Floating type wind power platform attitude monitoring and early warning control system

Abstract

The invention discloses a floating type wind power platform attitude monitoring and early warning control system. The system comprises an attitude sensing module, a data preprocessing module, a central processing module, an early warning module, a regulation and control execution module and a power supply module. The attitude sensing module collects data in multiple dimensions, after dual preprocessing of wavelet threshold denoising and Kalman filtering, the central processing module calculates attitude parameters through a complementary filtering algorithm, the attitude abnormality level is judged by combining a BP neural network dynamic early warning threshold model, and then the early warning module is triggered to conduct graded early warning. Meanwhile, the regulation and control execution module is driven to correct the posture hierarchically; and the power supply module adopts photovoltaic and lithium battery cooperative power supply. According to the invention, high-precision real-time monitoring, dynamic intelligent early warning and active linkage regulation and control of the platform attitude are realized, the early warning accuracy and the platform operation safety are improved, and the method is suitable for a deep and far sea floating type wind power platform scene.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power equipment monitoring technology, and in particular to a floating wind power platform attitude monitoring and early warning control system. Background Technology

[0002] With the acceleration of the global energy structure transformation, offshore wind power, as an important component of clean and renewable energy, is experiencing continuous expansion in its development scale. Compared to fixed offshore wind power platforms, floating wind power platforms can be applied to deeper sea areas, expanding the development scope of offshore wind power and showing broad application prospects. However, floating wind power platforms are constantly in a complex marine environment, subject to the coupled effects of various loads such as wind, waves, ocean currents, and tides, making them prone to multi-degree-of-freedom attitude movements such as roll, pitch, heave, and yaw.

[0003] Excessive changes in the platform's attitude can severely impact the normal operation of wind turbines. On the one hand, it may cause collisions between the turbine blades and the tower, damaging both the blades and the tower structure. On the other hand, it can exacerbate fatigue damage to the platform's mooring system, reduce mooring reliability, and even trigger major safety accidents such as platform overturning. Therefore, real-time and accurate monitoring of the floating wind turbine's attitude, along with timely warnings and control measures for abnormal attitudes, is crucial to ensuring the safe and stable operation of floating wind turbines.

[0004] In existing technologies, most solutions for attitude monitoring of floating wind turbine platforms employ a single type of sensor for data acquisition, such as gyroscopes or accelerometers, which suffers from low monitoring accuracy and weak anti-interference capabilities. Furthermore, existing early warning systems often rely on fixed thresholds for judgment, failing to fully consider the dynamic changes in marine environmental loads, resulting in poor accuracy and a high false alarm rate. In addition, most existing systems only possess monitoring and early warning functions, lacking linkage with platform control and execution mechanisms, and are unable to proactively correct the platform's attitude in a timely manner after an early warning, making it difficult to fundamentally mitigate safety risks.

[0005] In view of the shortcomings of the prior art, the present invention proposes a floating wind power platform attitude monitoring and early warning control system that can realize high-precision attitude monitoring, dynamic intelligent early warning and linkage control. Summary of the Invention

[0006] To address the problems of low attitude monitoring accuracy, poor early warning accuracy, and lack of linkage control capability in existing technologies for floating wind power platforms, this invention provides a floating wind power platform attitude monitoring and early warning control system, which realizes high-precision real-time monitoring of platform attitude, dynamic intelligent early warning, and proactive linkage control, ensuring the safe and stable operation of the platform.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A floating wind power platform attitude monitoring and early warning control system includes an attitude sensing module, a data preprocessing module, a central processing module, an early warning module, a control execution module, and a power supply module.

[0009] The attitude sensing module is used to collect attitude data of the floating wind power platform, marine environmental load data and mooring system force data, and transmit the collected data to the data preprocessing module.

[0010] The data preprocessing module is used to perform noise reduction, filtering, data synchronization and format conversion on the collected raw data to obtain standardized effective data, and transmit the effective data to the central processing module.

[0011] The central processing module is used to calculate the real-time attitude parameters of the platform based on standardized effective data and a preset attitude calculation algorithm. Then, it combines the dynamic early warning threshold model to determine whether the platform attitude is abnormal. If abnormal, it generates an early warning command and a corresponding attitude control command, and transmits them to the early warning module and the control execution module respectively.

[0012] The early warning module is used to receive early warning instructions sent by the central processing module and perform corresponding early warning operations according to the early warning level.

[0013] The control and execution module is used to receive attitude control commands sent by the central processing module and drive the corresponding actuator to perform actions, thereby correcting the platform's attitude.

[0014] The power module is used to provide a stable power supply for each module of the system.

[0015] Furthermore, the attitude perception module includes an attitude sensor group, an environmental load sensor group, and a mooring force sensor group;

[0016] The attitude sensor group includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer installed at the center of gravity of the platform, used to collect angular velocity data, linear acceleration data, and magnetic field strength data of the platform;

[0017] The environmental load sensor group includes a wind speed and direction sensor installed on the top of the platform, a wave sensor installed at the platform's draft, and an ocean current sensor, used to collect real-time data on wind speed, wind direction, wave height, wave period, ocean current speed, and ocean current direction.

[0018] The mooring force sensor group includes tension sensors installed on the platform's mooring cables, used to collect real-time tension data of each mooring cable.

[0019] Furthermore, the specific processing procedure of the data preprocessing module is as follows:

[0020] 1) Data Denoising: Wavelet thresholding denoising algorithm is used to remove noise from the original data. The expression for wavelet thresholding denoising is:

[0021]

[0022] in, Let be the wavelet coefficient of the j-th level k-th wavelet after wavelet decomposition. λ represents the denoised wavelet coefficients, λ represents the wavelet threshold, and sign(·) represents the sign function;

[0023] 2) Data Filtering: The Kalman filter algorithm is used to smooth the denoised data. The Kalman filter includes two stages: prediction and update. Its state equation and observation equation are as follows:

[0024] Prediction phase:

[0025]

[0026]

[0027] Update phase:

[0028]

[0029]

[0030]

[0031] in, Let be the prior state estimate at time k. Let A be the posterior state estimate at time k-1, A be the state transition matrix, and B be the control input matrix. This is the control input at time k-1. Let be the prior covariance matrix at time k. Let Q be the posterior covariance matrix at time k-1, and let Q be the process noise covariance matrix. Let H be the Kalman gain, H be the observation matrix, and R be the observation noise covariance matrix. Let I be the observation value at time k, and let I be the identity matrix.

[0032] 3) Data synchronization: Based on the timestamp information of each sensor, linear interpolation is used to synchronize the data collected by different sensors.

[0033] 4) Format conversion: Convert the synchronized data into a unified digital format.

[0034] Furthermore, the attitude calculation algorithm in the central processing module adopts a complementary filtering algorithm, which combines the angular velocity, linear acceleration and magnetic field strength data collected by the attitude sensor group to calculate the attitude and obtain the real-time attitude angles of the platform, including the roll angle θ, pitch angle φ and yaw angle ψ.

[0035] The core expression of the complementary filtering algorithm is:

[0036]

[0037]

[0038]

[0039] Where θ0, φ0, and ψ0 are the initial roll angle, initial pitch angle, and initial yaw angle, respectively. , , θ represents the angular velocities around each axis collected by the gyroscope. a φ a These are the roll and pitch angles calculated from accelerometer data, ψ m The yaw angle is calculated based on magnetometer data, and α and β are complementary filter weight coefficients with values ​​ranging from 0 < α < 1 and 0 < β < 1.

[0040] Furthermore, the dynamic early warning threshold model construction process in the central processing module is as follows:

[0041] 1) Construct a sample database: Collect historical attitude data, mooring force data, and corresponding safety status labels of the platform under different marine environmental levels;

[0042] 2) Feature extraction: Extract characteristic parameters such as peak attitude angle, rate of change of attitude angle, maximum tension of mooring cable, and rate of change of tension of mooring cable from the sample data;

[0043] 3) Model training: The extracted feature parameters are used as inputs and the safety status labels are used as outputs. The inputs are fed into the BP neural network model for training to obtain the dynamic early warning threshold model.

[0044] 4) Threshold Output: Input real-time marine environmental payload data into the trained dynamic early warning threshold model, and output the attitude angle early warning threshold θ corresponding to the environment. th φ th ψ th and the mooring cable tension warning threshold F th .

[0045] Furthermore, the anomaly detection logic of the central processing module is as follows:

[0046] The calculated real-time attitude angles θ, φ, and ψ are compared with their corresponding dynamic early warning thresholds θ, φ, and ψ. th φ th ψ th Comparison, while simultaneously setting the real-time maximum tension F of the mooring cable. max With the tensile warning threshold F th Compare;

[0047] If |θ|≤θ th |φ|≤φ th 、|ψ|≤ψ th And F max ≤F th If so, the platform's posture is considered normal;

[0048] If one or more of the conditions are met and exceed the corresponding warning threshold, the platform is judged to be in an abnormal posture, and the warning level is divided according to the degree of exceeding the threshold: mild warning, moderate warning, and severe warning.

[0049] Furthermore, the early warning module includes an audible and visual early warning unit, a remote communication early warning unit, and a local display unit;

[0050] When a mild warning command is received, the audible and visual warning unit emits a yellow warning light and a low-frequency warning sound, the local display unit displays the warning information, and the remote communication warning unit sends the warning information to the shore-based monitoring center.

[0051] When a moderate warning command is received, the audible and visual warning unit emits an orange warning light and a medium-frequency warning sound, the local display unit displays the warning information and preliminary control suggestions, and the remote communication warning unit sends the warning information and real-time monitoring data to the shore-based monitoring center.

[0052] When a severe warning command is received, the audible and visual warning unit emits a red warning light and a high-frequency warning sound, the local display unit displays the warning information and emergency control plan, and the remote communication warning unit sends the warning information, real-time monitoring data and emergency control plan to the shore-based monitoring center and triggers the shore-based alarm device.

[0053] Furthermore, the control and execution module includes an actuator controller and corresponding actuators, the actuators including active anti-roll fins at the bottom of the platform, a tension adjustment device for the mooring cable, and a pitch adjustment device for the wind turbine.

[0054] After receiving the attitude control command from the central processing module, the actuator controller drives the corresponding actuator to perform the action according to the warning level:

[0055] During a mild warning, the active anti-roll fins are driven to make small angle adjustments;

[0056] During a moderate warning, based on the active anti-roll fin adjustment, the tension adjustment device of the mooring lines is activated to adjust the tension distribution of each mooring line.

[0057] In the event of a severe warning, based on the above-mentioned control actions, the pitch adjustment device of the wind turbine is activated to reduce the windward area of ​​the wind turbine. If the attitude still cannot be corrected, a shutdown command is issued to control the wind turbine to stop running.

[0058] Furthermore, the power module adopts a combination of solar photovoltaic power supply and backup lithium battery power supply, including solar panels, charge and discharge controller, lithium battery pack and DC-DC converter;

[0059] The solar panels convert solar energy into electrical energy, which is used to charge the lithium battery pack via a charge / discharge controller, while also powering the various modules of the system. When there is insufficient sunlight or at night, the lithium battery pack powers the various modules of the system via the charge / discharge controller. The DC-DC converter is used to convert the output voltage to the rated voltage required by the various modules of the system.

[0060] The beneficial effects of this invention are as follows:

[0061] 1) High monitoring accuracy: This invention uses a multi-type sensor group to collect data collaboratively, covering multi-dimensional data such as platform attitude, marine environmental load and mooring force. It combines wavelet threshold denoising and Kalman filtering dual data preprocessing mechanism to effectively reduce noise interference. Then, attitude calculation is performed through complementary filtering algorithm, which significantly improves the accuracy and stability of attitude monitoring.

[0062] 2) High accuracy of early warning: This invention abandons the traditional fixed threshold early warning method and constructs a dynamic early warning threshold model based on BP neural network. It can dynamically adjust the early warning threshold according to the real-time marine environmental load, avoiding false alarms and missed alarms caused by environmental changes, and improving the accuracy and reliability of early warning.

[0063] 3) Possesses linkage control capability: The present invention is equipped with a control execution module, which can actively drive the corresponding actuator to perform actions according to the warning level, realize hierarchical correction of platform attitude, upgrade from passive warning to active prevention and control, and effectively reduce the safety risks caused by abnormal attitude.

[0064] 4) Stable and reliable power supply: The system adopts a power supply method that combines solar photovoltaic power and backup lithium batteries, eliminating the need for an external power source. This makes it suitable for remote marine environments and ensures long-term stable operation of the system. Attached Figure Description

[0065] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a block diagram of the system modules of the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0069] A floating wind power platform attitude monitoring and early warning control system includes an attitude sensing module, a data preprocessing module, a central processing module, an early warning module, a control execution module, and a power supply module.

[0070] In this embodiment, the attitude sensor group of the attitude perception module uses an MPU9250 nine-axis sensor, which is installed at the center of gravity of the floating wind power platform to collect three-axis angular velocity, three-axis acceleration, and three-axis magnetic field strength data of the platform; the environmental load sensor group uses an RS485 wind speed and direction sensor, an ultrasonic wave sensor, and an electromagnetic ocean current sensor, wherein the wind speed and direction sensor is installed on the outside of the nacelle at the top of the platform, and the wave sensor and ocean current sensor are installed at a draft of 10m on the platform; the mooring force sensor group uses tension and compression sensors, one of which is installed on each mooring cable, for a total of 6, to collect real-time tension data of each mooring cable.

[0071] The data preprocessing module uses an STM32F407 microprocessor as its core processing chip to perform data denoising, filtering, synchronization, and format conversion. In the wavelet threshold denoising algorithm, the db4 wavelet is selected as the wavelet basis, with a decomposition level of 3. The wavelet threshold λ is determined using the formula... The calculations show that σ is the noise standard deviation and n is the data length. In the Kalman filter algorithm, the state transition matrix A, control input matrix B, and observation matrix H are preset according to the sensor type and data dimension, while the process noise covariance matrix Q and observation noise covariance matrix R are obtained through experimental calibration.

[0072] The central processing module uses an industrial-grade ARM Cortex-A9 processor, runs on a Linux operating system, and has built-in preset attitude calculation algorithms and dynamic warning threshold models. The complementary filter weight coefficients α and β of the attitude calculation algorithm are 0.92 and 0.95, respectively. The BP neural network of the dynamic warning threshold model adopts a 3-layer structure, with 8 nodes in the input layer (including wind speed, wave height, wave period, ocean current speed, ocean current direction, peak attitude angle, rate of change of attitude angle, and maximum mooring cable tension), 12 nodes in the hidden layer, and 4 nodes in the output layer (including roll angle warning threshold, pitch angle warning threshold, yaw angle warning threshold, and mooring cable tension warning threshold). The activation function is the Sigmoid function, the training algorithm is gradient descent, the training sample size is 1000 sets, and the accuracy of the trained model reaches 98.5%.

[0073] The audible and visual warning unit of the early warning module uses LED warning lights and buzzers, and is installed in the deck control room of the platform; the remote communication early warning unit uses a 5G industrial module to support real-time data transmission with the shore-based monitoring center; the local display unit uses a 10.1-inch industrial touch screen to display real-time monitoring data, early warning information and control suggestions.

[0074] The actuator controller of the control and execution module is a PLC controller, which communicates with the central processing module via Ethernet; the active anti-roll fins are hydraulically driven anti-roll fins, a total of 4, installed around the bottom of the platform; the tension adjustment device of the mooring cable is an electric winch, which is matched with the mooring cable; the pitch adjustment device of the wind turbine is integrated in the wind turbine nacelle, and the pitch angle is adjusted by the PLC controller.

[0075] The power module uses four 200W monocrystalline silicon solar panels, which are installed on the platform deck. The lithium battery packs are two 12V / 100Ah lithium iron phosphate battery packs, which are used in parallel. The charge and discharge controller is an MPPT type charge and discharge controller, and the DC-DC converter outputs three voltages: 5V, 12V, and 24V, which power the various modules of the system respectively.

[0076] The working process of this embodiment is as follows:

[0077] 1) Data acquisition: The sensors of the attitude perception module acquire the platform's attitude data, marine environmental load data, and mooring force data in real time, and transmit the acquired raw data to the data preprocessing module via RS485 bus;

[0078] 2) Data preprocessing: The data preprocessing module uses a wavelet threshold denoising algorithm to remove noise from the original data, then uses a Kalman filter algorithm to smooth the denoised data, then completes data synchronization based on timestamp information, and finally converts the synchronized data into a standard digital format and transmits it to the central processing module.

[0079] 3) Attitude calculation and anomaly judgment: The central processing module calculates the preprocessed attitude sensor data through a complementary filtering algorithm to obtain the platform's real-time roll angle, pitch angle, and yaw angle; at the same time, it inputs the real-time marine environmental load data into the dynamic early warning threshold model to obtain the corresponding dynamic early warning threshold; and compares the real-time attitude angle and the maximum tension of the mooring cable with the dynamic early warning threshold to determine whether the platform's attitude is abnormal and the level of abnormality.

[0080] 4) Early Warning and Control: If the posture is determined to be normal, the central processing module only transmits the real-time monitoring data to the local display unit of the early warning module for display; if the posture is determined to be abnormal, the central processing module generates corresponding early warning commands and posture control commands, which are transmitted to the early warning module and the control execution module respectively; the early warning module performs audible and visual early warning, local display and remote early warning operations according to the early warning level; the control execution module drives the corresponding actuator to perform actions according to the posture control commands to correct the platform's posture.

[0081] Through the above implementation process, the present invention can monitor the attitude changes of the floating wind power platform in real time and accurately, issue early warnings dynamically and intelligently, and actively link the actuators to perform attitude correction, effectively ensuring the safe and stable operation of the floating wind power platform.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A floating wind power platform attitude monitoring and early warning control system, characterized in that, It includes an attitude perception module, a data preprocessing module, a central processing module, an early warning module, a control and execution module, and a power supply module; The attitude sensing module is used to collect attitude data of the floating wind power platform, marine environmental load data and mooring system force data, and transmit the collected data to the data preprocessing module. The data preprocessing module is used to perform noise reduction, filtering, data synchronization and format conversion on the collected raw data to obtain standardized effective data, and transmit the effective data to the central processing module. The central processing module is used to calculate the real-time attitude parameters of the platform based on standardized effective data and a preset attitude calculation algorithm. Then, it combines the dynamic early warning threshold model to determine whether the platform attitude is abnormal. If abnormal, it generates an early warning command and a corresponding attitude control command, and transmits them to the early warning module and the control execution module respectively. The early warning module is used to receive early warning instructions sent by the central processing module and perform corresponding early warning operations according to the early warning level. The control and execution module is used to receive attitude control commands sent by the central processing module and drive the corresponding actuator to perform actions, thereby correcting the platform's attitude. The power module is used to provide a stable power supply for each module of the system.

2. The floating wind power platform attitude monitoring and early warning control system according to claim 1, characterized in that, The attitude sensing module includes an attitude sensor group, an environmental load sensor group, and a mooring force sensor group. The attitude sensor group includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer installed at the center of gravity of the platform, used to collect angular velocity data, linear acceleration data, and magnetic field strength data of the platform; The environmental load sensor group includes a wind speed and direction sensor installed on the top of the platform, a wave sensor installed at the platform's draft, and an ocean current sensor, used to collect real-time data on wind speed, wind direction, wave height, wave period, ocean current speed, and ocean current direction. The mooring force sensor group includes tension sensors installed on the platform's mooring cables, used to collect real-time tension data of each mooring cable.

3. The floating wind power platform attitude monitoring and early warning control system according to claim 1, characterized in that, The specific processing procedure of the data preprocessing module is as follows: Data denoising: Wavelet thresholding denoising algorithm is used to remove noise from the original data. The expression for wavelet thresholding denoising is: in, Let be the wavelet coefficient of the j-th level k-th wavelet after wavelet decomposition. λ represents the denoised wavelet coefficients, λ represents the wavelet threshold, and sign(·) represents the sign function; Data filtering: The Kalman filter algorithm is used to smooth the denoised data. The Kalman filter includes two stages: prediction and update. Its state equation and observation equation are as follows: Prediction phase: Update phase: in, Let be the prior state estimate at time k. Let A be the posterior state estimate at time k-1, A be the state transition matrix, and B be the control input matrix. This is the control input at time k-1. Let be the prior covariance matrix at time k. Let Q be the posterior covariance matrix at time k-1, and let Q be the process noise covariance matrix. Let H be the Kalman gain, H be the observation matrix, and R be the observation noise covariance matrix. Let I be the observation value at time k, and let I be the identity matrix. Data synchronization: Based on the timestamp information of each sensor, linear interpolation is used to synchronize the data collected by different sensors in time; Format conversion: Converts synchronized data of various types into a unified digital format.

4. The floating wind power platform attitude monitoring and early warning control system according to claim 2, characterized in that, The attitude calculation algorithm in the central processing module adopts a complementary filtering algorithm, which combines the angular velocity, linear acceleration and magnetic field strength data collected by the attitude sensor group to calculate the attitude and obtain the real-time attitude angles of the platform, including the roll angle θ, pitch angle φ and yaw angle ψ. The core expression of the complementary filtering algorithm is: Where θ0, φ0, and ψ0 are the initial roll angle, initial pitch angle, and initial yaw angle, respectively. , , θ represents the angular velocities around each axis collected by the gyroscope. a φ a These are the roll and pitch angles calculated from accelerometer data, ψ m The yaw angle is calculated based on magnetometer data, and α and β are complementary filter weight coefficients with values ​​ranging from 0 < α < 1 and 0 < β < 1.

5. The floating wind power platform attitude monitoring and early warning control system according to claim 1, characterized in that, The process of constructing the dynamic early warning threshold model in the central processing module is as follows: Construct a sample database: collect historical attitude data, mooring force data, and corresponding safety status labels of the platform under different marine environmental conditions; Feature extraction: Extract characteristic parameters such as peak attitude angle, rate of change of attitude angle, maximum tension of mooring cable, and rate of change of tension of mooring cable from the sample data; Model training: The extracted feature parameters are used as inputs and the safety status labels are used as outputs. The inputs are fed into the BP neural network model for training to obtain the dynamic early warning threshold model. Threshold Output: Input real-time marine environmental payload data into the trained dynamic early warning threshold model, and output the attitude angle early warning threshold θ corresponding to the environment. th φ th ψ th and the mooring cable tension warning threshold F th .

6. The floating wind power platform attitude monitoring and early warning control system according to claim 5, characterized in that, The anomaly detection logic of the central processing module is as follows: The calculated real-time attitude angles θ, φ, and ψ are compared with their corresponding dynamic early warning thresholds θ, φ, and ψ. th φ th ψ th Comparison, while simultaneously setting the real-time maximum tension F of the mooring cable. max With the tensile warning threshold F th Compare; If |θ|≤θ th |φ|≤φ th 、|ψ|≤ψ th And F max ≤F th If so, the platform's posture is considered normal; If one or more of the conditions are met and exceed the corresponding warning threshold, the platform is judged to be in an abnormal posture, and the warning level is divided according to the degree of exceeding the threshold: mild warning, moderate warning, and severe warning.

7. The floating wind power platform attitude monitoring and early warning control system according to claim 1, characterized in that, The early warning module includes an audible and visual early warning unit, a remote communication early warning unit, and a local display unit; When a mild warning command is received, the audible and visual warning unit emits a yellow warning light and a low-frequency warning sound, the local display unit displays the warning information, and the remote communication warning unit sends the warning information to the shore-based monitoring center. When a moderate warning command is received, the audible and visual warning unit emits an orange warning light and a medium-frequency warning sound, the local display unit displays the warning information and preliminary control suggestions, and the remote communication warning unit sends the warning information and real-time monitoring data to the shore-based monitoring center. When a severe warning command is received, the audible and visual warning unit emits a red warning light and a high-frequency warning sound, the local display unit displays the warning information and emergency control plan, and the remote communication warning unit sends the warning information, real-time monitoring data and emergency control plan to the shore-based monitoring center and triggers the shore-based alarm device.

8. The floating wind power platform attitude monitoring and early warning control system according to claim 1, characterized in that, The control and execution module includes an actuator controller and corresponding actuators. The actuators include active anti-roll fins at the bottom of the platform, a tension adjustment device for the mooring cable, and a pitch adjustment device for the wind turbine. After receiving the attitude control command from the central processing module, the actuator controller drives the corresponding actuator to perform the action according to the warning level: During a mild warning, the active anti-roll fins are driven to make small angle adjustments; During a moderate warning, based on the active anti-roll fin adjustment, the tension adjustment device of the mooring lines is activated to adjust the tension distribution of each mooring line. In the event of a severe warning, based on the above-mentioned control actions, the pitch adjustment device of the wind turbine is activated to reduce the windward area of ​​the wind turbine. If the attitude still cannot be corrected, a shutdown command is issued to control the wind turbine to stop running.

9. The attitude monitoring and early warning control system for a floating wind power platform according to claim 1, characterized in that, The power module adopts a combination of solar photovoltaic power supply and backup lithium battery power supply, including solar panels, charge and discharge controller, lithium battery pack and DC-DC converter; The solar panels convert solar energy into electrical energy, which is used to charge the lithium battery pack via a charge / discharge controller, while also powering the various modules of the system. When there is insufficient sunlight or at night, the lithium battery pack powers the various modules of the system via the charge / discharge controller. The DC-DC converter is used to convert the output voltage to the rated voltage required by the various modules of the system.

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