Operation and maintenance system of offshore wind power equipment

Abstract

The invention discloses an operation and maintenance system for offshore wind power equipment, and relates to the technical field of offshore wind power, and the system comprises an equipment monitoring module which is used for monitoring the operation state of the offshore wind power equipment in real time, and collecting the operation state parameters of the equipment; the data processing module is used for processing and storing the running state data and carrying out data cleaning; the fault early warning module is used for analyzing the health condition of the equipment through a diagnosis algorithm and identifying potential faults in real time for fault prediction; the intervention module is used for notifying operation and maintenance personnel to remotely control the equipment to repair or adjust when the early warning signal is received; and the data visualization module is used for carrying out visual display. Through the fault early warning system combining the rule-based diagnosis algorithm and the machine learning algorithm, efficient fault prediction and early warning are realized, potential faults are found in advance, and an early warning signal is sent out, so that the dependence on manual inspection and field maintenance is reduced, the downtime and maintenance cost of equipment are reduced, and the overall operation and maintenance cost is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power, and particularly to an operation and maintenance system for offshore wind power equipment. Background Art

[0002] With the continuous increase in the global demand for renewable energy, offshore wind power, as an important source of clean energy, is gradually becoming an important part of the global energy structure adjustment. Offshore wind power equipment has high power generation capacity, can effectively address the challenges of climate change, and promote energy transformation. However, the operating environment of offshore wind power equipment is special. The equipment faces challenges from natural environments such as wind speed, waves, and salt spray, and is usually located in waters far from land, bringing great difficulties to its daily operation and maintenance.

[0003] The traditional operation and maintenance mode of offshore wind power equipment relies on manual inspections and regular maintenance. This method is not only inefficient but also costly. Especially when the equipment fails, the response time of manual intervention is relatively long, which may lead to long-term equipment downtime and seriously affect the power generation efficiency and economic benefits of the wind farm.

[0004] In order to improve the operation and maintenance efficiency of offshore wind power equipment, reduce the failure rate, and lower the operating cost, more and more research and development efforts are dedicated to using modern information technologies, including the Internet of Things, artificial intelligence, data analysis, etc., to establish intelligent operation and maintenance systems. These systems can monitor the equipment status in real time, predict equipment failures based on data analysis and algorithms, and provide remote control and adjustment functions, thereby reducing manual intervention and improving the operation stability and maintenance efficiency of the equipment.

[0005] Although the existing operation and maintenance systems have made certain progress in monitoring and fault warning, most systems still have the following problems: limited data processing capacity, unable to effectively process large-scale equipment operation data; inaccurate fault diagnosis algorithms, insufficient timeliness and accuracy of warning information; low system integration, making it difficult to effectively interface with external meteorological data, energy management systems, etc. Summary of the Invention

[0006] Based on the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide an operation and maintenance system for offshore wind power equipment to solve the above technical problems.

[0007] To achieve the above purpose, the present invention provides the following technical solution: An operation and maintenance system for offshore wind power equipment, comprising:

[0008] An equipment monitoring module, used for monitoring the operation status of offshore wind power equipment in real time and collecting the operation status parameters of the offshore wind power equipment;

[0009] A data processing module, which is used to process and store the operation status data from the device monitoring module and perform data cleaning;

[0010] A fault warning module, which is used to analyze the health status of the offshore wind power equipment through a diagnostic algorithm based on the operation status parameters, identify potential faults in real time for fault prediction, and send out warning signals;

[0011] An intervention module, which is used to notify the operation and maintenance personnel to remotely control the offshore wind power equipment for repair or adjustment when a warning signal is received;

[0012] A data visualization module, which is used to provide dashboards and data analysis reports and visually display the operation status parameters.

[0013] The present invention is further configured such that the device monitoring and diagnosis module collects the status data of the wind power equipment in real time through a sensor network, and the operation status parameters include wind speed, power generation power, rotational speed, current, and voltage.

[0014] The present invention is further configured such that data cleaning includes: outlier detection, which is used to identify and process outliers caused by sensor failures, data loss, or environmental interference; data completion, which is used to interpolate missing parameters, including mean interpolation and / or interpolation methods; normalization or standardization, which is used to ensure that the numerical values of status data with different dimensions are on the same scale.

[0015] The present invention is further configured such that, based on the operation status parameters, analyzing the health status of the offshore wind power equipment through a diagnostic algorithm, identifying potential faults in real time for fault prediction, and sending out warning signals, including:

[0016] Moving a window through the entire time series according to a preset time window size, where each time a time step is moved;

[0017] Calculating the operation status characteristics of the operation status parameters within each window, and the operation status characteristics include mean characteristics, standard deviation characteristics, maximum value characteristics, and minimum value characteristics;

[0018] Performing Fourier transform on the operation status parameters of the offshore wind power equipment, converting the time-domain signal into a frequency-domain signal, and extracting frequency component characteristics, spectral density characteristics, and main frequency component characteristics according to the frequency-domain signal;

[0019] Combining the mean characteristics, standard deviation characteristics, maximum value characteristics, minimum value characteristics, frequency component characteristics, spectral density characteristics, and main frequency component characteristics of the operation status parameters to generate a feature vector;

[0020] Setting the feature vector as the input, inputting it into the diagnostic algorithm to analyze the health status of the offshore wind power equipment, identifying potential faults in real time for fault prediction, and sending out warning signals.

[0021] The present invention is further configured such that the diagnostic algorithm includes a rule-based diagnostic algorithm and a machine learning algorithm.

[0022] The present invention is further configured such that the rule-based diagnostic algorithm identifies faults by setting thresholds, and limits the thresholds for one or more of the operating state parameters and / or feature vectors. When the defined conditions are not met, a warning signal is issued.

[0023] The present invention is further configured such that the machine learning algorithm classifies the health status of the equipment using a trained fault prediction model;

[0024] The training logic of the fault prediction model is as follows: obtain historical operating state parameters, generate feature vectors, label each data point with a status label, and generate a data set. The status labels include normal status or fault status;

[0025] Divide the data set into a training set and a validation set, select a machine learning algorithm, and construct a fault prediction model. The machine learning algorithms include decision tree, random forest, and support vector machine;

[0026] Input the feature vectors of the training set into the fault prediction model, calculate the error between the prediction result and the actual label, and define a loss function to measure the prediction error of the model;

[0027] Train until the maximum number of iterations or the loss function converges. Validate the fault prediction model using the validation set. After successful validation, complete the training of the fault prediction model.

[0028] The present invention is further configured to control the offshore wind power equipment for repair or adjustment, including adjusting the blade angle of the wind turbine and starting or stopping the offshore wind power equipment.

[0029] The present invention is further configured to further include an integration and interface module for integrating with external systems through open APIs, supporting the access of meteorological data and energy management information, and assisting in operation and decision-making.

[0030] The present invention provides an operation and maintenance system for offshore wind power equipment, including an equipment monitoring module for real-time monitoring of the operating status of offshore wind power equipment and collecting the operating status parameters of the offshore wind power equipment; a data processing module for processing and storing the operating status data from the equipment monitoring module and performing data cleaning; a fault warning module for analyzing the health status of the offshore wind power equipment based on the operating status parameters through a diagnostic algorithm, identifying potential faults in real-time for fault prediction, and sending out warning signals; an intervention module for notifying the operation and maintenance personnel to remotely control the offshore wind power equipment for repair or adjustment when a warning signal is received; and a data visualization module for providing dashboards and data analysis reports to visually display the operating status parameters. The beneficial effects generated include:

[0031] 1. Efficient fault prediction and warning: By using the fault warning module and combining a rule-based diagnostic algorithm with a machine learning algorithm, the health status of the equipment is analyzed in real-time. By extracting the feature vectors of the equipment operating status and predicting potential equipment faults, abnormal conditions during equipment operation can be detected in advance, and warning signals can be sent out, thus effectively avoiding the impact of sudden faults on the equipment and reducing equipment downtime and maintenance costs;

[0032] 2. Reducing operation and maintenance costs: Through intelligent monitoring, prediction, and remote control, the present invention effectively reduces the dependence on manual inspections and on-site maintenance, reducing labor costs. At the same time, the system can predict faults in advance and provide warnings, avoiding large-scale equipment failures, reducing repair time and equipment downtime losses, and ultimately reducing the overall operation and maintenance costs.

[0033] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically illustrates the specific embodiments of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings:

[0035] Figure 1 FIG. shows a schematic structural diagram of an operation and maintenance system for an offshore wind power equipment according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than for limiting the protection scope of the present invention.

[0037] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0038] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0039] An operation and maintenance system for an offshore wind power device, as Figure 1 shown, includes:

[0040] The device monitoring module is used to monitor the operating status of the offshore wind power device in real time and collect the operating status parameters of the offshore wind power device;

[0041] The data processing module is used to process and store the operating status data from the device monitoring module and perform data cleaning;

[0042] The fault warning module is used to analyze the health status of the offshore wind power device based on the operating status parameters through a diagnostic algorithm, identify potential faults in real time for fault prediction, and issue a warning signal;

[0043] The intervention module is used to notify the operation and maintenance personnel to remotely control the offshore wind power device for repair or adjustment when a warning signal is received;

[0044] The data visualization module is used to provide a dashboard and a data analysis report, and visually display the operating status parameters.

[0045] The present invention is further configured such that the device monitoring and diagnosis module collects the status data of the wind power equipment in real time through a sensor network, and the operating status parameters include wind speed, generated power, rotational speed, current, and voltage. Specifically, the wind speed refers to the wind speed magnitude in the area where the wind power equipment is located. The wind speed directly affects the power generation capacity of the wind turbine. Too low or too high wind speed will affect the operating efficiency and safety of the equipment. The generated power refers to the ability of the wind power equipment to convert wind energy into electrical energy, reflecting the working state and power generation efficiency of the wind turbine. The rotational speed refers to the rotational speed of the rotor (wind turbine blade) of the wind turbine. The rotational speed is closely related to the wind speed and generated power. Too high or too low rotational speed may indicate that there is a fault or abnormal operation in the wind power equipment. The current refers to the amount of current transmitted in the power system. Fluctuations or abnormalities in the current are usually precursors to equipment failures. The voltage refers to the voltage value output by the equipment. Changes in the voltage can reflect the electrical state of the wind power equipment. Too high or too low voltage may cause equipment damage or unstable operation.

[0046] The present invention is further configured to perform data cleaning, including: outlier detection for identifying and processing outliers caused by sensor failures, data loss, or environmental interference; data completion for interpolating missing parameters, including mean interpolation and / or interpolation methods; and normalization or standardization for ensuring that the numerical values of status data with different dimensions are on the same scale.

[0047] The present invention is further configured to analyze the health status of the offshore wind power equipment based on the operating status parameters, identify potential faults in real time for fault prediction, and issue warning signals, including:

[0048] Moving a window through the entire time series according to a preset time window size, where each time it moves one time step; specifically, the system first performs window traversal on the collected operating status data according to the preset time window size. The data within each window represents the status of the equipment within a period of time, and the window moves one fixed time step each time. In this way, the operating status of the equipment at different time periods can be gradually analyzed, and potential abnormalities or fault signs can be identified.

[0049] Calculating the operating status characteristics of the operating status parameters within each window, where the operating status characteristics include mean characteristics, standard deviation characteristics, maximum value characteristics, and minimum value characteristics; specifically, within each time window, the system extracts a set of operating status characteristics. These characteristics include: mean characteristics, which is the arithmetic mean of all data within the window, reflecting the central tendency of the equipment operation; standard deviation characteristics, which is an index reflecting data volatility. The larger the standard deviation, the higher the instability of the equipment operation state; maximum value characteristics, which is the maximum value of the data within the window, usually used to identify extreme fluctuations or emergencies; minimum value characteristics, which is the minimum value of the data within the window, helping to identify the low valley state during operation.

[0050] Perform Fourier transform on the operation state parameters of the offshore wind power equipment to convert the time-domain signal into a frequency-domain signal. According to the frequency-domain signal, extract frequency component features, spectral density features, and main frequency component features. Specifically, the system performs Fourier transform on the operation state parameters to convert the time-domain signal into a frequency-domain signal. The time-domain signal represents the changes of the equipment over time, while the frequency-domain signal reveals the frequency components behind these changes. Through frequency-domain analysis, potential fault modes or periodic changes can be captured.

[0051] From the frequency-domain signal, the system extracts the following frequency features: frequency component features, which reflect the distribution of each frequency in the signal and help identify periodic changes; spectral density features, which represent the distribution of signal energy at different frequencies and can help identify the working mode and abnormal fluctuations of the equipment; main frequency component features, which represent the most dominant frequency component in the signal and can help determine the working frequency or abnormal frequency of the equipment.

[0052] Merge the mean feature, standard deviation feature, maximum value feature, minimum value feature, frequency component feature, spectral density feature, and main frequency component feature of the operation state parameters to generate a feature vector. Specifically, merge the above calculated operation state features (mean feature, standard deviation feature, maximum value feature, minimum value feature, frequency component feature, spectral density feature, and main frequency component feature) to form a complete feature vector. This feature vector contains comprehensive information about the health status of the equipment at different time periods.

[0053] Set the feature vector as the input, and input the diagnostic algorithm to analyze the health status of the offshore wind power equipment, identify potential faults in real time for fault prediction, and send out warning signals.

[0054] The present invention is further configured such that the diagnostic algorithm includes a rule-based diagnostic algorithm and a machine learning algorithm. The present invention is further configured such that the rule-based diagnostic algorithm identifies faults by setting thresholds. By setting thresholds for one or more of the operation state parameters and / or the feature vector, when the defined conditions are not met, a warning signal is sent out. The present invention is further configured such that the machine learning algorithm classifies the health status of the equipment using a trained fault prediction model.

[0055] The training logic of the fault prediction model is as follows: Obtain historical operation state parameters, generate feature vectors, label each data point with a state label, and generate a data set. The state label includes a normal state or a fault state.

[0056] Divide the data set into a training set and a validation set, select a machine learning algorithm, and construct a fault prediction model. The machine learning algorithms include decision tree, random forest, and support vector machine.

[0057] Input the feature vectors of the training set into the fault prediction model, calculate the error between the prediction result and the actual label, and define a loss function to measure the prediction error of the model.

[0058] Train until the maximum number of iterations or the loss function converges, and verify the fault prediction model through the validation set. After passing the verification, complete the training of the fault prediction model. Specifically, using machine learning algorithms for training can discover potential patterns in the device health status through a large amount of historical data, thereby accurately predicting whether the device is in a fault state. Compared with traditional rule-based diagnostic methods, machine learning models have stronger adaptability and accuracy; through real-time prediction and remote control functions, the present invention can effectively reduce manual intervention, optimize the device operation and maintenance efficiency, and reduce labor costs. At the same time, it can effectively prevent the occurrence of large-scale faults and avoid equipment downtime and high maintenance costs.

[0059] The present invention is further configured to control the offshore wind power equipment to perform repairs or adjustments, including adjusting the blade angle of the wind turbine and starting or stopping the offshore wind power equipment. Specifically, adjusting the blade angle of the wind turbine: The adjustment of the blade angle of the wind turbine directly affects the working efficiency and wind energy conversion efficiency of the wind turbine. When the operating state of the equipment is abnormal, the performance of the wind turbine can be improved by adjusting the blade angle, or further damage to the equipment can be prevented by reducing the load; starting or stopping the equipment: According to the result of the fault prediction, the system can remotely start or stop the equipment. If it is predicted that the equipment will fail and may cause serious damage, the system can choose to immediately stop the operation of the equipment to avoid further expansion of the fault. On the contrary, if the equipment is in an inefficient or non-optimal working state, the system can also adjust the parameters to make the equipment return to the optimal working state.

[0060] The present invention is further configured to further include an integration and interface module for integrating with external systems through an open API, supporting the access of meteorological data and energy management information, and assisting in operation and decision-making. Specifically, integrate with external systems through an open API. This design enables the operation and maintenance system of the offshore wind power equipment to effectively dock and share data with external meteorological data systems, energy management systems, etc., in order to optimize the operation and maintenance decisions of the wind power equipment.

[0061] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0062] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. The specific meaning can be understood by referring to the context.

[0063] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0064] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0065] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0066] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0067] In several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in an electrical, mechanical, or other form.

[0068] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0069] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0070] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0071] As described above, the foregoing are only specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. An operation and maintenance system for an offshore wind power device, characterized in that, Including: An equipment monitoring module, which is used to monitor the operating status of the offshore wind power equipment in real time and collect the operating status parameters of the offshore wind power equipment; A data processing module, which is used to process and store the operating status data from the equipment monitoring module and perform data cleaning; A fault warning module, which is used to analyze the health status of the offshore wind power equipment based on the operating status parameters, identify potential faults in real time for fault prediction, and issue a warning signal through a diagnostic algorithm; An intervention module, which is used to notify the operation and maintenance personnel to remotely control the offshore wind power equipment for repair or adjustment when a warning signal is received; A data visualization module, which is used to provide a dashboard and a data analysis report, and visually display the operating status parameters.

2. The operation and maintenance system of an offshore wind power equipment according to claim 1, characterized in that, The equipment monitoring and diagnosis module collects the status data of the wind power equipment in real time through a sensor network, and the operating status parameters include wind speed, power generation power, rotation speed, current, and voltage.

3. The operation and maintenance system for an offshore wind power equipment according to claim 2, characterized in that, Performing data cleaning, including: outlier detection, which is used to identify and process outliers caused by sensor failures, data loss, or environmental interference; data completion, which is used to interpolate missing parameters, including mean interpolation and / or interpolation methods; normalization or standardization, which is used to ensure that the numerical values of status data with different dimensions are on the same scale.

4. The operation and maintenance system of an offshore wind power equipment according to claim 3, characterized in that, Based on the operating status parameters, analyzing the health status of the offshore wind power equipment through a diagnostic algorithm, identifying potential faults in real time for fault prediction, and issuing a warning signal, including: Moving a window through the entire time series according to a preset time window size, where each time it moves one time step; Calculating the operating status characteristics of the operating status parameters within each window, and the operating status characteristics include mean characteristics, standard deviation characteristics, maximum characteristics, and minimum characteristics; Performing a Fourier transform on the operating status parameters of the offshore wind power equipment to convert the time-domain signal into a frequency-domain signal, and extracting frequency component characteristics, spectral density characteristics, and main frequency component characteristics according to the frequency-domain signal; Combining the mean characteristics, standard deviation characteristics, maximum characteristics, minimum characteristics, frequency component characteristics, spectral density characteristics, and main frequency component characteristics of the operating status parameters to generate a feature vector; Setting the feature vector as the input, inputting it into the diagnostic algorithm to analyze the health status of the offshore wind power equipment, identifying potential faults in real time for fault prediction, and issuing a warning signal.

5. The operation and maintenance system of an offshore wind power device according to claim 4, characterized in that, The diagnostic algorithm includes a rule-based diagnostic algorithm and a machine learning algorithm.

6. The operation and maintenance system for an offshore wind power device according to claim 5, characterized in that, The rule-based diagnostic algorithm identifies faults by setting thresholds, and limits one or more of the operating status parameters and / or feature vectors. When the limit conditions are not met, a warning signal is issued.

7. The operation and maintenance system for an offshore wind power equipment according to claim 5, characterized in that, The machine learning algorithm classifies the health status of the equipment using a trained fault prediction model; The training logic of the fault prediction model is: obtaining historical operating status parameters, generating feature vectors, labeling each data point with a status label, and generating a data set, where the status label includes a normal state or a fault state; Dividing the data set into a training set and a validation set, selecting a machine learning algorithm, and constructing a fault prediction model. The machine learning algorithms include decision trees, random forests, and support vector machines; Input the feature vectors of the training set into the fault prediction model, calculate the error between the prediction result and the actual label, and define a loss function to measure the prediction error of the model. Train until the maximum number of iterations or the loss function converges, and verify the fault prediction model through the validation set. After passing the verification, complete the training of the fault prediction model.

8. The operation and maintenance system of an offshore wind power equipment according to claim 1, characterized in that, Control the offshore wind power equipment to perform repairs or adjustments, including adjusting the blade angle of the fan, starting or stopping the offshore wind power equipment.

9. The operation and maintenance system of an offshore wind power equipment according to claim 1, characterized in that, It also includes an integration and interface module for integrating with external systems through open APIs, supporting the access of meteorological data and energy management information, and assisting in operation and decision-making.

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