Offshore wind power operation risk analysis system based on deep learning

Abstract

The invention relates to the technical field of offshore wind power operation risk analysis, and discloses an offshore wind power operation risk analysis system based on deep learning, and the system comprises a data collection and preprocessing module which is used for collecting wind power equipment operation state data and environment data of multiple types of sensors, and carrying out the cleaning and preprocessing of the data; the multi-modal data fusion and feature extraction module is used for extracting and fusing multi-modal features from different data sources; and the real-time risk prediction and intelligent decision module is used for analyzing the operation state of the wind power equipment in real time based on a deep learning model, and performing prediction and alarm when a fault trend is detected. By introducing a self-attention mechanism and a Transform architecture, deep fusion analysis is performed on multi-modal data from wind power equipment, and information of different data sources can be weighted and integrated in a unified feature space, so that the system can adaptively focus on important features, and the analysis precision and fault detection capability of the multi-modal data are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power operation risk analysis, and specifically to an offshore wind power operation risk analysis system based on deep learning. Background Art

[0002] Offshore wind power is an important development direction of renewable energy. With the increase in global demand for clean energy, the construction scale of offshore wind farms has increased. Offshore wind turbines face a variety of complex environmental conditions and operational risks during operation, such as high humidity, high salinity, strong winds, lightning, waves and freezing, and other factors lead to frequent failures of wind turbines, high maintenance costs and poor operational stability. Therefore, health status monitoring and fault risk analysis of offshore wind power equipment have become key links to ensure the safe and stable operation of wind farms.

[0003] In the analysis and prediction of offshore wind power operation risks, most of them use traditional methods based on data statistical analysis and machine learning models, such as support vector machines, random forests, etc. for fault detection and risk analysis. However, traditional methods have the following shortcomings:

[0004] In the prior art, such as the "offshore wind power operation risk analysis system based on data analysis" described in patent CN202211283126.5, the influencing factors are collected and the correlation analysis is performed, but multimodal data is not effectively integrated, and complex information from different data sources cannot be processed. Its single-dimensional data processing method causes the system to be unable to fully utilize multi-source information when identifying potential faults of wind power equipment;

[0005] The system of patent CN202211283126.5 relies on traditional data analysis methods during risk analysis, such as fault prediction based on historical operating status, lacks real-time response capabilities and adaptive adjustment mechanisms, and its risk analysis mainly relies on static data, and cannot timely adjust the analysis strategy and prediction results according to changes in real-time data on site;

[0006] In patent CN202211283126.5, the system determines the relationship between risk factors through traditional correlation analysis, fails to reveal the specific impact of each risk factor on the fault prediction results, and lacks a detailed explanation of the internal logic of the model and the prediction results. This approach makes the system exhibit certain "black box" characteristics when providing fault risk analysis and cannot gain the trust of users.

[0007] Therefore, those skilled in the art provide an offshore wind power operation risk analysis system based on deep learning to solve the problems raised in the above background technology. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides an offshore wind power operation risk analysis system based on deep learning, which solves the problems of insufficient multimodal data fusion capability, poor real-time responsiveness and adaptability, and lack of model interpretability in offshore wind power operation, and realizes efficient monitoring of the status of wind power equipment and accurate risk analysis.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A deep learning-based offshore wind power operation risk analysis system, comprising:

[0010] Data collection and preprocessing module, used to collect wind power equipment operating status data and environmental data from multiple sensors, and clean and preprocess the data;

[0011] Multimodal data fusion and feature extraction module, used to extract and fuse multimodal features from different data sources;

[0012] Real-time risk prediction and intelligent decision-making module, which is used to analyze the operating status of wind power equipment in real time based on deep learning models, and to make predictions and alarms when fault trends are detected;

[0013] Model interpretability and intelligent decision support module, used to explain the prediction results and provide intuitive visualization;

[0014] The adaptive optimization and federated learning module is used to optimize model performance through edge computing and federated learning technology, and realize distributed collaborative training and data privacy protection.

[0015] Preferably, the data acquisition and preprocessing module uses a density-based clustering algorithm to detect outliers, and the outlier detection is judged by the following formula:

[0016]

[0017] Among them, ∈ is the threshold parameter, which is used to control the distance between data points.

[0018] Preferably, the multimodal data fusion and feature extraction module includes a convolutional neural network and a long short-term memory network for processing visual data and time series data, and the state update formula of the LSTM is:

[0019] i t =σ(W i ·[h t-1 ,x t ]+b i ),

[0020] f t =σ(W f ·[h t-1 ,x t ]+bf ),

[0021] o t =σ(W o ·[h t-1 ,x t ]+b o ),

[0022] C t =f t ·C t-1 +i t tanh(W c ·[h t-1 ,x t ]+b c ),

[0023] h t =o t tanh(C t ),

[0024] Among them, i t 、f t , o t are input gate, forget gate and output gate;

[0025] C t represents the cell state at time step t;

[0026] h t represents the hidden state at time step t;

[0027] x t represents the input data at time step t;

[0028] h t-1 represents the hidden state of the previous time step t-1;

[0029] W i , W f , W o , W c Represents the weight parameters of the LSTM network;

[0030] b i 、b f 、b o 、b c Represents the bias term of the LSTM network;

[0031] σ represents the logistic sigmoid activation function; tanh represents the hyperbolic tangent activation function.

[0032] Preferably, the adaptive fusion unit uses a self-attention mechanism to perform weighted fusion on the multimodal data, and the self-attention mechanism is calculated by the following formula:

[0033]

[0034] Among them, Q and K are the query matrix and key matrix.

[0035] Preferably, the real-time risk prediction and intelligent decision-making module adopts a deep Q network to optimize the equipment control strategy, and the Q value update formula is:

[0036]

[0037] Among them, s is the current state, a is the executed action, r is the immediate reward, and γ is the discount factor.

[0038] Preferably, the deep reinforcement learning control unit selects the device control action through an ∈-greedy strategy, and the strategy expression is:

[0039]

[0040] Among them, ∈ is the exploration probability and |A| is the size of the action space.

[0041] Preferably, the model interpretability analysis module uses a graph neural network to implement causal relationship analysis between devices, and the node embedding update formula is:

[0042]

[0043] in, is the neighborhood of node i.

[0044] Preferably, the interpretability analysis and visualization unit uses the SHAP algorithm to interpret the prediction results of the model, and the SHAP value calculation formula is:

[0045]

[0046] Among them, φ i It represents the SHAP value of feature i for the model output result, S represents the feature subset, and N represents the entire feature set in the model. represents the feature subset S is the subset of the set N after removing feature i, and |S|! represents the factorial of the feature subset S;

[0047] (|N|-|S|-1) represents the factorial of the remaining features in feature subset N except subset S and feature i, and |N|! represents the factorial of feature set N;

[0048] f(S∪{i}) represents the output result of model f when it contains feature subset S and feature i;

[0049] f(S) represents the output result of model f when it includes feature subset S.

[0050] Preferably, the adaptive optimization and federated learning module performs model training independently at each edge node, and adds noise interference through a differential privacy protection mechanism, and the formula is as follows:

[0051]

[0052] in, is the noise term.

[0053] Preferably, the federated learning unit realizes the aggregation of model parameters of each node through a secure multi-party computing protocol, and the model update formula is:

[0054]

[0055] Among them, n i is the data volume of each node, and n is the total data volume.

[0056] The present invention provides an offshore wind power operation risk analysis system based on deep learning.

[0057] Beneficial effects:

[0058] 1. The present invention introduces the self-attention mechanism and Transformer architecture to perform deep fusion analysis on multimodal data from wind power equipment. Information from different data sources can be weighted and integrated in a unified feature space, so that the system can adaptively focus on important features and significantly improve the analysis accuracy and fault detection capabilities of multimodal data. The effective fusion of multimodal data enables the system to monitor the state changes of equipment in multiple dimensions, so as to accurately identify abnormal equipment conditions in complex marine environments and fully capture wind turbine faults and potential risks.

[0059] 2. The present invention deploys computing nodes at the edge of the wind farm, sinking data processing and model reasoning to the local area, thereby reducing data transmission delays and network dependence. The system can analyze the status of wind power equipment in real time on site, respond quickly according to environmental changes, and effectively improve the accuracy and timeliness of fault warnings. In combination with deep reinforcement learning algorithms, the system can achieve dynamic control of wind turbines by continuously learning and optimizing equipment operation strategies in a real-time environment, so as to automatically adjust equipment operating parameters according to real-time feedback, so that wind turbines can operate with the optimal strategy under different weather conditions, thereby improving the operating efficiency and life of the equipment.

[0060] 3. The present invention introduces interpretable technologies such as SHAP and graph neural networks, so that the system can reveal the logic behind the model decision when performing fault prediction and risk analysis. Users can clearly understand the impact of each input feature on the final prediction result, making the model's judgment process more transparent, and operation and maintenance personnel can obtain detailed feature importance analysis when viewing the fault prediction results. The transparent analysis improves the trust of the model, provides a more favorable basis for decision-making, and guides operation and maintenance personnel to make scientific decisions in equipment maintenance and risk management. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0062] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0063] The present invention is described in detail below in conjunction with the accompanying drawings:

[0064] Example:

[0065] Please see attached Figure 1 , an embodiment of the present invention provides an offshore wind power operation risk analysis system based on deep learning, comprising:

[0066] Data collection and preprocessing module, used to collect wind power equipment operating status data and environmental data from multiple sensors, and clean and preprocess the data;

[0067] Multimodal data fusion and feature extraction module, used to extract and fuse multimodal features from different data sources;

[0068] Real-time risk prediction and intelligent decision-making module, which is used to analyze the operating status of wind power equipment in real time based on deep learning models, and to make predictions and alarms when fault trends are detected;

[0069] Model interpretability and intelligent decision support module, used to explain the prediction results and provide intuitive visualization;

[0070] The adaptive optimization and federated learning module is used to optimize model performance through edge computing and federated learning technology, and realize distributed collaborative training and data privacy protection.

[0071] The benefit of the data acquisition and preprocessing module is that it can remove noise and outliers by implementing data cleaning and preprocessing steps, ensure the accuracy and reliability of subsequent analysis, improve the integrity of the data set, and provide a solid foundation for risk analysis; the benefit of the multimodal data fusion and feature extraction module is that it can integrate information from different perspectives, extract more representative and information-rich features, improve the prediction accuracy of the model, effectively process and interpret large-scale, high-dimensional data sets, make the model more robust, and cope with various complex operating environments; the benefit of the real-time risk prediction and intelligent decision-making module is that it greatly reduces potential equipment failures and downtime, and can adjust and optimize the operation strategy according to specific circumstances, thereby reducing risks and optimizing the overall performance of the wind farm; the benefit of the model interpretability and intelligent decision support module is that it helps operation and maintenance personnel and managers understand the prediction and decision-making process of the model, improves users' trust in the system output, and enables operation and maintenance personnel to perform scientific maintenance and risk management based on the detailed feature importance analysis provided by the model; the benefit of the adaptive optimization and federated learning module is that it improves the scalability and flexibility of the model, which is particularly important when processing sensitive equipment operation data.

[0072] The data collection and preprocessing module uses a density-based clustering algorithm to detect outliers. Outlier detection is determined by the following formula:

[0073]

[0074] Among them, ∈ is the threshold parameter, which is used to control the distance between data points.

[0075] The role of density-based clustering algorithm for outlier detection. In the data acquisition and preprocessing module, the density-based clustering algorithm is used to detect outliers. Its role is to identify abnormal points or noise in the data set. The abnormal points are usually abnormal data caused by sensor failure, environmental interference or other abnormal conditions;

[0076] The advantage of density-based clustering algorithms is that they can automatically identify outliers in the data and maintain high detection accuracy when the data set contains noise or outliers. For sensor data of offshore wind power equipment, its algorithm can reliably identify outliers in the data to prevent abnormal data from affecting subsequent analysis and prediction. It can also adaptively adjust detection parameters and flexibly respond to various abnormal situations, which is particularly important for real-time detection and prediction systems. At the same time, density-based clustering algorithms can adapt to data of different dimensions and effectively integrate various types of information to perform comprehensive anomaly detection.

[0077] The multimodal data fusion and feature extraction module includes a convolutional neural network and a long short-term memory network, which are used to process visual data and time series data. The state update formula of LSTM is:

[0078] i t=σ(W i ·[h t-1 ,x t ]+b i ),

[0079] f t =σ(W f ·[h t-1 ,x t ]+b f ),

[0080] o t =σ(W o ·[h t-1 ,x t ]+b o ),

[0081] C t =f t ·C t-1 +i t tanh(W c ·[h t-1 ,x t ]+b c ),

[0082] h t =o t tanh(C t ),

[0083] Among them, i t 、f t , o t are input gate, forget gate and output gate;

[0084] C t represents the cell state at time step t;

[0085] h t represents the hidden state at time step t;

[0086] x t represents the input data at time step t;

[0087] h t-1 represents the hidden state of the previous time step t-1;

[0088] W i , W f , W o , W c Represents the weight parameters of the LSTM network;

[0089] b i 、b f 、b o 、b cRepresents the bias term of the LSTM network;

[0090] σ represents the logistic sigmoid activation function; tanh represents the hyperbolic tangent activation function.

[0091] The role of the multimodal data fusion and feature extraction module, which uses a combination of convolutional neural networks and long short-term memory networks to process visual data and time series data from offshore wind power equipment. Convolutional neural networks are specifically used to process image data and extract visual features, such as surface damage, wear and other visual anomalies of equipment, while long short-term memory networks are used to analyze and predict time series-based data, such as wind speed, voltage, current, etc.;

[0092] Convolutional neural networks automatically learn complex patterns in images through deep network structures without the need for manual feature design, which is crucial for monitoring the appearance and structural status of wind power equipment.

[0093] Long short-term memory networks can effectively capture long-term dependencies in time series data and are extremely effective in predicting and monitoring time-based parameter changes;

[0094] By processing multiple types of data simultaneously, the system can identify problems at a visual level and obtain dynamic change trends from time series analysis, thereby improving the comprehensiveness and accuracy of fault detection and prediction.

[0095] The adaptive fusion unit uses the self-attention mechanism to perform weighted fusion of multimodal data. The self-attention mechanism is calculated by the following formula:

[0096]

[0097] Among them, Q and K are the query matrix and key matrix.

[0098] The role of the self-attention mechanism. The self-attention mechanism is an attention mechanism in the sequence model. By calculating the weights of pairs of elements in the sequence, it helps the model establish direct dependencies between data points at different positions. In the multimodal data fusion and feature extraction module, the self-attention mechanism is particularly used to integrate information from different sources;

[0099] The self-attention mechanism can capture and emphasize the most important features in the input data. By weighted fusion features, a more comprehensive and rich feature representation is constructed. This is especially important for multimodal data of wind power systems, ensuring that important information is fully utilized.

[0100] Compared with traditional sequence processing models, the self-attention mechanism does not depend on the order of the sequence and can process the sequence in parallel. Parallelism significantly improves the efficiency of data processing, especially when processing large-scale data sets.

[0101] Since the self-attention layer can show the contribution of each input part to the final output, the interpretability of the model is enhanced. In the risk analysis of wind power systems, it can be clearly seen that the features play a key role in decision-making, helping the maintenance team to better understand the model predictions and make decisions.

[0102] The real-time risk prediction and intelligent decision-making module uses a deep Q network to optimize the equipment control strategy. The Q value update formula is:

[0103]

[0104] Among them, s is the current state, a is the executed action, r is the immediate reward, and γ is the discount factor.

[0105] Deep Q-network is a method that optimizes the decision-making process by combining classic Q-learning and deep learning techniques. In the offshore wind power operation risk analysis system, deep Q-network is used to optimize the equipment control strategy and adjust the operation of wind power equipment in real time in an automated manner to deal with possible risks or failures.

[0106] The deep Q network quickly adapts to environmental changes by updating the Q value in real time, allowing the system to adjust the control strategy of wind power equipment immediately. Its automated decision-making process significantly improves the system's ability to respond to emergencies, such as severe weather or equipment failure.

[0107] Automated optimization control strategies reduce the need for manual intervention and reduce operation and maintenance costs. At the same time, optimized equipment control strategies can reduce energy consumption and unnecessary equipment wear, further reducing operating costs.

[0108] The deep Q network can learn to extract behavioral strategies from complex data, enabling the system to adapt to the ever-changing operating environment and equipment status. Its learning and adaptability make the system more robust in the face of a changing marine environment.

[0109] The deep reinforcement learning control unit selects the device control action through the ∈-greedy strategy, and the strategy expression is:

[0110]

[0111] Among them, ∈ is the exploration probability and |A| is the size of the action space.

[0112] In the real-time risk prediction and intelligent decision-making module, the deep reinforcement learning control unit adopts the ∈-greedy strategy to select the device control action. This is a decision-making strategy commonly used in reinforcement learning algorithms. It aims to balance the relationship between exploration and utilization. The ∈-greedy strategy randomly determines a small-probability exploration action or determines the currently known optimal action with a high probability, so that the learning algorithm can use the known best strategy while trying new strategies.

[0113] ∈-greedy strategy allows the system to use the known optimal strategy most of the time, while retaining a certain degree of randomness to explore new possibilities. Its strategy helps the system avoid converging to the local optimal solution too early and improves the possibility of global optimization;

[0114] By randomly exploring new actions, the system avoids overfitting to specific initial conditions or noise, and enhances the adaptability and stability of the model in different or unknown environments;

[0115] By dynamically adjusting the ∈ value, the ratio of exploration and utilization is adjusted according to the system's learning progress and environmental changes, making the system more flexible in responding to complex operating environments and task requirements.

[0116] The model interpretability analysis module uses graph neural networks to implement causal relationship analysis between devices. The node embedding update formula is:

[0117]

[0118] in, is the neighborhood of node i.

[0119] In the model interpretability analysis module, graph neural networks are used to analyze the cause-effect relationship between devices. The analysis is based on the interaction and influence between the components of the devices. By using graph neural networks, each device component is processed as a node, and the physical connection or functional dependency between device components is represented by edges.

[0120] Graph neural networks can significantly improve the understanding of the interactions between components of offshore wind power equipment, especially the complex and non-intuitive internal structural relationships. By updating node embeddings, graph neural networks simulate how components affect each other and help maintenance personnel identify key influencing factors and potential risk points.

[0121] By visualizing each node and its connections, maintenance personnel can directly see the impact of components on system performance and how faults propagate in the equipment;

[0122] Understanding the cause-and-effect relationships between equipment components makes maintenance strategies more targeted, such as prioritizing maintenance of parts that affect the overall performance of the system. At the same time, fault diagnosis can be faster and more accurate, reducing equipment downtime.

[0123] The interpretability analysis and visualization unit uses the SHAP algorithm to explain the prediction results of the model. The SHAP value calculation formula is:

[0124]

[0125] Among them, φ i It represents the SHAP value of feature i for the model output result, S represents the feature subset, and N represents the entire feature set in the model. represents the feature subset S is the subset of the set N after removing feature i, and |S|! represents the factorial of the feature subset S;

[0126] (|N|-|S|-1) represents the factorial of the remaining features in feature subset N except subset S and feature i, and |N|! represents the factorial of feature set N;

[0127] f(S∪{i}) represents the output result of model f when it contains feature subset S and feature i;

[0128] f(S) represents the output result of model f when it includes feature subset S.

[0129] The SHAP algorithm is a method for explaining the predictions of machine learning models. It is based on the Shap l ey value in cooperative game theory. The algorithm quantifies the importance of each feature in the prediction result by evaluating the contribution of each feature to the model output.

[0130] SHAP values ​​provide a clear measure of how each feature affects the model's predictive output. Transparency enables the model's decision-making process to be understood and trusted by users, especially in areas that require a high degree of interpretability.

[0131] By analyzing the SHAP value, decision makers can identify the impact of features on the model’s predictions, features are redundant, and can guide data scientists to perform feature engineering and optimize the model’s input and structure;

[0132] The application of SHAP value can reveal the potential bias and unfair decision-making basis of the model, and reduce bias by adjusting or retraining the model to ensure the fairness of the model's decision;

[0133] With a deeper understanding of model predictions, developers can more effectively adjust and improve models, especially in areas where models perform poorly. In addition, SHAP values ​​can help business leaders develop more precise strategies and improve decision-making efficiency and effectiveness.

[0134] When explaining model decisions to non-technical stakeholders, SHAP values ​​provide an effective visual and numerical tool that aids communication and collaboration across departments.

[0135] The adaptive optimization and federated learning modules independently perform model training on each edge node and add noise interference through the differential privacy protection mechanism. The formula is as follows:

[0136]

[0137] in, is the noise term.

[0138] The adaptive optimization and federated learning module adopts the strategy of independently training models on each edge node, while introducing a differential privacy protection mechanism to enhance data security. In this method, each node updates its own model parameters and protects the privacy of training data by adding random noise.

[0139] By adding noise to the gradient update, the differential privacy mechanism ensures that even in the case of information leakage, it is difficult for an attacker to accurately infer the data of any specific individual from the shared model parameters, which is particularly important for applications involving sensitive data.

[0140] Federated learning allows multiple edge nodes to share model updates instead of raw data, and can jointly train global models without directly exchanging data, reducing the risk of data leakage;

[0141] Since each edge node can learn using information from different data sources, federated learning helps improve the generalization ability of the global model. The model is adapted to the data characteristics of a single node and can adapt to a wider data distribution.

[0142] Model training at edge nodes can reduce the need to transmit large amounts of data to central servers, thereby reducing communication costs and latency, which is particularly important for real-time or near real-time application scenarios, such as in real-time monitoring and control systems for wind farms.

[0143] The federated learning unit aggregates the model parameters of each node through a secure multi-party computing protocol. The model update formula is:

[0144]

[0145] Among them, n i is the data volume of each node, and n is the total data volume.

[0146] The federated learning unit uses a secure multi-party computing protocol to achieve secure aggregation of model parameters at each edge node. Under its architecture, model training is performed locally on each node. Each node does not need to share original data, but securely aggregates model parameters through the SMPC protocol.

[0147] Privacy is protected through the secure multi-party computing protocol. Each node shares the parameters of the model instead of directly sharing the original data. This mechanism reduces the risk of data leakage. Even if there are malicious participants, it is difficult to obtain the data content of other nodes through the SMPC protocol, thus ensuring the privacy of each node.

[0148] Since different nodes have differently distributed data sets, federated learning improves generalization capabilities by aggregating heterogeneous data to train a global model. The integration method helps the global model perform better in a wider range of environments, rather than relying on data from a single node for training.

[0149] The SMPC protocol allows multiple nodes to work together. As the number of nodes increases, more data can be used for training without requiring the central server to bear too much data transmission burden. Therefore, the system has high scalability and is suitable for large-scale distributed systems.

[0150] By using the SMPC protocol, model updates are encrypted, and participating nodes will not expose their model update details to other nodes. Even if some nodes are hacked, attackers cannot infer sensitive data by analyzing the updates, greatly improving the security of the system.

[0151] Federated learning performs local training on each edge node to fully utilize edge computing resources and reduce the computing pressure and network transmission burden of the central server. In addition, through federated learning, the system can efficiently utilize distributed node resources to improve the training efficiency of the model;

[0152] SMPC needs to transmit model parameters instead of raw data or large-scale gradient information, which reduces the communication bandwidth demand and the delay caused by large-scale data transmission. It is especially important for distributed scenarios such as offshore wind power that require real-time response.

[0153] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A deep learning-based offshore wind power operation risk analysis system, characterized in that: include: Data collection and preprocessing module, used to collect wind power equipment operating status data and environmental data from multiple sensors, and clean and preprocess the data; Multimodal data fusion and feature extraction module, used to extract and fuse multimodal features from different data sources; Real-time risk prediction and intelligent decision-making module, which is used to analyze the operating status of wind power equipment in real time based on deep learning models, and to make predictions and alarms when fault trends are detected; Model interpretability and intelligent decision support module, used to explain the prediction results and provide intuitive visualization; The adaptive optimization and federated learning module is used to optimize model performance through edge computing and federated learning technology, and realize distributed collaborative training and data privacy protection.

2. According to claim 1, a deep learning-based offshore wind power operation risk analysis system is characterized in that: The data acquisition and preprocessing module uses a density-based clustering algorithm to detect outliers, and the outlier detection is judged by the following formula: Among them, ∈ is the threshold parameter, which is used to control the distance between data points.

3. According to the offshore wind power operation risk analysis system based on deep learning in claim 1, it is characterized in that: The multimodal data fusion and feature extraction module includes a convolutional neural network and a long short-term memory network, which are used to process visual data and time series data. The state update formula of the LSTM is: i t =σ(W i ·[h t-1 ,x t ]+b i ), f t =σ(W f ·[h t-1 ,x t ]+b f ), the t =σ(W o ·[h t-1 ,x t ]+b o ), C t =f t ·C t-1 +i t ·tanh(W c ·[h t-1 ,x t ]+b c ), h t =o t ·tanh(C t ), Among them, i t 、f t , o t are input gate, forget gate and output gate; C t represents the cell state at time step t; h t represents the hidden state at time step t; x t represents the input data at time step t; h t-1 represents the hidden state of the previous time step t-1; W i , W f , W o , W c Represents the weight parameters of the LSTM network; b i 、b f 、b o 、b c Represents the bias term of the LSTM network; σ represents the logistic sigmoid activation function; tanh represents the hyperbolic tangent activation function.

4. According to claim 1, a deep learning-based offshore wind power operation risk analysis system is characterized in that: The adaptive fusion unit uses a self-attention mechanism to perform weighted fusion on multimodal data, and the self-attention mechanism is calculated by the following formula: Among them, Q and K are the query matrix and key matrix.

5. The offshore wind power operation risk analysis system based on deep learning according to claim 1 is characterized in that: The real-time risk prediction and intelligent decision-making module uses a deep Q network to optimize the equipment control strategy, and the Q value update formula is: Among them, s is the current state, a is the executed action, r is the immediate reward, and γ is the discount factor.

6. The offshore wind power operation risk analysis system based on deep learning according to claim 1 is characterized in that: The deep reinforcement learning control unit selects the device control action through the ∈-greedy strategy, and the strategy expression is: Among them, ∈ is the exploration probability and |A| is the size of the action space.

7. The offshore wind power operation risk analysis system based on deep learning according to claim 1 is characterized in that: The model interpretability analysis module uses graph neural network to realize causal relationship analysis between devices, and the node embedding update formula is: in, is the neighborhood of node i.

8. The offshore wind power operation risk analysis system based on deep learning according to claim 1, characterized in that: The interpretability analysis and visualization unit uses the SHAP algorithm to interpret the prediction results of the model. The SHAP value calculation formula is: Among them, φ i It represents the SHAP value of feature i for the model output result, S represents the feature subset, and N represents the entire feature set in the model. represents the feature subset S is the subset of the set N after removing feature i, and |S|! represents the factorial of the feature subset S; (|N|-|S|-1) represents the factorial of the remaining features in feature subset N except subset S and feature i, and |N|! represents the factorial of feature set N; f(S∪{i}) represents the output result of model f when it contains feature subset S and feature i; f(S) represents the output result of model f when it includes feature subset S.

9. The offshore wind power operation risk analysis system based on deep learning according to claim 1, characterized in that: The adaptive optimization and federated learning module performs model training independently on each edge node and adds noise interference through the differential privacy protection mechanism. The formula is as follows: in, is the noise term.

10. The offshore wind power operation risk analysis system based on deep learning according to claim 1, characterized in that: The federated learning unit aggregates the model parameters of each node through a secure multi-party computing protocol, and the model update formula is: Among them, n i is the data volume of each node, and n is the total data volume.

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