A floating water area floating vessel jacking vibration suppression control method

Abstract

The application discloses a kind of flow water area floating ship jacking vibration suppression control methods, it is related to ship and ocean engineering technical field, according to the conventional environmental parameter of flow water area to determine the basic buoyancy value of floating box, and determine the normal fluctuation range of the buoyancy of floating box, corresponding sensor is respectively set in the key position of floating box, obtain the buoyancy fluctuation data of floating box, establish the corresponding relationship of buoyancy fluctuation and damping adjustment, obtain current buoyancy fluctuation data, according to the control instruction generated in corresponding relationship, continuously collect buoyancy fluctuation data after damping adjustment, and dynamically optimize damping adjustment parameter, the application is by real-time monitoring the buoyancy fluctuation data of key position of floating box, dynamically analyzes buoyancy variation characteristics in combination with environmental parameter, establishes the accurate corresponding relationship between buoyancy fluctuation and damping adjustment parameter, by continuously collecting adjusted buoyancy fluctuation data and dynamically optimizing damping parameter, the safety, accuracy and stability of floating ship jacking operation are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of shipbuilding and marine engineering technology, and in particular to a method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water. Background Technology

[0002] The environment of flowing water is complex and changeable. Dynamic changes in environmental parameters such as water flow speed, wave height and frequency can cause fluctuations in the buoyancy of the pontoon, which in turn causes vibrations during the lifting of the floating vessel. Such vibrations not only reduce the accuracy and stability of the lifting operation, but may also cause fatigue damage to the pontoon structure, hull and operating equipment, and even cause safety accidents. Therefore, how to effectively suppress the vibration during the lifting of the floating vessel in flowing water and ensure the safety and stability of the operation has become a technical problem that urgently needs to be solved in this field.

[0003] Existing technologies lack real-time monitoring and precise analysis of buoyancy fluctuations, making it impossible to dynamically adjust damping parameters based on characteristic parameters such as the amplitude, frequency, and trend of buoyancy fluctuations, resulting in poor vibration suppression. Furthermore, existing technologies fail to establish a precise correspondence between buoyancy fluctuations and damping adjustment parameters, causing damping adjustments to often lag or become excessive, failing to quickly restore system stability and potentially even triggering secondary vibrations due to improper adjustment. They also lack adaptability to different water conditions and loading states, making it difficult to achieve continuous and efficient vibration suppression in complex and variable flowing water environments, thus limiting the reliability and applicability of floating vessel lifting operations. Summary of the Invention

[0004] The technical problem solved by this invention is: the lack of real-time monitoring and accurate analysis of buoyancy fluctuations, making it impossible to dynamically adjust damping parameters based on characteristic parameters such as the amplitude, frequency, and trend of buoyancy fluctuations, resulting in poor vibration suppression effect; at the same time, existing technologies have failed to establish a precise correspondence between buoyancy fluctuations and damping adjustment parameters, making damping adjustment often lagging or excessive, unable to quickly restore system stability, and may even cause secondary vibration due to improper adjustment; and it lacks adaptability to different water conditions and different loading states, making it difficult to achieve continuous and efficient vibration suppression in complex and variable flowing water environments, thus limiting the reliability and applicability of floating vessel lifting operations.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water, comprising the following steps:

[0006] Step S1: Determine the basic buoyancy value of the pontoon based on the conventional environmental parameters of the flowing water area, and determine the normal fluctuation range of the buoyancy of the pontoon.

[0007] Step S2: Install corresponding sensors at key locations of the pontoon, acquire buoyancy fluctuation data of the pontoon through the sensors, and determine whether damping adjustment is required.

[0008] Step S3: Establish the correspondence between buoyancy fluctuations and damping adjustments;

[0009] Step S4: Obtain the current buoyancy fluctuation data. When damping adjustment is required, generate a control command according to the corresponding relationship and adjust the damping adjustment parameters.

[0010] Step S5: Continuously collect buoyancy fluctuation data after damping adjustment and dynamically optimize damping adjustment parameters.

[0011] As a preferred embodiment of the vibration suppression and control method for floating hulls in flowing water as described in this invention, step S1 specifically includes:

[0012] Obtain the conventional environmental parameters of the flowing water area, including the average water depth, daily water flow velocity, common wave height, and common wave frequency of the flowing water area. Determine the basic buoyancy value of the pontoon by using the conventional environmental parameters through on-site mechanical calculations.

[0013] Buoyancy detection devices are deployed at key stress locations of the pontoon to acquire buoyancy change data. These key stress locations include the load-bearing area at the bottom of the pontoon, the side of the pontoon facing the water impact, the buoyancy device interface, and the connection point of the buoyancy device inside the pontoon. By monitoring the buoyancy change data of the pontoon under normal operating conditions, the normal buoyancy fluctuation range of the pontoon is determined. These normal operating conditions include the daily average water depth, normal water flow velocity, common wave height, and no sudden severe weather.

[0014] As a preferred embodiment of the vibration suppression and control method for floating hulls in flowing water as described in this invention, step S2 specifically includes:

[0015] Corresponding sensors are installed at key locations of the pontoon, including the center of the bottom of the pontoon, the bottom edge area of ​​the pontoon, the interface between the side wall of the pontoon and the water surface, the connection node between the pontoon and the hull, and the area near the waterline of the pontoon. The sensors include pressure sensors, displacement sensors, and vibration sensors.

[0016] The buoyancy fluctuation data of the pontoon is acquired through the sensor, and the buoyancy fluctuation data includes the buoyancy fluctuation amplitude, buoyancy fluctuation frequency, and buoyancy fluctuation trend.

[0017] The buoyancy fluctuation data is converted into electrical signals using high-frequency data acquisition technology and transmitted to the control center.

[0018] As a preferred embodiment of the vibration suppression and control method for floating hulls in flowing water as described in this invention, step S2 further includes:

[0019] The data processing module of the control center is used to filter the electrical signal to obtain a first processed electrical signal, and the feature parameters of the first processed electrical signal are extracted by a preset algorithm. The feature parameters include buoyancy fluctuation amplitude parameter, buoyancy fluctuation frequency parameter, and buoyancy fluctuation trend parameter.

[0020] The characteristic parameters are compared with the normal fluctuation range of buoyancy to determine whether damping adjustment is needed.

[0021] As a preferred embodiment of the vibration suppression and control method for floating hulls in flowing water as described in this invention, the filtering process specifically includes:

[0022] The electrical signal is filtered by a hardware filtering circuit to obtain a first filtered electrical signal. The filtering parameters of the first filtered electrical signal are dynamically adjusted by an adaptive algorithm to obtain a second filtered electrical signal. The second filtered electrical signal is then processed by pulse noise removal technology to remove abnormal noise, resulting in a first processed electrical signal. The abnormal noise includes abnormal noise generated by sudden impacts and instantaneous sensor jitter.

[0023] As a preferred embodiment of the floating vessel lifting vibration suppression and control method in flowing water as described in this invention, the step of comparing the characteristic parameters with the normal buoyancy fluctuation range to determine whether damping adjustment is required specifically includes:

[0024] The characteristic parameters are compared with the normal fluctuation range of buoyancy to determine abnormal situations. Abnormal situations include amplitude abnormalities, frequency abnormalities, and trend abnormalities. Damping adjustment is required when abnormal situations occur.

[0025] The normal buoyancy fluctuation range includes the normal range of buoyancy fluctuation amplitude, the normal range of buoyancy fluctuation frequency, and the normal range of buoyancy fluctuation trend.

[0026] The abnormal situations to be identified include:

[0027] Regarding the buoyancy fluctuation amplitude parameter: the buoyancy fluctuation amplitude parameter includes instantaneous buoyancy value, peak buoyancy value, trough buoyancy value, and buoyancy deviation value;

[0028] The buoyancy fluctuation amplitude parameter is compared with the normal range of buoyancy fluctuation amplitude. If the value of at least one parameter in the buoyancy fluctuation amplitude parameter exceeds the normal range of buoyancy fluctuation amplitude, it is determined to be an amplitude abnormality.

[0029] Regarding the buoyancy fluctuation frequency parameters: the buoyancy fluctuation frequency parameters include fluctuation period, fluctuation frequency, and the proportion of the dominant fluctuation frequency;

[0030] The buoyancy fluctuation frequency parameter is compared with the normal range of buoyancy fluctuation frequency. If the value of at least one parameter in the buoyancy fluctuation frequency parameter exceeds the normal range of buoyancy fluctuation frequency, it is determined to be a frequency abnormality.

[0031] Regarding buoyancy fluctuation trend parameters: the buoyancy fluctuation trend parameters include short-term rate of change, cumulative offset, and fluctuation stability index;

[0032] The buoyancy fluctuation trend parameters are compared with the normal range of buoyancy fluctuation trends. If the value of at least one parameter in the buoyancy fluctuation trend parameters exceeds the normal range of buoyancy fluctuation trends, it is determined that the trend is abnormal.

[0033] As a preferred embodiment of the vibration suppression and control method for floating hulls in flowing water as described in this invention, step S3 specifically includes:

[0034] Raw data on buoyancy fluctuations under different water conditions and loading states were obtained, and data on the damper’s suppression effect under different damping adjustment parameters were also obtained.

[0035] The different water conditions include calm water, wavy water, and turbulent water;

[0036] The different loading states include full load, empty load and off-center load;

[0037] The different damping adjustment parameters include the damping coefficient adjustment amount, damping adjustment speed, and damping adjustment duration;

[0038] The suppression effect data includes fluctuation attenuation rate, system stability recovery time, and residual vibration intensity.

[0039] The raw data of buoyancy fluctuation under the same water conditions and loading state and the data of the damper’s suppression effect under different damping adjustment parameters were matched one-to-one to obtain the dataset.

[0040] The dataset is cleaned to remove outliers, and a first dataset is obtained. Statistical analysis is used to classify the buoyancy fluctuations in the first dataset into different buoyancy fluctuation types according to the differences in characteristics and a set combination, and a second dataset is obtained. The buoyancy fluctuations of the same type in the second dataset are analyzed to establish the correspondence between buoyancy fluctuations and damping adjustment.

[0041] The differences in characteristics include different buoyancy fluctuation amplitudes, different buoyancy fluctuation frequencies, and different buoyancy fluctuation trends;

[0042] The buoyancy fluctuation amplitude includes small fluctuations, medium fluctuations, and large fluctuations;

[0043] The buoyancy fluctuation frequency includes high-frequency fluctuations and low-frequency fluctuations;

[0044] The buoyancy fluctuation trend includes an upward trend, a downward trend, and a stable trend;

[0045] The set combination includes a combination of buoyancy fluctuation amplitude, buoyancy fluctuation frequency, and buoyancy fluctuation trend.

[0046] As a preferred embodiment of the floating vessel lifting vibration suppression and control method in flowing water as described in this invention, the step of analyzing the same type of buoyancy fluctuations in the second dataset and establishing the correspondence between buoyancy fluctuations and damping adjustments specifically includes:

[0047] Extract the damping adjustment parameters and suppression effect data corresponding to the same type of buoyancy fluctuations, compare the suppression effect data of the same type of buoyancy fluctuations under different damping adjustment parameters, quantify the suppression effect data, and screen the damping adjustment parameters with the fastest fluctuation decay rate, the shortest system stability recovery time, and the smallest residual vibration intensity among the suppression effect data, and determine the damping adjustment parameters as the optimal damping adjustment parameters for the same type of buoyancy fluctuations.

[0048] As a preferred embodiment of the vibration suppression and control method for floating hulls in flowing water as described in this invention, step S4 specifically includes:

[0049] The system acquires current buoyancy fluctuation data, compares the characteristic parameters of the current buoyancy fluctuation data with the normal buoyancy fluctuation range, and determines whether any abnormalities have occurred. If an abnormality occurs, damping adjustment is required. A control command is generated based on the current buoyancy fluctuation data and the corresponding relationship, and the control command is transmitted to the damper. The damper executes the control command to adjust the damping adjustment parameters and generates a damping force that matches the control command. The damping force suppresses the buoyancy jacking vibration.

[0050] As a preferred embodiment of the vibration suppression and control method for floating hulls in flowing water as described in this invention, step S5 specifically includes:

[0051] Continuously collect buoyancy fluctuation data after damping adjustment to obtain actual suppression effect data. Compare the actual suppression effect data with the preset target suppression effect data. If the actual suppression effect data does not reach the preset target suppression effect data, continuously adjust the damping adjustment parameters until the actual suppression effect data reaches the preset target suppression effect data.

[0052] The beneficial effects of this invention are as follows: By monitoring buoyancy fluctuation data at key locations of the pontoon in real time and dynamically analyzing buoyancy change characteristics in conjunction with environmental parameters, this invention achieves accurate identification and rapid response to pontoon lifting vibrations. It establishes a precise correspondence between buoyancy fluctuations and damping adjustment parameters, automatically generates optimal control commands, and realizes dynamic adjustment of damping parameters. This effectively suppresses vibrations and quickly restores system stability. Simultaneously, by continuously collecting adjusted buoyancy fluctuation data and dynamically optimizing damping parameters, the system can adapt to different water conditions and loading states, significantly improving the continuity and efficiency of vibration suppression. By quantifying suppression effect data and selecting optimal damping parameters, the invention avoids the blindness and lag of traditional experience-based adjustments, reduces structural fatigue damage and the risk of secondary vibrations, ensures the safety, accuracy, and stability of pontoon lifting operations, and expands its applicability in complex flowing water environments. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the steps of a method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water, as provided in one embodiment of the present invention. Detailed Implementation

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0055] Example, refer to Figure 1 As an embodiment of the present invention, a method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water is provided, comprising the following steps:

[0056] Step S1: Determine the basic buoyancy value of the pontoon based on the conventional environmental parameters of the flowing water area, and determine the normal fluctuation range of the buoyancy of the pontoon.

[0057] Step S2: Install corresponding sensors at key locations of the pontoon, acquire buoyancy fluctuation data of the pontoon through the sensors, and determine whether damping adjustment is required.

[0058] Step S3: Establish the correspondence between buoyancy fluctuations and damping adjustments.

[0059] Step S4: Obtain the current buoyancy fluctuation data. When damping adjustment is required, generate control commands according to the corresponding relationship and adjust the damping adjustment parameters.

[0060] Step S5: Continuously collect buoyancy fluctuation data after damping adjustment and dynamically optimize damping adjustment parameters.

[0061] Step S1 specifically includes:

[0062] Obtain the routine environmental parameters of the flowing water area, including the average water depth, daily water flow velocity, common wave height, and common wave frequency. Determine the basic buoyancy value of the pontoon through on-site mechanical calculations based on these routine environmental parameters.

[0063] Buoyancy detection devices are deployed at key stress points of the pontoon to acquire buoyancy change data. Key stress points include the load-bearing area at the bottom of the pontoon, the side of the pontoon facing the water impact, the interface of the buoyancy device, and the connection point of the buoyancy device inside the pontoon. By monitoring the buoyancy change data of the pontoon under normal operating conditions, the normal fluctuation range of the buoyancy of the pontoon is determined. Normal operating conditions include the daily average water depth, ordinary water flow velocity, common wave height, and no sudden severe weather.

[0064] The basic buoyancy value is the benchmark value for the stable bearing capacity of the pontoon in a normal water environment. It is determined by mechanical calculations based on parameters such as average water depth and daily water flow velocity. It serves as the "baseline" for the normal floating of the pontoon. The buoyancy change data is dynamic fluctuation data collected in real time at key locations, reflecting the real-time state of the pontoon affected by water flow, waves, etc. The basic buoyancy value provides a reference standard for the buoyancy change data. Under normal operating conditions, the buoyancy change data should fluctuate within a preset range around the basic buoyancy value. Once it exceeds the range, damping adjustment needs to be activated. At the same time, the basic buoyancy value can help analyze the causes of buoyancy changes and provide a basis for precise control.

[0065] Based on the fundamental buoyancy value determined by conventional environmental parameters and mechanical calculations, the objectivity and accuracy of buoyancy assessment are enhanced, avoiding deviations caused by empirical estimation. By monitoring buoyancy changes in real time at key stress locations, the stress state of the pontoon during actual operation can be accurately identified, effectively improving the sensitivity to abnormal fluctuations and laying the foundation for timely initiation of vibration suppression control.

[0066] Step S2 specifically includes:

[0067] Corresponding sensors are installed at key locations of the pontoon, including the center of the bottom of the pontoon, the bottom edge area of ​​the pontoon, the interface between the side wall of the pontoon and the water surface, the connection node between the pontoon and the hull, and the area near the waterline of the pontoon. The sensors include pressure sensors, displacement sensors, and vibration sensors.

[0068] The buoyancy fluctuation data of the floating box is acquired by sensors. The buoyancy fluctuation data includes the buoyancy fluctuation amplitude, buoyancy fluctuation frequency, and buoyancy fluctuation trend.

[0069] High-frequency data acquisition technology is used to convert buoyancy fluctuation data into electrical signals and transmit them to the control center.

[0070] By installing corresponding sensors at key locations on the pontoon, the subtle dynamic changes of the pontoon in different stress areas can be comprehensively captured. Pressure sensors can monitor fluctuations in water flow impact and load-bearing pressure, displacement sensors can capture the positional shift of the pontoon, and vibration sensors can directly sense the vibration intensity. The combination of these three sensors enables multi-dimensional monitoring of buoyancy fluctuations. By acquiring buoyancy fluctuation data of the pontoon through sensors, the real-time status of the pontoon affected by water flow and waves can be intuitively reflected. High-frequency data acquisition technology converts these data into electrical signals and transmits them to the control center, ensuring the timeliness and integrity of the data, and providing the original basis for subsequent filtering and anomaly judgment.

[0071] Step S2 also includes:

[0072] The data processing module of the control center is used to filter the electrical signal to obtain the first processed electrical signal, and the feature parameters of the first processed electrical signal are extracted by a preset algorithm. The feature parameters include buoyancy fluctuation amplitude parameter, buoyancy fluctuation frequency parameter, and buoyancy fluctuation trend parameter.

[0073] By comparing the characteristic parameters with the normal fluctuation range of buoyancy, it can be determined whether damping adjustment is needed.

[0074] The pre-defined algorithm first performs multi-dimensional analysis on the filtered electrical signal. In the time domain, it extracts amplitude parameters reflecting the strength of fluctuations by calculating indicators such as peak value, valley value, and deviation. In the frequency domain, it converts the time domain signal into frequency domain data using Fourier transform or wavelet transform, identifying the frequency, period, and dominant frequency ratio of the dominant fluctuation to form frequency parameters. Simultaneously, it uses a sliding window technique to perform segmented trend fitting on the signal, extracting trend parameters reflecting the direction of fluctuations by calculating short-term rate of change, cumulative offset, and stability indicators (such as variance). Through parameter extraction in these three dimensions, the continuous electrical signal is transformed into quantifiable feature indicators, providing a concrete basis for subsequent comparison with the normal fluctuation range, and thus determining whether damping adjustment needs to be initiated.

[0075] The data processing module in the control center filters the electrical signals to provide a reliable data foundation for subsequent analysis. By extracting the characteristic parameters of the first processed electrical signals through a preset algorithm, the abstract signals are transformed into quantifiable indicators, clarifying the nature of the buoyancy fluctuations. By comparing the characteristic parameters with the normal fluctuation range of buoyancy, it is possible to accurately determine whether damping adjustment needs to be initiated. This enables a scientific assessment and precise decision-making of the buoyancy vibration state, providing a clear basis for subsequent damping adjustments and ensuring the targeted and effective vibration suppression.

[0076] Filtering specifically includes:

[0077] The first filtered electrical signal is obtained by filtering high-frequency environmental noise of the electrical signal through a hardware filtering circuit. The filtering parameters of the first filtered electrical signal are dynamically adjusted through an adaptive algorithm to obtain a second filtered electrical signal. The second filtered electrical signal is then processed by pulse noise removal technology to remove abnormal noise, resulting in a first processed electrical signal. Abnormal noise includes abnormal noise generated by sudden impacts and instantaneous jitter of the sensor.

[0078] The adaptive algorithm dynamically adjusts the filter cutoff frequency and filter gain by monitoring the frequency components, amplitude change rate, and stability characteristics of the first filtered electrical signal in real time: for residual noise in specific frequency bands, the cutoff frequency is adjusted for precise filtering; for large fluctuations in signal amplitude, the gain is reduced to avoid distortion of the effective signal; for stable signals, the gain is increased to enhance the noise reduction effect, forming a "monitoring-analysis-adjustment" closed loop, and realizing the dynamic adaptation of filter parameters to signal characteristics.

[0079] The high-frequency environmental noise of the electrical signal is filtered by a hardware filtering circuit to initially purify the signal and avoid the impact of high-frequency interference on the basic data. The first filtered electrical signal is dynamically adjusted by an adaptive algorithm to adapt the filtering process to the signal fluctuation characteristics of different water environments, further optimizing the signal quality. The second filtered electrical signal is processed by pulse noise removal technology to remove abnormal noise and eliminate signal distortion caused by transient interference. This effectively solves the problems of noise mixing and insufficient stability in the original electrical signal, ensuring that the first processed electrical signal can truly reflect the actual buoyancy fluctuation state of the pontoon.

[0080] The characteristic parameters are compared with the normal fluctuation range of buoyancy to determine whether damping adjustment is needed. This specifically includes:

[0081] The characteristic parameters are compared with the normal fluctuation range of buoyancy to determine abnormal situations. Abnormal situations include amplitude abnormalities, frequency abnormalities, and trend abnormalities. Damping adjustment is required when abnormal situations occur.

[0082] The normal range of buoyancy fluctuations includes the normal range of buoyancy fluctuation amplitude, the normal range of buoyancy fluctuation frequency, and the normal range of buoyancy fluctuation trend.

[0083] The normal buoyancy fluctuation range is a preset benchmark range based on long-term monitoring data of conventional environmental parameters in flowing water and conventional operating conditions of the pontoon. It covers normal thresholds in three dimensions: amplitude, frequency, and trend, providing an objective reference for anomaly judgment. The three types of anomalies are clearly targeted: amplitude anomalies focus on whether the strength of buoyancy fluctuations exceeds the safe range; frequency anomalies focus on whether the fluctuation rhythm deviates from the normal pattern; and trend anomalies are alert to whether the fluctuations show a continuous deterioration or unstable development direction. The three correspond to different risk characteristics of pontoon vibration.

[0084] Abnormal situations include:

[0085] Regarding the buoyancy fluctuation amplitude parameter: the buoyancy fluctuation amplitude parameter includes instantaneous buoyancy value, peak buoyancy value, trough buoyancy value, and buoyancy deviation value.

[0086] The buoyancy fluctuation amplitude parameter is compared with the normal range of buoyancy fluctuation amplitude. If the value of at least one parameter in the buoyancy fluctuation amplitude parameter exceeds the normal range of buoyancy fluctuation amplitude, it is determined to be an amplitude abnormality.

[0087] Instantaneous buoyancy value reflects the real-time buoyancy state at a certain moment, peak buoyancy reflects the maximum buoyancy value within a certain period, trough buoyancy corresponds to the minimum buoyancy value within the same period, and buoyancy deviation value is the difference between real-time buoyancy and basic buoyancy value.

[0088] During the judgment process, these four parameters are compared with the preset normal range of buoyancy fluctuation amplitude. For example, the instantaneous buoyancy value should be in the range of 800-1200kN, the peak value should not exceed 1300kN, the valley value should not be lower than 700kN, and the deviation value should be controlled within ±200kN. If any parameter exceeds the corresponding threshold (for example, the instantaneous value suddenly increases to 1250kN, the peak value reaches 1350kN, the valley value drops to 680kN, or the deviation value reaches +220kN), regardless of whether other parameters are normal, it will be directly judged as amplitude abnormality. This multi-dimensional judgment method can capture abnormal fluctuations in buoyancy intensity from different angles such as real-time status, extreme intensity, and reference deviation, ensuring that no amplitude abnormality that may affect the stability of the pontoon is missed, and providing a precise triggering basis for subsequent damping adjustment.

[0089] Regarding buoyancy fluctuation frequency parameters: Buoyancy fluctuation frequency parameters include fluctuation period, fluctuation frequency, and the proportion of dominant fluctuation frequency.

[0090] The buoyancy fluctuation frequency parameter is compared with the normal range of buoyancy fluctuation frequency. If the value of at least one parameter in the buoyancy fluctuation frequency parameter exceeds the normal range of buoyancy fluctuation frequency, it is determined to be a frequency anomaly.

[0091] The oscillation period reflects the time interval for buoyancy to complete one undulation change, the oscillation frequency reflects the number of oscillations per unit time, and the oscillation frequency ratio refers to the proportion of energy in the main oscillation frequency range to the total oscillation energy.

[0092] During the judgment process, these three parameters are compared with the preset normal range of buoyancy fluctuation frequency. For example, the fluctuation period should be in the range of 5-15 seconds, the fluctuation frequency should be controlled in the range of 0.07-0.2Hz, and the proportion of the main fluctuation frequency should not be less than 60%. As long as any parameter exceeds the corresponding threshold (such as the period shortens to 3 seconds, the frequency rises to 0.3Hz, or the proportion of the main frequency drops to 50%), regardless of whether other parameters are normal, it will be directly judged as a frequency abnormality. This multi-dimensional judgment method can accurately capture abnormal situations such as excessively fast or slow fluctuation rhythm or disordered energy distribution, ensuring that the buoy is promptly identified when it encounters frequency abnormalities caused by rapid water flow, wave resonance, etc., providing a clear basis for subsequent damping adjustment.

[0093] Regarding buoyancy fluctuation trend parameters: buoyancy fluctuation trend parameters include short-term rate of change, cumulative offset, and fluctuation stability index.

[0094] The buoyancy fluctuation trend parameters are compared with the normal range of buoyancy fluctuation trends. If the value of at least one parameter in the buoyancy fluctuation trend parameters exceeds the normal range of buoyancy fluctuation trends, it is determined that the trend is abnormal.

[0095] The short-term rate of change reflects how fast the buoyancy fluctuation increases or decreases per unit time, while the cumulative offset reflects the cumulative degree of buoyancy deviation from the baseline state over a period of time. Fluctuation stability indicators (such as variance and standard deviation) are used to measure the dispersion and regularity of fluctuations.

[0096] During the judgment process, these three parameters are compared with the preset normal range of buoyancy fluctuation trend. For example, the short-term change rate should be controlled within ±5kN / s, the cumulative deviation should not exceed ±100kN, and the standard deviation of the fluctuation stability index should not exceed 80kN. If any parameter exceeds the corresponding threshold (such as the short-term change rate reaching +7kN / s, the cumulative deviation dropping to -120kN, or the standard deviation of the stability index rising to 90kN), regardless of whether other parameters are normal, it will be directly judged as an abnormal trend. This multi-dimensional judgment method can accurately capture abnormal trends such as continuous enhancement of buoyancy fluctuation, continuous deviation from the benchmark, or decrease in stability. It ensures that the pontoon is promptly identified when it encounters abnormal trends caused by continuous changes in water flow or gradual changes in structural stress, providing a forward-looking decision-making basis for subsequent damping adjustment.

[0097] By comparing three characteristic parameters—buoyancy fluctuation amplitude, buoyancy fluctuation frequency, and buoyancy fluctuation trend—with their corresponding normal ranges, abnormal amplitude, abnormal frequency, and abnormal trend can be accurately identified. If any parameter exceeds the standard, it is judged as an abnormality and damping adjustment is triggered. This mechanism transforms fluctuation data into a clear adjustment signal, avoids subjective bias, ensures timely capture of vibration risks, and improves the timeliness and accuracy of vibration suppression. At the same time, multi-dimensional parameter verification reduces the possibility of misjudgment and provides a reliable decision-making basis for subsequent targeted damping adjustment.

[0098] Step S3 specifically includes:

[0099] Raw data on buoyancy fluctuations under different water conditions and loading states were obtained, and data on the damper's suppression effect under different damping adjustment parameters were also obtained.

[0100] Different water conditions include calm water, wavy water, and turbulent water.

[0101] Different loading states include full load, no load, and partial load.

[0102] Different damping adjustment parameters include the damping coefficient adjustment amount, damping adjustment speed, and damping adjustment duration.

[0103] The suppression effect data include fluctuation decay rate, system stability recovery time, and residual vibration intensity.

[0104] The raw data of buoyancy fluctuations under the same water conditions and loading status and the data of damper suppression effect under different damping adjustment parameters were matched one-to-one to obtain the dataset.

[0105] The dataset is cleaned to remove outliers, resulting in the first dataset. Statistical analysis is used to classify the buoyancy fluctuations in the first dataset into different buoyancy fluctuation types according to their characteristic differences and set combinations, resulting in the second dataset. The buoyancy fluctuations of the same type in the second dataset are analyzed to establish the correspondence between buoyancy fluctuations and damping adjustment.

[0106] Buoyancy fluctuations are categorized into different types based on a set combination. First, the sub-type standards for three dimensions—amplitude, frequency, and trend—are defined: amplitude is divided into small fluctuations, medium fluctuations, and large fluctuations based on a quantification threshold; frequency is defined as high-frequency fluctuations or low-frequency fluctuations based on a range; and trend is determined as an upward trend, a downward trend, or a stable trend based on the direction of the parameters. Based on these three sub-types, a three-dimensional combination matrix of "amplitude × frequency × trend" is constructed to form specific combination types such as "small fluctuations + high-frequency fluctuations + upward trend" and "large fluctuations + low-frequency fluctuations + stable trend". Finally, all fluctuation samples in the dataset are traversed, and the amplitude, frequency, and trend features of each sample are extracted and matched one by one with the type labels in the combination matrix. The samples are then assigned to the corresponding feature combination category, thereby achieving a systematic classification of buoyancy fluctuation types according to a set combination.

[0107] The differences in characteristics include different buoyancy fluctuation amplitudes, different buoyancy fluctuation frequencies, and different buoyancy fluctuation trends.

[0108] Buoyancy fluctuations can be categorized into small, medium, and large fluctuations.

[0109] Buoyancy fluctuation frequencies include high-frequency fluctuations and low-frequency fluctuations.

[0110] Buoyancy fluctuation trends include upward trends, downward trends, and steady trends.

[0111] The set combination includes the combination of buoyancy fluctuation amplitude, buoyancy fluctuation frequency, and buoyancy fluctuation trend.

[0112] By covering different water conditions such as calm, waves, and turbulence, as well as different loading states such as full load, no load, and off-center load, and combining the data collection on the damper's suppression effect under different coefficient adjustment amounts, adjustment speeds, and maintenance times, the comprehensiveness and scenario adaptability of the data are ensured. Through data cleaning to remove outliers, and classifying wave types according to characteristic combinations of amplitude (small / medium / large), frequency (high frequency / low frequency), and trend (rising / falling / stable), the accurate classification of complex wave states is achieved. Finally, by analyzing waves of the same type, a corresponding relationship is established, so that buoyancy waves with different characteristics can be matched with the optimal damping adjustment scheme.

[0113] The analysis of similar buoyancy fluctuations in the second dataset and the establishment of the correspondence between buoyancy fluctuations and damping adjustments specifically include:

[0114] Extract damping adjustment parameters and suppression effect data corresponding to the same type of buoyancy fluctuations. Compare the suppression effect data of the same type of buoyancy fluctuations under different damping adjustment parameters. Quantify the suppression effect data. Select the damping adjustment parameters with the fastest fluctuation decay rate, the shortest system stability recovery time, and the smallest residual vibration intensity from the suppression effect data. Determine the damping adjustment parameters as the optimal damping adjustment parameters for the same type of buoyancy fluctuations.

[0115] Quantitative suppression effect data, fluctuation decay rate: using the fluctuation amplitude at a certain moment as a benchmark (such as the initial amplitude when the anomaly occurs), calculate the reduction ratio of the amplitude per unit time. The formula can be expressed as "(initial amplitude - amplitude at a certain moment) / initial amplitude × 100%", and the result is presented as a percentage (such as 60% decay within 5 seconds). The larger the value, the faster the decay rate.

[0116] System stabilization recovery time: From the start of damping adjustment until the fluctuation amplitude stabilizes within the normal range and there are no fluctuations exceeding the threshold for 30 consecutive seconds, record the duration of this process, quantified in seconds (e.g., 80 seconds). The smaller the value, the higher the recovery efficiency.

[0117] For residual vibration intensity: After the system stabilizes, take the average value of the fluctuation amplitude within 5 minutes, or calculate the standard deviation of the fluctuation amplitude (reflecting the degree of fluctuation dispersion), and express it in a specific force unit (such as kN) (e.g., the average amplitude of residual fluctuation is 15kN). The smaller the value, the weaker the residual vibration.

[0118] "Fastest fluctuation decay rate" means that the rate at which the buoyancy fluctuation amplitude decreases reaches its maximum value per unit time, which can quickly weaken the fluctuation intensity. For example, the fluctuation amplitude of 50kN is reduced more than that under other adjustment parameters in the same time. "Shortest system stability recovery time" emphasizes that the time taken from abnormal fluctuation to the buoy box returning to a stable state is the shortest, which can minimize the duration of the impact of vibration on the structure. "Minimum residual vibration intensity" requires that after the fluctuation subsides, the remaining small vibration amplitude is at the lowest level to avoid the cumulative damage from continuous minor vibrations.

[0119] By directly linking the fluctuation characteristics with specific adjustment parameters, it ensures that each fluctuation type can be matched with a targeted optimal adjustment scheme, providing a scientific and quantifiable decision-making basis for subsequent real-time damping adjustment, and greatly improving the efficiency and accuracy of vibration suppression.

[0120] Step S4 specifically includes:

[0121] The system acquires current buoyancy fluctuation data, compares the characteristic parameters of the current buoyancy fluctuation data with the normal buoyancy fluctuation range to determine if any abnormalities have occurred. If an abnormality occurs, damping adjustment is required. Based on the current buoyancy fluctuation data and the corresponding relationship, a control command is generated and transmitted to the damper. The damper executes the control command to adjust the damping adjustment parameters and generates a damping force that matches the control command. The damping force suppresses the buoyancy jacking vibration.

[0122] The core process of vibration control involves suppressing the buoyancy jacking vibration through damping force. This process utilizes the dissipation of kinetic energy by damping force. When the buoyancy jacking vibration occurs due to factors such as water fluctuations and load changes, the damper adjusts parameters such as the damping coefficient and speed according to control commands. This causes the output damping force to act in the opposite direction to the vibration. When the vibration amplitude increases, the damping force increases accordingly to prevent the vibration from intensifying. When the vibration trend weakens, the damping force is dynamically adjusted to avoid excessive suppression that could trigger new stresses. The damping force acts on the buoyancy jacking structure through mechanical transmission, converting the kinetic energy generated by the vibration into heat energy and other forms of energy for dissipation. This rapidly attenuates the buoyancy fluctuation amplitude, reduces the frequency impact, and curbs the deterioration of the vibration trend, ultimately gradually weakening the jacking vibration intensity to a stable range, thus achieving effective control of vibration risks.

[0123] By acquiring real-time buoyancy fluctuation data and extracting characteristic parameters, and comparing them with preset normal ranges to determine anomalies, vibration risks are identified in a timely manner. When an anomaly occurs, control commands are quickly generated based on the previously established "buoyancy fluctuation-damping adjustment" correspondence, driving the damper to adjust parameters and generate matching damping force. Ultimately, the vibration is directly suppressed through the damping force. This process seamlessly connects data monitoring, anomaly judgment, decision generation, and execution feedback to form a dynamic response mechanism, effectively improving the timeliness and stability of floating vessels in response to vibrations in complex aquatic environments and reducing the potential impact of vibrations on structural safety.

[0124] Step S5 specifically includes:

[0125] Continuously collect buoyancy fluctuation data after damping adjustment to obtain actual suppression effect data. Compare the actual suppression effect data with the preset target suppression effect data. If the actual suppression effect data does not reach the preset target suppression effect data, continuously adjust the damping adjustment parameters until the actual suppression effect data reaches the preset target suppression effect data.

[0126] The preset target suppression effect data are quantitative standards pre-set to evaluate the damping adjustment's effect on buoyancy fluctuations. They are the core basis for judging whether vibration suppression meets the standards. Clear thresholds need to be set for key suppression effect parameters: for example, the fluctuation attenuation rate is specified as "within 30 seconds after the damping adjustment is started, the attenuation ratio of buoyancy fluctuation amplitude is ≥90%", that is, the reduction ratio from the abnormal initial amplitude to 30 seconds must reach more than 90%; the system stabilization recovery time is "the total time from the start of adjustment to the buoyancy fluctuation stabilizing within the normal range for 60 consecutive seconds is ≤50 seconds", to ensure that the vibration subsides quickly; the residual vibration intensity is set as "within 5 minutes after the system stabilizes, the maximum amplitude of buoyancy fluctuation is ≤18kN", limiting the upper limit of the residual vibration intensity.

[0127] Continuously collect buoyancy fluctuation data after damping adjustment. If the floating vessel is in a high-frequency fluctuation environment (such as a wave-dense area), shorten the collection interval (such as collecting 10 times per second) to ensure real-time capture of vibration changes; if the fluctuation is gentle, reduce the frequency (such as collecting once every 5 seconds) to reduce data storage and processing pressure while ensuring monitoring effectiveness and avoiding resource waste.

[0128] By continuously monitoring and verifying the buoyancy fluctuation data after adjustment, the vibration suppression effect is ensured to reach the expected goal, effectively making up for the possible deviations in a single adjustment. This ensures that no matter how precise the initial adjustment is, the ideal vibration suppression effect can be achieved in the end, further improving the system's robustness in dealing with complex fluctuation scenarios.

[0129] This invention achieves accurate identification and rapid response to floating vessel lifting vibrations by real-time monitoring of buoyancy fluctuation data at key locations of the pontoon and dynamically analyzing buoyancy change characteristics in conjunction with environmental parameters. It establishes a precise correspondence between buoyancy fluctuations and damping adjustment parameters, automatically generates optimal control commands, and dynamically adjusts damping parameters to effectively suppress vibrations and quickly restore system stability. Furthermore, by continuously collecting adjusted buoyancy fluctuation data and dynamically optimizing damping parameters, the system can adapt to different water conditions and loading states, significantly improving the sustainability and efficiency of vibration suppression. By quantifying suppression effect data and selecting optimal damping parameters, it avoids the blindness and lag of traditional experience-based adjustments, reduces structural fatigue damage and the risk of secondary vibrations, ensures the safety, accuracy, and stability of floating vessel lifting operations, and expands its applicability in complex flowing water environments.

[0130] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

Claims

1. A method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water, characterized in that, Includes the following steps: Step S1: Determine the basic buoyancy value of the pontoon based on the conventional environmental parameters of the flowing water area, and determine the normal fluctuation range of the buoyancy of the pontoon. Step S2: Install corresponding sensors at key locations of the pontoon, acquire buoyancy fluctuation data of the pontoon through the sensors, and determine whether damping adjustment is required. Step S3: Establish the correspondence between buoyancy fluctuations and damping adjustments; Step S4: Obtain the current buoyancy fluctuation data. When damping adjustment is required, generate a control command according to the corresponding relationship and adjust the damping adjustment parameters. Step S5: Continuously collect buoyancy fluctuation data after damping adjustment and dynamically optimize damping adjustment parameters; Step S3 specifically includes: Raw data on buoyancy fluctuations under different water conditions and loading states were obtained, and data on the damper’s suppression effect under different damping adjustment parameters were also obtained. The different water conditions include calm water, wavy water, and turbulent water; The different loading states include full load, empty load and off-center load; The different damping adjustment parameters include the damping coefficient adjustment amount, damping adjustment speed, and damping adjustment duration; The suppression effect data includes fluctuation attenuation rate, system stability recovery time, and residual vibration intensity. The raw data of buoyancy fluctuation under the same water conditions and loading state and the data of the damper’s suppression effect under different damping adjustment parameters were matched one-to-one to obtain the dataset. The dataset is cleaned to remove outliers, and a first dataset is obtained. Statistical analysis is used to classify the buoyancy fluctuations in the first dataset into different buoyancy fluctuation types according to the differences in characteristics and a set combination, and a second dataset is obtained. The buoyancy fluctuations of the same type in the second dataset are analyzed to establish the correspondence between buoyancy fluctuations and damping adjustment. The differences in characteristics include different buoyancy fluctuation amplitudes, different buoyancy fluctuation frequencies, and different buoyancy fluctuation trends; The buoyancy fluctuation amplitude includes small fluctuations, medium fluctuations, and large fluctuations; The buoyancy fluctuation frequency includes high-frequency fluctuations and low-frequency fluctuations; The buoyancy fluctuation trend includes an upward trend, a downward trend, and a stable trend; The set combination includes a combination of buoyancy fluctuation amplitude, buoyancy fluctuation frequency and buoyancy fluctuation trend; The analysis of similar buoyancy fluctuations in the second dataset and the establishment of the correspondence between buoyancy fluctuations and damping adjustments specifically include: Extract the damping adjustment parameters and suppression effect data corresponding to the same type of buoyancy fluctuations, compare the suppression effect data of the same type of buoyancy fluctuations under different damping adjustment parameters, quantify the suppression effect data, and screen the damping adjustment parameters with the fastest fluctuation decay rate, the shortest system stability recovery time, and the smallest residual vibration intensity among the suppression effect data, and determine the damping adjustment parameters as the optimal damping adjustment parameters for the same type of buoyancy fluctuations.

2. The method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water as described in claim 1, characterized in that: Step S1 specifically includes: Obtain the conventional environmental parameters of the flowing water area, including the average water depth, daily water flow velocity, common wave height, and common wave frequency of the flowing water area. Determine the basic buoyancy value of the pontoon by using the conventional environmental parameters through on-site mechanical calculations. Buoyancy detection devices are deployed at key stress locations of the pontoon to acquire buoyancy change data. These key stress locations include the load-bearing area at the bottom of the pontoon, the side of the pontoon facing the water impact, the buoyancy device interface, and the connection point of the buoyancy device inside the pontoon. By monitoring the buoyancy change data of the pontoon under normal operating conditions, the normal buoyancy fluctuation range of the pontoon is determined. These normal operating conditions include the daily average water depth, normal water flow velocity, common wave height, and no sudden severe weather.

3. The method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water as described in claim 2, characterized in that: Step S2 specifically includes: Corresponding sensors are installed at key locations of the pontoon, including the center of the bottom of the pontoon, the bottom edge area of ​​the pontoon, the interface between the side wall of the pontoon and the water surface, the connection node between the pontoon and the hull, and the area near the waterline of the pontoon. The sensors include pressure sensors, displacement sensors, and vibration sensors. The buoyancy fluctuation data of the pontoon is acquired through the sensor, and the buoyancy fluctuation data includes the buoyancy fluctuation amplitude, buoyancy fluctuation frequency, and buoyancy fluctuation trend. The buoyancy fluctuation data is converted into electrical signals using high-frequency data acquisition technology and transmitted to the control center.

4. The method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water as described in claim 3, characterized in that: Step S2 further includes: The data processing module of the control center is used to filter the electrical signal to obtain a first processed electrical signal, and the feature parameters of the first processed electrical signal are extracted by a preset algorithm. The feature parameters include buoyancy fluctuation amplitude parameter, buoyancy fluctuation frequency parameter, and buoyancy fluctuation trend parameter. The characteristic parameters are compared with the normal fluctuation range of buoyancy to determine whether damping adjustment is needed.

5. The method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water as described in claim 4, characterized in that: The filtering process specifically includes: The electrical signal is filtered by a hardware filtering circuit to obtain a first filtered electrical signal. The filtering parameters of the first filtered electrical signal are dynamically adjusted by an adaptive algorithm to obtain a second filtered electrical signal. The second filtered electrical signal is then processed by pulse noise removal technology to remove abnormal noise, resulting in a first processed electrical signal. The abnormal noise includes abnormal noise generated by sudden impacts and instantaneous sensor jitter.

6. The method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water as described in claim 5, characterized in that: The step of comparing the characteristic parameters with the normal fluctuation range of buoyancy to determine whether damping adjustment is needed specifically includes: The characteristic parameters are compared with the normal fluctuation range of buoyancy to determine abnormal situations. Abnormal situations include amplitude abnormalities, frequency abnormalities, and trend abnormalities. Damping adjustment is required when abnormal situations occur. The normal buoyancy fluctuation range includes the normal range of buoyancy fluctuation amplitude, the normal range of buoyancy fluctuation frequency, and the normal range of buoyancy fluctuation trend. The abnormal situations to be identified include: Regarding the buoyancy fluctuation amplitude parameter: the buoyancy fluctuation amplitude parameter includes instantaneous buoyancy value, peak buoyancy value, trough buoyancy value, and buoyancy deviation value; The buoyancy fluctuation amplitude parameter is compared with the normal range of buoyancy fluctuation amplitude. If the value of at least one parameter in the buoyancy fluctuation amplitude parameter exceeds the normal range of buoyancy fluctuation amplitude, it is determined to be an amplitude abnormality. Regarding the buoyancy fluctuation frequency parameters: the buoyancy fluctuation frequency parameters include fluctuation period, fluctuation frequency, and the proportion of the dominant fluctuation frequency; The buoyancy fluctuation frequency parameter is compared with the normal range of buoyancy fluctuation frequency. If the value of at least one parameter in the buoyancy fluctuation frequency parameter exceeds the normal range of buoyancy fluctuation frequency, it is determined to be a frequency abnormality. Regarding buoyancy fluctuation trend parameters: the buoyancy fluctuation trend parameters include short-term rate of change, cumulative offset, and fluctuation stability index; The buoyancy fluctuation trend parameters are compared with the normal range of buoyancy fluctuation trends. If the value of at least one parameter in the buoyancy fluctuation trend parameters exceeds the normal range of buoyancy fluctuation trends, it is determined that the trend is abnormal.

7. The method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water as described in claim 6, characterized in that: Step S4 specifically includes: The system acquires current buoyancy fluctuation data, compares the characteristic parameters of the current buoyancy fluctuation data with the normal buoyancy fluctuation range, and determines whether any abnormalities have occurred. If an abnormality occurs, damping adjustment is required. A control command is generated based on the current buoyancy fluctuation data and the corresponding relationship, and the control command is transmitted to the damper. The damper executes the control command to adjust the damping adjustment parameters and generates a damping force that matches the control command. The damping force suppresses the buoyancy jacking vibration.

8. The method for suppressing and controlling vibration during the lifting of a floating vessel in flowing water as described in claim 7, characterized in that: Step S5 specifically includes: Continuously collect buoyancy fluctuation data after damping adjustment to obtain actual suppression effect data. Compare the actual suppression effect data with the preset target suppression effect data. If the actual suppression effect data does not reach the preset target suppression effect data, continuously adjust the damping adjustment parameters until the actual suppression effect data reaches the preset target suppression effect data.

Images