Floating foundation adaptive design control method based on real-time monitoring feedback
Through the closed-loop control method of real-time monitoring and dynamic cleaning, the data distortion problem caused by fouling of floating foundation sensors was solved, high-precision monitoring and stable control were achieved, and the sensor life was extended.
Patent Information
- Application Number
- CN202511181529.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-22
- Publication Date
- 2025-10-17
AI Technical Summary
In the existing technology, the monitoring sensors of floating foundations are easily affected by the marine environment and lack the means to accurately and real-timely monitor the thickness of surface dirt, resulting in distortion of monitoring data such as attitude and environmental load. In addition, the sensors are prone to corrosion and the attachment of marine organisms, resulting in significant measurement deviations.
The structural status monitoring sensor group collects posture and dirt status data in real time, and the ultrasonic cleaning device is used to dynamically adjust the cleaning timing and parameters. The data correction algorithm is used to compensate for monitoring deviations, and a closed-loop optimization control method is used to ensure sensor accuracy and floating foundation stability.
It improves the accuracy of monitoring data and control reliability, effectively removes dirt on the sensor surface, ensures the stable operation of the floating foundation in complex marine environments, extends the life of the sensor, and reduces control deviations.
Smart Images
Figure CN120793095A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of ocean engineering, in particular to a floating foundation adaptive design control method based on real-time monitoring feedback. BACKGROUND
[0002] The floating foundation adaptive design control method is a dynamic management technology for floating foundations in marine, lake and other water environments, such as offshore wind power floating bodies and floating platforms.
[0003] In the prior art, the floating foundation control method based on real-time monitoring feedback relies on fixed-period sensor data acquisition and preset threshold control logic: usually, attitude sensors and environmental load sensors are arranged to obtain foundation operation data, when the monitoring value exceeds the preset safety range, control actions such as ballast adjustment and mooring tension adjustment are triggered; for the dirt problem on the surface of the sensor, periodic manual cleaning or fixed-time automatic cleaning mode is usually adopted, and data correction relies on a simple linear compensation algorithm, lacking dynamic adaptation to dirt type and accumulation rate.
[0004] In a complex marine environment, the monitoring sensors of the floating foundation face multiple adverse conditions, high salinity seawater can easily cause corrosion to the sensor hardware, accelerate equipment aging and even directly damage it, shorten its service life, the attachment of marine organisms and the deposition of silt can continuously cover the sensor surface, causing significant deviation between the measured values of key parameters such as floating body inclination and stress and the actual values, and the continuous rocking of the sea waves not only aggravates the mechanical wear of the sensor, but also interferes with its normal monitoring. SUMMARY
[0005] To overcome the shortcomings of the prior art, the present application provides a floating foundation adaptive design control method based on real-time monitoring feedback, which solves the problem that the monitoring sensors of the floating foundation are easily affected by the marine environment and lack accurate real-time monitoring means for the thickness of surface dirt, causing distortion of monitoring data such as attitude and environmental load.
[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: a floating foundation adaptive design control method based on real-time monitoring feedback, comprising the following steps: S1, through a structure state monitoring sensor group on a floating foundation module, for real-time acquisition of floating foundation attitude, environmental load data and dirt state data attached to the surface of the sensor group, to provide original data support for subsequent cleaning and control; S2, based on the dirt state data sensed in step S1, starting the ultrasonic cleaning device integrated on the surface of the sensor group, removing the dirt by dynamically adjusting the cleaning time and working parameters, and ensuring the monitoring accuracy of the sensor group; S3, using the sensor group data cleaned in step S2 to correct the monitoring data deviation, inputting the corrected high-precision monitoring data into the floating foundation module to compensate for the monitoring deviation caused by dirt, dynamically adjusting the ballast foundation structure parameters to optimize the floating foundation state; S4, verifying the cleaning effect of step S2 and the control adjustment result of step S3, feeding back the verification result to step S1 to optimize the model parameters of state perception and dirt judgment, and feeding back to S3 to optimize the data correction algorithm parameters, realizing the whole process closed-loop optimization from monitoring to control; Step S1 provides the basis for dirt judgment for step S2, the cleaning effect of step S2 determines the data quality of step S3, the control optimization of step S3 depends on the cleaning accuracy of step S2, and the verification result of step S4 feeds back to the state perception and dirt judgment model parameters of step S1.
[0007] Preferably, in step S1, a dirt thickness monitoring and cleaning triggering algorithm is adopted, and the formula is:
[0008] is the current sensor surface dirt thickness; is the dirt thickness reference value after the last cleaning operation in step S2, and when initially installed =0; ; is the real-time inclination of the floating foundation; is the influence coefficient of water flow velocity on dirt accumulation rate; is the water flow velocity near the sensor; is the cumulative running time since the last cleaning operation in step S2; When D≥ , the monitoring process is immediately interrupted and the cleaning operation in step S2 is triggered, is the preset cleaning triggering threshold.
[0009] Preferably, in step S3, a data correction algorithm based on dirt thickness D is adopted to compensate and correct the original monitoring data of the sensor to obtain the corrected monitoring data P; The data correction algorithm is used to reduce the influence of dirt D on monitoring accuracy when the monitoring signal of the sensor group is linearly attenuated due to dirt coverage; By analyzing the dirt thickness before cleaning and the corresponding original monitoring data relative to the standard value the bias relationship between the monitoring data P and the standard value Pref, the cleaning trigger threshold in step S2 can be inversely evaluated The rationality of the setting and the accuracy of the fouling accumulation model parameters provide feedback basis for the optimization of the cleaning trigger strategy.
[0010] Preferably, the working time length of the ultrasonic cleaning device in step S2 is determined by the following formula:
[0011] for the cleaning time; for the cleaning efficiency coefficient; for the fouling thickness when the cleaning operation in step S2 is started.
[0012] Preferably, the vibration power of the ultrasonic cleaning device in step S2 is dynamically adjusted according to the real-time monitored vibration intensity of the floating foundation in step S1; When the vibration intensity is greater than the preset threshold, the vibration power is increased to offset the interference of the foundation movement on cleaning; When the vibration intensity is less than the preset threshold, the vibration power is reduced to reduce the additional disturbance to the stability of the floating foundation, and the dynamic matching of cleaning efficiency and foundation movement state is realized.
[0013] Preferably, when the fouling thickness exceeds the cleaning trigger threshold by a preset proportion threshold of 1.5 times, the high-pressure auxiliary cleaning unit working in parallel with the ultrasonic cleaning device is automatically started until step S1 monitors and stops.
[0014] Preferably, the ballast parameter adjustment of the floating foundation in step S3 is based on the corrected monitoring data P; The floating foundation module in step S3 calculates and executes the adjustment amount ΔM of the ballast parameter according to the deviation of P from the target reference value Pref, and according to the floating foundation posture state reflected by P, so as to optimize the stability and load distribution of the floating foundation.
[0015] Preferably, the fouling influence correction coefficient C in step S3 is obtained through calibration test: In the simulated environment with known fouling thickness D, the deviation of the original data of the sensor from the standard value is calculated C ( - ) / ( × ); is the reference value under the state of no fouling, and With The same working condition measurement data.
[0016] Preferably, the cleaning effect verification standard in step S4 is: After cleaning , The 5% threshold is the allowable error, and the verification is performed during the stable period of the environmental parameters. The reference monitoring value is obtained by a high-precision independent measurement means associated with the data link of the original sensor group in the sensor cleaning state, and the high-precision independent measurement device is a laser radar measurement device.
[0017] Preferably, the closed-loop iteration of step S4 is specifically: The deviation between the dirt prediction model in the verification result and the actual monitoring is converted into the correction value of the dirt accumulation model parameter in step S1 And , and the values of And are dynamically updated accordingly.
[0018] The present application provides a floating foundation adaptive design control method based on real-time monitoring feedback. It has the following beneficial effects: 1. The present application improves the monitoring data accuracy and control reliability. The attitude, environmental load and dirt state data are collected in real time by the structure state monitoring sensor group, combined with the accurate monitoring of the dirt thickness by the capacitive thickness sensor, and the cleaning time, power and duration are dynamically adjusted by the ultrasonic cleaning device. The sensor surface dirt can be effectively removed to ensure that it is always in a stable operating state in complex marine environments.
[0019] 2. The present application obtains a correction coefficient by calibration test to compensate and correct the original data, ensures the high-precision of the monitoring data input into the floating foundation module, provides reliable basis for control decisions such as ballast parameter adjustment, and avoids control deviation caused by dirt interference. BRIEF DESCRIPTION OF DRAWINGS
[0020] Figure 1 The method flowchart of the present application. DETAILED DESCRIPTION
[0021] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings of the present application.
[0022] Please refer to the accompanying Figure 1 The floating foundation adaptive design control method based on real-time monitoring feedback provided by the present application includes the following steps: S1, through the structural state monitoring sensor group on the floating foundation module, for real-time collection of floating foundation posture, environmental load data and dirt state data attached to the surface of the sensor group, providing original data support for subsequent cleaning and control, specifically including: The sensor group consists of: Attitude sensor: including high-precision fiber-optic gyroscope, model KVH1775, measuring the roll angle, pitch angle and yaw angle of the floating foundation; Environmental load sensor: including ultrasonic flowmeter, pressure sensor and wave force sensor; Dirt state sensor: integrating capacitive thickness sensor on the surface of each environmental load sensor to monitor dirt thickness in real time; At the same time, optical camera is matched to assist in identifying dirt type; Data acquisition and preprocessing: The sensor group transmits data to the central controller through industrial Ethernet, PLC model Siemens S7-1200, sampling frequency set to 10Hz; Preprocessing includes data filtering, using Kalman filter to remove high-frequency noise; S2, based on the dirt state data perceived in step S1, starting the ultrasonic cleaning device integrated on the surface of the sensor group, removing dirt by dynamically adjusting the cleaning time and working parameters, and ensuring the monitoring accuracy of the sensor group; In step S1, dirt thickness monitoring and cleaning triggering algorithm is used, the formula is:
[0023] The current sensor surface dirt thickness; The dirt thickness reference value after the last cleaning operation in step S2, when initially installed =0; ; Real-time inclination of the floating foundation; The influence coefficient of flow velocity on dirt accumulation rate; Flow velocity near the sensor; Cumulative running time since the last cleaning operation in step S2; When D≥ , interrupt the monitoring process and trigger the cleaning operation in step S2, The preset cleaning triggering threshold; The working duration of the ultrasonic cleaning device in step S2 is determined by the following formula:
[0024] for cleaning duration; for cleaning efficiency coefficient; for the thickness of the dirt when starting the cleaning operation in step S2; The vibration power of the ultrasonic cleaning device in step S2 is dynamically adjusted according to the vibration intensity of the floating foundation monitored in real time in step S1; When the vibration intensity is greater than the preset threshold, the vibration power is increased to offset the interference of the foundation movement on the cleaning; When the vibration intensity is less than the preset threshold, the vibration power is reduced to reduce the additional disturbance to the stability of the floating foundation, and the dynamic matching of the cleaning efficiency and the foundation movement state is realized; When the thickness of the dirt exceeds the cleaning trigger threshold by a preset proportion threshold of 1.5 times in step S2, the high-pressure auxiliary cleaning unit working in parallel with the ultrasonic cleaning device is automatically started until the monitoring in step S1 stops; Step S2 starts the ultrasonic cleaning device based on the dirt data of S1, which is integrated into the sensor surface model BRANSON8800, and the specific parameters and control logic are as follows: Cleaning opportunity trigger: When the capacitive thickness sensor monitors that the thickness of the dirt reaches the cleaning trigger threshold, the central controller immediately interrupts the current monitoring process, triggers the cleaning instruction, and when the thickness of the dirt exceeds the preset cleaning threshold by 1.5 times, the system will simultaneously start the high-pressure auxiliary cleaning unit, which works in cooperation with the ultrasonic cleaning device, until the thickness of the dirt decreases to below the threshold, which not only ensures the removal of stubborn dirt, but also avoids energy waste; S3, using the sensor group data cleaned in step S2 to correct the monitoring data deviation, inputting the corrected high-precision monitoring data into the floating foundation module for compensating the monitoring deviation caused by the dirt, so as to optimize the state of the floating foundation; The ballast parameter adjustment of the floating foundation in step S3 is based on the corrected monitoring data P; The floating foundation module in step S3 calculates and executes the adjustment amount ΔM of the ballast parameter according to the deviation of P from the target reference value Pref, and according to the attitude state of the floating foundation reflected by P, so as to optimize the stability and load distribution of the floating foundation; The dirt influence correction coefficient C in step S3 is obtained through calibration test: In the simulated environment with known dirt thickness D, the deviation of the original data of the sensor from the standard value is calculated, and C is calculated. - ) / ( × ); is the reference value in the non-fouling state, and and Measured data for the same working conditions; Correction of measurement deviation caused by sensor fouling: During the data correction phase, we first simulated a real ocean environment in the laboratory. We manually applied dirt of varying thicknesses, ranging from 0.1 to 0.8 mm, to the sensor surface. At the same time, we used a calibrated standard measuring instrument to record the exact values under the same operating conditions. The values were then compared to the original sensor measurements. Through repeated experiments, we calculated the deviation patterns corresponding to different dirt thicknesses to form specialized correction parameters. Optimize the floating foundation status based on the corrected accurate data: During the floating foundation control optimization phase, a ballast tank is installed at each corner of the floating foundation. The system has preset safe operating thresholds: roll and pitch angles do not exceed 3°, and the mooring line tension does not exceed 500kN. When the corrected monitoring data indicates that the foundation condition exceeds the threshold, the system activates the PID automatic adjustment algorithm. The PID algorithm outputs a precisely calculated ballast adjustment value ΔM, which is then combined with the current attitude data to calculate the ballast required for equilibrium. S4: Verify the cleaning effect of step S2 and the control adjustment result of step S3, and feed the verification result back to step S1 to optimize the model parameters of state perception and dirt judgment. At the same time, feed it back to S3 to optimize the data correction algorithm parameters, thus achieving closed-loop optimization of the entire process from monitoring to control. Step S1 provides a basis for fouling judgment in step S2. The cleaning effect of step S2 determines the data quality of step S3. The control optimization of step S3 depends on the cleaning accuracy of step S2. The verification result of step S4 feeds back the state perception and fouling judgment model parameters of step S1. Step S4 is to ensure that the cleaning effect meets the standards through independent verification and continuously optimize the model parameters to adapt to environmental changes; The cleaning effect verification phase should be conducted during a period when the environmental parameters are stable, specifically: The water velocity fluctuation does not exceed 0.2m / s, the wave height fluctuation does not exceed 0.1m, and this stable state lasts for more than 10 minutes to avoid environmental interference affecting the verification accuracy; A LiDAR measurement device, model RIEGLVZ-4000, which is independent of the original sensor set and has a ranging accuracy of ±5mm, is used as an independent reference device to measure and record the actual monitoring values when the sensors are in a clean state. The verification criteria are: After cleaning, the deviation between the monitoring data of the original sensor group and the reference monitoring value measured by the laser radar needs to be controlled within 5%; The closed loop iteration link: If the deviation between the predicted result of fouling and the actual situation is found in the verification process, the two key parameters for calculating the fouling thickness in step S1, i.e. the influence coefficient of the inclination of the floating foundation on the accumulation of fouling and the influence coefficient of the water flow velocity on the accumulation of fouling, need to be corrected, so that the corrected parameters are more in line with the actual accumulation law. In order to ensure that the model can dynamically adapt to the long-term changes of the marine environment, the above parameter correction work is carried out once every 24 hours, and through continuous iteration, the fouling prediction model always maintains high precision, providing reliable basis for subsequent cleaning triggering and control optimization.
[0025] Although embodiments of the present application have been shown and described, it is to be understood that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application. The scope of the present application is defined by the appended claims and their equivalents.
Claims
1. A floating foundation adaptive design and control method based on real-time monitoring feedback, characterized in that: The following steps are involved: S1. The structural condition monitoring sensor group on the floating foundation module is used to collect real-time data on the floating foundation posture, environmental load, and dirt status data attached to the surface of the sensor group, providing raw data support for subsequent cleaning and control; S2. Based on the dirt status data sensed in step S1, start the ultrasonic cleaning device integrated on the surface of the sensor group to remove dirt by dynamically adjusting the cleaning timing and working parameters to ensure the monitoring accuracy of the sensor group; S3. Correcting monitoring data deviation using the sensor group data cleaned in step S2, and inputting the corrected high-precision monitoring data into the floating foundation module to compensate for monitoring deviation caused by fouling, and dynamically adjusting ballast foundation structural parameters to optimize the floating foundation state; S4: Verify the cleaning effect of step S2 and the control adjustment result of step S3, and feed the verification result back to step S1 to optimize the model parameters of state perception and dirt judgment. At the same time, feed it back to S3 to optimize the data correction algorithm parameters, thus achieving closed-loop optimization of the entire process from monitoring to control. Step S1 provides a basis for dirt judgment in step S2. The cleaning effect of step S2 determines the data quality of step S3. The control optimization of step S3 depends on the cleaning accuracy of step S2. The verification result of step S4 feeds back the state perception and dirt judgment model parameters of step S1.
2. The method for adaptive design and control of floating foundations based on real-time monitoring feedback according to claim 1 is characterized in that: In step S1, the dirt thickness monitoring and cleaning triggering algorithm is adopted, and the formula is: ； is the current dirt thickness on the sensor surface; The dirt thickness reference value after the most recent cleaning operation in step S2 is completed. =0; ； The real-time inclination angle of the floating foundation; is the influence coefficient of water flow velocity on the dirt accumulation rate; is the water flow velocity near the sensor; is the cumulative running time since the most recent completion of the cleaning operation in step S2; When D≥ When the monitoring process is interrupted immediately and the cleaning operation of step S2 is triggered, It is the preset cleaning trigger threshold.
3. The method for adaptive design and control of floating foundations based on real-time monitoring feedback according to claim 2 is characterized in that: In step S3, a data correction algorithm based on the dirt thickness D is used to correct the original monitoring data of the sensor. Perform compensation correction to obtain the corrected monitoring data P; The data correction algorithm is used to reduce the linear attenuation of the monitoring signal caused by dirt coverage of the sensor group, thereby reducing the impact of dirt D on the monitoring accuracy; By analyzing the dirt thickness before cleaning Corresponding original monitoring data Relative to the standard value The deviation relationship can be used to reversely evaluate the cleaning trigger threshold in step S2. The rationality of the settings and the accuracy of the dirt accumulation model parameters provide feedback for the optimization of the cleaning triggering strategy.
4. The method for adaptive design and control of floating foundations based on real-time monitoring and feedback according to claim 1 is characterized in that: The working time of the ultrasonic cleaning device in step S2 is determined by the following formula: ； For cleaning duration; is the cleaning efficiency coefficient; The thickness of the dirt when the cleaning operation is started in step S2.
5. The method for adaptive design and control of floating foundation based on real-time monitoring feedback according to claim 2 is characterized in that: In step S2, the vibration power of the ultrasonic cleaning device is dynamically adjusted according to the vibration intensity of the floating foundation monitored in real time in step S1; When the vibration intensity is greater than the preset threshold, the vibration power is increased to offset the interference of the basic movement on cleaning; When the vibration intensity is less than a preset threshold, the vibration power is reduced to reduce additional disturbance to the stability of the floating foundation, thereby achieving dynamic matching between the cleaning efficiency and the motion state of the foundation.
6. The method for adaptive design and control of floating foundations based on real-time monitoring feedback according to claim 2 is characterized in that: In step S2, the dirt thickness exceeds the cleaning trigger threshold When the ratio reaches 1.5 times of the preset threshold, the high-pressure auxiliary cleaning unit is automatically started in parallel with the ultrasonic cleaning device until step S1 detects Then stop.
7. The method for adaptive design and control of floating foundations based on real-time monitoring feedback according to claim 3 is characterized in that: In step S3, the ballast parameter adjustment of the floating foundation is performed based on the corrected monitoring data P; The floating foundation module in step S3 calculates and executes the adjustment amount ΔM of the ballast parameter according to the deviation between P and the target reference value Pref and the attitude state of the floating foundation reflected by P, so as to optimize the stability and load distribution of the floating foundation.
8. The method for adaptive design and control of floating foundations based on real-time monitoring and feedback according to claim 3 is characterized in that: The fouling correction coefficient C in step S3 is obtained through a calibration test: Measure the sensor raw data in a simulated environment with known dirt thickness D With standard value Deviation, calculate C ( - ) / ( × ); is the reference value in the non-fouling state, and and The data are measured under the same working conditions.
9. The method for adaptive design and control of floating foundations based on real-time monitoring and feedback according to claim 1, characterized in that: The cleaning effect verification standard in step S4 is: After cleaning , To allow for a 5% error threshold, verification was performed during a period when environmental parameters were stable; It is a benchmark monitoring value obtained by a high-precision independent measurement method associated with the original sensor group data link when the sensor is in a clean state. The high-precision independent measurement equipment is a lidar measurement device.
10. The method for adaptive design and control of floating foundation based on real-time monitoring feedback according to claim 1, characterized in that: The closed-loop iteration of step S4 is specifically as follows: The deviation between the fouling prediction model and the actual monitoring in the verification results is converted into the fouling accumulation model parameters in step S1 and The correction value is updated dynamically accordingly and The value of .