parallel response.

[0061] As Figure 2 , the data acquisition layer integrates multi-source sensors and edge computing nodes to collect dynamic signals such as fan tower acceleration, wave water level, and blade stress in real time. For example, an inclinometer and a weather meter are deployed on a floating fan platform to monitor the fan inclination and wind speed; distributed fiber optic sensors are installed in the cable system to capture temperature and strain changes. The data is preprocessed by the edge node, such as noise reduction and feature extraction, and then uploaded to the cloud. The innovation point of this layer is to adopt the "near physical simulation" technology to synchronize the sensor data with the virtual twin model in real time to ensure the high fidelity of the data.

[0062] The digital twin business platform includes: (1) a production operation supervision module, which detects equipment anomalies in real time, and the monitoring module combines current data and temperature changes to evaluate the risk of insulation aging. (2) An operation and maintenance data module, which stores time-series data (such as wind speed curves), structured data (such as equipment ledgers), and unstructured data (such as inspection images) to provide a data pool for upper-layer analysis. (3) An operation and maintenance evaluation module, which evaluates the device health index (DHI) based on an adaptive decision algorithm (ADA), and at the same time quantitatively scores the operation and maintenance effect. Combined with (4) an asset management module, which tracks the full life cycle status of the equipment, and combines blockchain technology to record spare part replacement records to prevent data tampering.

[0063] The present disclosure realizes the seamless integration and intelligent operation and maintenance of multi-source devices in an offshore wind farm through standardized modeling, high-concurrency communication, and digital twin closed-loop control, and has significant advantages in terms of protocol compatibility, real-time performance, optimization efficiency, etc., providing a highly reliable and scalable solution for the industry. Specific Embodiment 2:

[0065] An offshore wind power monitoring method, based on the monitoring system described in Specific Embodiment 2, includes: abstracting the devices used in offshore wind power into object models, defining device attributes, measuring point parameters, and logical relationships; constructing a device model library based on the object-oriented modeling technology of SMEIS, and generating a standardized description file in XML format; when transmitting data, mapping real-time data to the measuring point model through the OPC UA protocol, and using a data bus to achieve data sharing of any subsystem, solving the problem of incompatible data formats of heterogeneous devices, and supporting cross-platform data fusion; using a digital twin platform to receive device data in real time, updating the status of the corresponding virtual model in the device model library, and evaluating the health index of the corresponding device based on ADA.

[0066] Specifically, first, physical entities such as wind turbine generators and booster station devices are abstracted into object models, and device attributes are defined, including rated power, operating status, measuring point parameters, and logical relationships; among them, the measuring point parameters include vibration frequency, temperature threshold, etc.; the logical relationships include device-measuring point association, fault dependency chain, etc.; after constructing a device model library based on the object-oriented modeling technology of SMEIS, a standardized description file in XML format is generated. At the same time, at the data transmission layer, real-time data is mapped to the measuring point model through the OPC UA protocol, and a data bus is used to achieve data sharing of multiple subsystems, such as a fan monitoring subsystem and a submarine cable monitoring subsystem, so that "model-measuring point" is bidirectionally bound, solving the problem of incompatible data formats of heterogeneous devices, and supporting cross-platform data fusion.

[0067] Such as Figure 2 To achieve intelligent operation and maintenance, this embodiment deeply integrates the middleware with the digital twin platform to form a closed loop of "data acquisition - simulation optimization - instruction issuance"; moreover, the digital twin platform receives device data in real time, updates the status of the virtual model, such as blade stress distribution and tower vibration spectrum, and evaluates the device health index (DHI) based on ADA. When the DHI is abnormal, the platform automatically triggers a near-physical simulation, generates an optimization plan, and issues instructions to the on-site controller through the middleware to execute the optimization plan; using the "simulation - execution iterative optimization" mechanism, the model is recalculated and the instructions are updated every 10 minutes to ensure continuous optimization in a dynamic environment. For example, when the wave load suddenly changes, the system can complete the simulation and issue new parameters within 1 minute, increasing the power generation efficiency by 8%.

[0068] The present disclosure also provides an embodiment:

[0069] An electronic device includes: a storage medium and a processing unit; wherein, the storage medium is used to store a computer program, and the processing unit exchanges data with the storage medium and is used to execute the computer program through the processing unit when performing offshore wind power monitoring to perform the steps of the monitoring method as described in Specific Embodiment 2.

[0070] A computer-readable storage medium stores a computer program therein; when the computer program runs, it executes the steps of the monitoring method as described in Specific Embodiment 2.

[0071] In the present disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. And in the present disclosure, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0072] The above-disclosed are only several specific implementation scenarios of the present disclosure. However, the present disclosure is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present disclosure. The above serial numbers of the present disclosure are only for description and do not represent the superiority or inferiority of the implementation scenarios.

Claims

1. An offshore wind power monitoring system based on the Haineng Interconnection Standard System, characterized in that Including: A data acquisition layer for collecting real-time dynamic signals, as well as temperature and strain change data; A digital twin business platform that interacts with the data acquisition layer and is used to, based on the digital twin system, through standardized data model definition, high-concurrency communication middleware design, and a virtual-real fusion simulation optimization mechanism, achieve unified management, real-time interaction, and intelligent decision-making of multi-source device data, breaking through the data island and response delay problems of traditional centralized architectures.

2. The offshore wind power monitoring system based on the Haineng Interfusion standard system according to claim 1, wherein The digital twin business platform includes: A production operation supervision module for real-time detection of equipment abnormalities, and the monitoring module combines current data and temperature changes to evaluate the risk of insulation aging; An operation and maintenance data module that interacts with the data acquisition layer and is used to store the dynamic signals, as well as temperature and strain change data; An operation and maintenance evaluation module that interacts with the operation and maintenance data module and is used to evaluate the health index of any device based on an adaptive decision-making algorithm and simultaneously quantitatively score the operation and maintenance effect; An asset management module that interacts with the operation and maintenance evaluation module and is used to track the full life cycle status of the equipment, record spare part replacement records in combination with blockchain technology, and prevent data tampering.

3. The offshore wind power monitoring system based on the Haineng Interconnection Standard System according to claim 1, wherein The digital twin system includes: A data modeling layer that interacts with the data acquisition layer and is used to perform object-oriented digital modeling on offshore wind farm equipment; A data transmission layer that interacts with the data modeling layer and is used to collect data of any device measurement point using the OPC UA protocol and achieve efficient transmission of structured data through the MQTT protocol; A communication service layer that communicates with the data acquisition layer and is used to generate independent object references for each device when at least two devices initiate service requests simultaneously to achieve parallel response.

4. The offshore wind power monitoring system based on the Haineng Interconnection Standard System according to claim 3, characterized in that, The object-oriented digital modeling of offshore wind farm equipment includes: Abstracting the key equipment of the offshore wind farm into a wind farm model and a booster station model; Defining attributes, measurement points, and association relationships for any type of equipment model through object-oriented technology; Eliminating the communication barriers of heterogeneous devices through a built-in protocol conversion module to achieve unified data formats.

5. The offshore wind power monitoring system based on the Haineng Interconnection Standard System according to claim 3, characterized in that The collection of data of any device measurement point using the OPC UA protocol and the efficient transmission of structured data through the MQTT protocol include: Realizing the dynamic binding of the equipment model and real-time data through a standardized data bus, specifically: Collecting data of device measurement points using the OPC UA protocol and achieving efficient transmission of structured data through the MQTT protocol; Building a data verification module in the data bus to filter and alarm abnormal values, ensure data quality, achieve unified access and sharing of multi-subsystem data, and solve the data island problem in traditional systems.

6. The offshore wind power monitoring system based on the Haineng Interconnection Standard System according to claim 3, characterized in that, Generating independent object references for each device to achieve parallel response includes: Constructing a high-concurrency communication service using a dynamic request coordinator mechanism; When at least two devices initiate service requests simultaneously, the DRC module receives the requests in non-blocking I / O mode and caches them in a priority queue; The server dynamically allocates service threads, generates independent object references for any device, and realizes parallel response.

7. A method for monitoring offshore wind power of an offshore wind power monitoring system based on the Haineng Interconnection Standard System according to any one of claims 1-6, characterized in that, It includes: Abstract the devices used in offshore wind power into an object model, and define device attributes, measurement point parameters and logical relationships; Based on the object-oriented modeling technology of SMEIS, construct a device model library and generate a standardized description file in XML format; During data transmission, map real-time data to the measurement point model through the OPC UA protocol, and use the data bus to realize data sharing of any subsystem, solve the problem of incompatible data formats of heterogeneous devices, and support cross-platform data fusion; Use the digital twin platform to receive device data in real time, update the status of the corresponding virtual model in the device model library, and evaluate the health index of the corresponding device based on ADA.

8. The offshore wind power monitoring method according to claim 7, characterized in that, It includes: When the health index of any device is abnormal, the digital twin platform automatically triggers a near-physical simulation, generates an optimization plan, and sends the optimization plan to the on-site controller to execute the optimization plan.

9. An electronic device, characterized in that, It includes: A storage medium for storing a computer program, A processing unit that exchanges data with the storage medium, and is used to execute the computer program through the processing unit during offshore wind power monitoring to perform the steps of the offshore wind power monitoring method described in claim 7 or 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program; When the computer program is running, it executes the steps of the offshore wind power monitoring method described in claim 7 or 8.

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