Fused owc breakwater adaptive plenum control method and system

Abstract

The application belongs to the technical field of ocean engineering and renewable energy utilization, and relates to a fusion type OWC breakwater adaptive air chamber control method and system. The method comprises the following steps: based on real-time monitoring results of effective wave height, dominant wave period and incident wave direction of the working sea area of the breakwater, a current wave state vector is constructed; the current wave state vector is input into a wave state prediction model to obtain at least one predicted wave state vector; based on the current wave state vector and the predicted wave state vector, a sea state grade and corresponding wave energy characteristic parameters are determined; based on the wave energy characteristic parameters, the sea state grade and the predicted wave state vector, a target value of air chamber structure parameters is determined through a prediction-control coupling mapping relationship, and is cooperatively optimized to generate an air chamber adjustment instruction; and the air chamber width, the forewall draft depth and the forewall fillet radius are adaptively adjusted according to the air chamber adjustment instruction.

Description

Technical Field

[0001] This application belongs to the field of marine engineering and renewable energy utilization technology, and in particular relates to an adaptive air chamber control method and system for integrated OWC breakwaters. Background Technology

[0002] Oscillating Water Column (OWC) wave energy generation devices are a wave energy utilization technology that uses the reciprocating oscillation of air in an air chamber caused by waves to drive an air turbine to generate electricity. Due to its relatively simple structure, reliable operation, and strong environmental adaptability, OWC devices have been applied to a certain extent in port projects such as breakwaters and seawalls, and are gradually becoming an important technical approach for combining port protection structures with wave energy utilization.

[0003] Existing open-circuit current collectors (OWCs) are typically designed with fixed geometric parameters, meaning their chamber width, forewall draft, and wave-facing structural morphology are essentially unadjustable after construction. However, wave characteristics in the actual marine environment exhibit significant time-varying and randomness. Under different wave heights, wave periods, and incident wave directions, the resonance characteristics and energy conversion efficiency of OWCs vary considerably. When actual sea conditions do not match the device's design operating conditions, the device often struggles to maintain an ideal energy absorption state, leading to reduced energy utilization efficiency and potentially increased structural stress and operational risks.

[0004] In existing technologies, some studies achieve power control by adjusting the load of air turbines or generators. However, such methods are mainly passive adjustment methods on the power generation side and cannot dynamically optimize the structural parameters of the air chamber, thus making it difficult to adapt to complex and ever-changing sea conditions.

[0005] In summary, how to achieve adaptive adjustment of the structural parameters of the OWC device's air chamber under actual sea state changes in order to improve wave energy capture efficiency while ensuring structural operational safety has become an urgent technical problem to be solved. Summary of the Invention

[0006] To address the problems of existing OWC breakwaters, such as fixed air chamber structures under complex sea conditions, wave energy conversion efficiency easily affected by changes in wave height, period, and incident direction, low energy utilization, and difficulty in ensuring structural safety, this application proposes an integrated adaptive air chamber control method and system for OWC breakwaters. By real-time sensing of the effective wave height, dominant wave period, and incident wave direction in the sea area in front of the breakwater, combined with short-term wave prediction and extraction of wave energy characteristic parameters, the system achieves online adaptive adjustment of air chamber width, draft of the forewall, and corner radius. Based on real-time monitoring and feedback optimization control of wave energy absorption efficiency, wave dissipation performance, and structural stress state, a closed-loop intelligent control system of "sensing-prediction-control-execution" is constructed. This enables dynamic optimization and adjustment of the air chamber structure, efficient utilization of wave energy, and safe and reliable operation of the breakwater throughout the entire wave energy generation process.

[0007] To achieve the above objectives, the first aspect of this application provides a fusion-type OWC breakwater adaptive air chamber control method, comprising the following steps: Based on the real-time monitoring results of the effective wave height, dominant wave period and incident wave direction in the working sea area of ​​the breakwater, the current wave state vector is constructed. Input the current wave state vector into the wave state prediction model to obtain at least one predicted wave state vector. Determine the sea state level and its corresponding wave energy characteristic parameters based on the current wave state vector and the predicted wave state vector; Based on wave energy characteristic parameters, sea state level and predicted wave state vector, the target values ​​of air chamber width B, front wall draft D and front wall fillet radius R are determined through a preset prediction-control coupling mapping relationship. Based on the coupling relationship between the target values, collaborative optimization is performed to generate air chamber adjustment commands. Based on the air chamber adjustment command, the air chamber width, the front wall draft depth, and the front wall corner radius are adaptively adjusted.

[0008] In some embodiments, the wave state prediction model is constructed as follows: The significant wave height, dominant wave period and incident wave direction in the historical sea state database are preprocessed to obtain a historical wave data sequence. Based on the historical wave data sequence, the evolution trends of effective wave height, dominant wave period, and incident wave direction, as well as their interrelationships, are determined. Based on the evolution trend and parameter correlation, a wave state evolution expression is constructed; The parameters to be determined in the wave state evolution expression are identified to minimize the deviation between the expression calculation result and the historical wave data sequence. The wave state evolution expression is verified based on historical wave data sequences from different time periods, and the expression parameters are corrected according to the verification results to obtain the wave state prediction model.

[0009] In some embodiments, the current wave state vector is constructed as follows: Obtain the current wavefront displacement time series; Based on the current wavefront displacement time series, the effective wave height, dominant wave period, and incident wave direction are obtained; The significant wave height, dominant wave period, and incident wave direction are combined into a three-dimensional column vector. The three-dimensional column vector is standardized to obtain the standardized current wave state vector.

[0010] In some embodiments, the method for determining the sea state level is as follows: Extract the current effective wave height and the predicted effective wave height from the current wave state vector and the predicted wave state vector, respectively. Based on a preset range of significant wave height thresholds, and in conjunction with the current significant wave height, the predicted significant wave height, and their changing trends, sea conditions are classified into multiple levels to determine the corresponding sea condition grades. The sea condition grades include at least Level I, Level II, Level III, Level IV, and Level V, where Level V corresponds to extreme sea conditions. The sea condition grades are dynamically corrected based on whether the predicted significant wave height crosses the threshold of an adjacent grade.

[0011] In some embodiments, the method for extracting wave energy characteristic parameters is as follows: The current wavefront displacement time series and the predicted wavefront displacement time series of the target sea area are integrated to obtain the comprehensive wavefront displacement time series; Based on the comprehensive wavefront displacement time series, the total wave energy and power spectral density function are obtained; Extract the peak frequency and corresponding peak energy from the power spectral density function; Obtain the current air cell structure parameters of the integrated OWC breakwater, determine the corresponding current OWC air cell resonance frequency based on the current air cell structure parameters, and perform matching analysis between the spectral peak frequency and the current OWC air cell resonance frequency to obtain the frequency matching coefficient. The spectral width parameter is calculated based on the power spectral density function; Frequency-direction joint analysis was performed on the predicted wavefront displacement time series to obtain the directional energy distribution ratio; By summarizing the total wave energy, peak energy, spectral width parameter, frequency matching coefficient, and directional energy distribution ratio, a set of wave energy characteristic parameters is obtained.

[0012] In some embodiments, the method for generating the air chamber adjustment command is as follows: Wave energy characteristic parameters, sea state level, and predicted wave state vector are input into a pre-constructed prediction-control coupling mapping relationship to calculate the target values ​​of air chamber width B, front wall draft D, and front wall fillet radius R, respectively. The prediction-control coupling mapping relationship is obtained through joint training of historical sea state data, numerical simulation results, and real-time operation data. The target values ​​of air chamber width B, front wall draft D, and front wall fillet radius R are encapsulated as preliminary control commands. Determine whether the initial control commands meet the preset physical constraints, and optimize the settings of unmet target values ​​to bring them within a feasible range; Based on the coupling relationship between the optimized air chamber width B, the front wall draft D, and the front wall fillet radius R, the parameter collaborative optimization strategy and the adjustment execution sequence are determined, and a structured air chamber adjustment command is generated accordingly.

[0013] In some embodiments, the adaptive air chamber control method further includes the following steps: During the adjustment process, the wave energy absorption efficiency, wave dissipation coefficient and structural stress state of the breakwater are monitored in real time, and the monitoring results are recorded as real-time performance data. Based on real-time performance data, determine whether the adaptive adjustment has reached the preset performance threshold set: if not, optimize and update the air chamber adjustment command based on real-time performance data and execute it until the preset performance threshold set is reached; if it has been reached, maintain the current air chamber adjustment command and continuously monitor the wave state changes.

[0014] In some embodiments, the method for optimizing and updating the air chamber adjustment command based on real-time performance data is as follows: The system acquires real-time performance data during the operation of the air chamber and compares it with a preset performance threshold. When the real-time performance data is lower than the preset performance threshold, it generates performance deviation information. Based on performance deviation information, predictive output data and corresponding observation data characterizing the gas chamber operating status are acquired, and a difference analysis is performed on the predictive output data and corresponding observation data within a continuous preset time period. When the deviation between the predictive output data and the corresponding observation data is within a preset allowable error range and exhibits random fluctuations, it is determined that the performance deviation is mainly caused by wave randomness and does not trigger online updates. When the deviation between the predictive output data and the corresponding observation data exceeds the preset allowable error range for a first set time within a continuous time period, the source type of the performance deviation is determined. The corresponding online update path is selected based on the source type of performance deviation: when the performance deviation source is wave state prediction error, the prediction model update path is entered to adaptively update the parameters of the wave state prediction model; when the performance deviation source is prediction-control coupling mapping error, the mapping relationship update path is entered to self-learn and update the prediction-control coupling mapping relationship function. Based on the updated wave state prediction model or prediction-control coupling mapping relationship, the air chamber regulation command is corrected to obtain the updated air chamber regulation command.

[0015] A second aspect of this application provides an integrated OWC breakwater adaptive air chamber control system for implementing the adaptive air chamber control method disclosed in the first aspect, comprising a sensing layer, a decision layer, and an execution layer, wherein... The perception layer is used to monitor the effective wave height, dominant wave period and incident wave direction in the working sea area of ​​the breakwater in real time, and to construct a standardized current wave state vector. The decision-making layer is configured to: generate at least one predicted wave state vector based on the standardized current wave state vector; extract corresponding wave energy characteristic parameters based on the standardized current wave state vector and the predicted wave state vector; and determine the structured air chamber adjustment command based on the predicted wave state vector and the wave energy characteristic parameters. The execution layer continuously or discretely adjusts the air chamber width, front wall draft depth, and corner radius according to the air chamber adjustment command; The system works collaboratively through a perception layer, a decision-making layer, and an execution layer to form a closed-loop adaptive control system of "perception-prediction-feature extraction-parameter mapping-optimization decision-execution-feedback update".

[0016] In some embodiments, the execution layer includes a dynamic adjustment device for the air chamber width, an electric lifting mechanism for the front wall draft, and a deformable structure for the front wall fillet radius, wherein: The air chamber width dynamic adjustment device is configured to switch the air chamber width between multiple preset discrete positions; The electric lifting mechanism for the front wall draft is configured to continuously adjust the front wall draft in the vertical direction. The deformable structure with a rounded corner radius of the front wall is configured to continuously adjust the rounded corner radius of the front wall within a predetermined adjustable range, and to adjust it in coordination with the air chamber width and the draft depth of the front wall.

[0017] Compared with existing technologies, the advantages and positive effects of this application are as follows: This application optimizes the energy absorption efficiency and structural performance of the breakwater by predicting real-time wave conditions, extracting wave energy characteristics, and achieving continuous or discrete adaptive adjustment of the air chamber width, front wall draft, and corner radius based on prediction-control coupling mapping. This application achieves coordinated adaptive optimization of three structural parameters—air chamber width, front wall draft, and front wall corner radius—rather than existing technologies that only adjust the power generation load or a single structural parameter, thereby improving wave energy absorption efficiency, wave dissipation performance, and structural operational stability under complex sea conditions. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an adaptive air chamber control method for an integrated OWC breakwater disclosed in an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of the structure of the air chamber width dynamic adjustment device disclosed in the embodiments of this application.

[0020] Figure 3 This is a schematic diagram of the electric lifting mechanism for the front wall draft disclosed in an embodiment of this application.

[0021] Figure 4 This is a schematic diagram of the deformable front wall fillet radius structure disclosed in an embodiment of this application. Detailed Implementation

[0022] The present application will now be described in detail through exemplary embodiments. However, it should be understood that, without further description, elements, structures, and features in one embodiment may be advantageously incorporated into other embodiments.

[0023] like Figure 1 As shown, an adaptive air chamber control method for fusion-type OWC breakwaters includes the following steps: Obtain the structural parameters of the integrated OWC breakwater and its historical sea condition database for the working sea area; Based on the historical sea condition database of the breakwater working area, a wave state prediction model is constructed. Based on the real-time monitoring results of the effective wave height, dominant wave period and incident wave direction in the working sea area of ​​the breakwater, the current wave state vector is constructed. Input the current wave state vector into the wave state prediction model to obtain at least one predicted wave state vector. Determine the sea state level and its corresponding wave energy characteristic parameters based on the current wave state vector and the predicted wave state vector; Based on wave energy characteristic parameters, sea state level and predicted wave state vector, the target values ​​of air chamber structural parameters are determined, and air chamber adjustment commands are generated based on the target values. Based on the air chamber adjustment command, the air chamber width, the front wall draft depth, and the front wall corner radius are adaptively adjusted. During the adjustment process, the wave energy absorption efficiency, wave dissipation coefficient and structural stress state of the breakwater are monitored in real time, and the monitoring results are recorded as real-time performance data. Based on real-time performance data, determine whether the adaptive adjustment has reached the preset performance threshold set: if not, optimize and update the air chamber adjustment command based on real-time performance data and execute it until the preset performance threshold set is reached; if it has been reached, maintain the current air chamber adjustment command and continuously monitor the wave state changes.

[0024] The aforementioned integrated OWC breakwater adaptive air chamber control method of this application acquires breakwater structural parameters and a historical sea state database of the working sea area to construct a wave state prediction model, thereby achieving wave state perception and prediction. Based on real-time monitoring data of the breakwater's working sea area, including significant wave height, dominant wave period, and incident wave direction, a current wave state vector is constructed and input into the wave state prediction model to obtain a predicted wave state vector. Based on the current wave state vector and the predicted wave state vector, the sea state level is determined and the corresponding wave energy characteristic parameters are extracted. According to the wave energy characteristic parameters, sea state level, and predicted wave state vector, the target values ​​of the air chamber structural parameters are determined, and the air chamber is generated based on the target values. The system adaptively adjusts the air chamber width, front wall draft, and corner radius. During adjustment, it monitors the breakwater's wave energy absorption efficiency, wave dissipation coefficient, and structural stress state in real time and records this as real-time performance data. Based on the real-time performance data, it determines whether the adaptive adjustment has reached a preset performance threshold set. If not, the air chamber adjustment command is optimized, updated, and executed until the preset performance threshold set is reached. If it has been reached, the current adjustment command is maintained, and wave state changes are continuously monitored. On this basis, the system achieves continuous, real-time, and performance optimization management of the air chamber adjustment process, thereby improving the breakwater's wave dissipation efficiency, reducing structural stress fluctuations, and ensuring long-term stable operation under complex sea conditions.

[0025] In some embodiments, the structural parameters of the integrated OWC breakwater include: adjustable settings for the air chamber width, boundary values ​​for the adjustment range of the draft of the front wall, deformation limits for the radius of the front wall fillet, overall geometric dimensions of the breakwater, azimuth settings for the air chamber openings, rated power and rated speed of the turbine, and mechanical property parameters of the structural materials.

[0026] In some embodiments, the historical sea state database adopts a hybrid storage architecture of MySQL relational database and InfluxDB time series database, which are used to store structured parameters and high-frequency time series data respectively, and establish a fast indexing mechanism based on a two-dimensional joint distribution of wave height and period, so that the query response time is controlled within 1 second.

[0027] In some embodiments, constructing a wave state prediction model based on the historical sea state database includes the following steps: Preprocessing of the significant wave height, dominant wave period and incident wave direction in the historical sea state database includes outlier removal, missing value completion and time series alignment to obtain a continuous and consistent historical wave data sequence. Based on the historical wave data sequence, the evolution trends of effective wave height, dominant wave period, and incident wave direction, as well as their interrelationships, are determined. Based on the evolution trend and parameter correlation, a wave state evolution expression is constructed; The parameters to be determined in the wave state evolution expression are identified to minimize the deviation between the expression calculation result and the historical wave data sequence. The wave state evolution expression is verified based on historical wave data sequences from different time periods, and the expression parameters are corrected according to the verification results to obtain the wave state prediction model.

[0028] Specifically, based on the verification results, the undetermined parameters in the wave state evolution expression are adaptively corrected online using a recursive least squares algorithm to adapt to changes in sea state.

[0029] The wave state prediction method based on the historical sea state database described in this application preprocesses the significant wave height, dominant wave period, and incident wave direction in the historical sea state database, including outlier removal, missing value completion, and time series alignment, to obtain a continuous and consistent historical wave data sequence. Based on the historical wave data sequence, the evolution trend and interrelationship of significant wave height, dominant wave period, and incident wave direction are analyzed, and a wave state evolution expression is constructed accordingly. The undetermined parameters in the wave state evolution expression are identified to minimize the deviation between the expression calculation result and the historical wave data sequence. The expression is then verified based on historical data from different time periods, and the expression parameters are corrected according to the verification results, ultimately yielding a wave state prediction model that can be used for short-term prediction. Based on this, accurate prediction and continuous management of wave state in the breakwater working area are achieved, thus providing reliable data support for air chamber regulation and real-time adaptive control of the breakwater, and ensuring the long-term stable operation of the system under complex sea conditions.

[0030] In some embodiments, the current wave state vector is constructed as follows: At least two wave buoys are deployed in the sea area in front of the breakwater. Each wave buoy is equipped with an acceleration sensor and an tilt sensor to acquire wave motion signals. The acceleration and attitude signals of the wave buoy are integrated to obtain the current wave surface displacement time series; Based on the current wavefront displacement time series, the effective wave height, dominant wave period, and incident wave direction are obtained; The significant wave height, dominant wave period, and incident wave direction are combined into a three-dimensional column vector. The three-dimensional column vector is standardized by normalizing the ratio of the effective wave height to the design maximum wave height, normalizing the ratio of the dominant wave period to the maximum period, and normalizing the incident wave direction over a 360-degree range. At the same time, timestamp information and data quality identifiers are added to the three-dimensional column vector to obtain the standardized current wave state vector.

[0031] Specifically, the method for obtaining the effective wave height is as follows: Individual wave heights are extracted from the wave surface displacement sequence using the zero-point crossing method. All individual wave heights are then sorted by their magnitude, and the average of the highest third of these individual wave heights is taken as the effective wave height for the current sea area. This method can effectively filter out abnormal fluctuations and accurately reflect the overall energy characteristics of waves over short periods, providing fundamental parameters for subsequent air chamber regulation.

[0032] Specifically, the method for obtaining the dominant wave period is as follows: A Fast Fourier Transform is performed on the wavefront displacement time series to convert the time-domain signal to the frequency domain for analysis. The dominant frequency is identified through the spectral amplitude distribution. The dominant wave period is defined as the period corresponding to the maximum amplitude in the spectrum, calculated by the reciprocal of the frequency. This method can distinguish multiple frequency components in complex wave signals and accurately extract the most representative wave period, thus providing targeted time-domain reference information for breakwater air chamber adjustment strategies.

[0033] Specifically, the method for obtaining the incident wave direction is as follows: using wavefront displacement time series and buoy attitude data, the spatial distribution characteristics of ocean waves are modeled through maximum entropy spectral analysis. The maximum entropy method, with limited statistical information as constraints, derives the least biased wave direction spectrum, thereby estimating the main incident direction and directional dispersion of the wave. The incident wave direction characterizes the main propagation direction of wave energy, while the directional dispersion reflects the distribution width of wave energy in different directions. These parameters can accurately describe the spatial characteristics of waves, providing crucial basis for adaptively adjusting the air chamber opening direction, forewall angle, and energy absorption strategies.

[0034] The method described in this application for constructing the current wave state vector involves deploying at least two wave buoys in the sea area in front of the breakwater, each equipped with an accelerometer and an inclination sensor, to achieve real-time acquisition of wave motion signals. Based on the acquired acceleration and attitude signals, the current wave surface displacement time series is obtained through integration. The significant wave height is obtained using the zero-crossing method, the dominant wave period is extracted using fast Fourier transform, and the incident wave direction and directional dispersion are estimated using the maximum entropy method. The significant wave height, dominant wave period, and incident wave direction are combined into a three-dimensional column vector and standardized. Specifically, the ratio of the significant wave height to the design maximum wave height is normalized, the ratio of the dominant wave period to the maximum period is normalized, and the incident wave direction is mapped to a 360-degree range. Simultaneously, timestamp information and data quality identifiers are added to the three-dimensional column vector to obtain a standardized current wave state vector. Based on this, accurate perception and continuous management of the wave state in the breakwater's working area are achieved, providing reliable data support for subsequent wave prediction and air chamber adaptive adjustment, and ensuring the long-term stable operation of the system under complex sea conditions.

[0035] In some embodiments, determining the corresponding sea state level based on the current wave state vector and the predicted wave state vector includes the following steps: The current significant wave height and the predicted significant wave height are extracted from the current wave state vector and the predicted wave state vector, respectively, and used as the main parameters for determining the sea state level. Based on the preset significant wave height threshold range and combined with the changing trends of the current and predicted significant wave heights, sea states are classified into four levels: Level I, Level II, Level III, Level IV, and Level V. Level I corresponds to low-energy sea states, Level II corresponds to general sea states, Level III corresponds to moderate sea states, Level IV corresponds to high-energy sea states, and Level V corresponds to extreme sea states. Different sea state levels correspond to different air chamber structure parameter adjustment strategies.

[0036] Specifically, low-energy sea states are characterized by: almost no wind or a light breeze, a smooth sea surface with only fine ripples, no obvious wave crests, wave height <0.3m, extremely low seawater kinetic energy, and no swells. Normal sea states are characterized by: gentle breezes, small, short waves on the sea surface, wave crests without foam, wave height 0.3–1.0m, a small number of low-frequency swells, and overall moderate energy. Moderate sea states are characterized by: persistent sea winds, numerous white, broken waves, clearly visible swells, wave height 1.0–2.5m, significant water kinetic energy, and continuous slight rocking of the ship. High-energy sea states are characterized by: strong winds, large areas of broken white waves, long-period swells superimposed with wind waves, wave height 2.5–6m, strong water impact, and violent sea surface fluctuations. Extreme sea states are characterized by: storms / hurricanes, towering waves, wave height >6m, multiple layers of wind waves, swells, and broken waves superimposed, completely broken sea surface, enormous seawater impact kinetic energy, accompanied by storm surge.

[0037] The sea state level is determined not only based on the current significant wave height, but also dynamically corrected by taking into account the trend of the predicted significant wave height. When the predicted significant wave height crosses the threshold of the adjacent sea state level, the system switches to the corresponding sea state level in advance and generates a chamber regulation strategy based on the switched sea state level to achieve predictive regulation.

[0038] The method described in this application for determining sea state levels based on the current wave state vector and the predicted wave state vector extracts the significant wave height from both vectors and uses it as the main parameter for sea state level determination. Combining a preset significant wave height threshold range and the trend of significant wave height variation, the method classifies sea states into four levels: Level I, Level II, Level III, Level IV, and Level V. Level I corresponds to low-energy sea states, Level II to general sea states, Level III to moderate sea states, Level IV to high-energy sea states, and Level V to extreme sea states. Different sea state levels correspond to different air chamber structure parameter adjustment strategies. Based on this, the method enables real-time determination of the sea state level in the breakwater's working area, providing accurate data for wave energy feature extraction and adaptive air chamber adjustment, and ensuring the continuous and stable operation of the system under complex sea conditions.

[0039] In some embodiments, the method for extracting wave energy characteristic parameters is as follows: The current wavefront displacement time series and the predicted wavefront displacement time series of the target sea area are integrated to obtain the comprehensive wavefront displacement time series; The variance of the composite wave surface displacement time series is calculated to obtain the total wave energy, which is then converted into wave energy per unit area of ​​sea surface. The power spectral density function is obtained by performing a fast Fourier transform on the composite wavefront displacement time series. The peak frequencies and corresponding peak energies are extracted from the power spectral density function to reflect the energy concentration of the dominant wave components. The current air chamber structure parameters of the integrated OWC breakwater are obtained. Based on the current air chamber structure parameters, the corresponding current OWC air chamber resonance frequency is determined. The spectral peak frequency is matched with the current OWC air chamber resonance frequency to obtain the frequency matching coefficient. The frequency matching coefficient is used to characterize the degree of matching between the wave dominant frequency and the current OWC air chamber resonance frequency under the current air chamber structure state, and to evaluate the synchronicity between the wave dominant frequency and the air chamber dynamic response under the current air chamber structure state. The spectral width parameter is calculated based on the power spectral density function to measure the dispersion of wave energy in the frequency domain and to evaluate the impact of the spectral width parameter on the operational stability of the integrated OWC breakwater. Frequency-direction joint analysis was performed on the predicted wavefront displacement time series to obtain the directional energy distribution ratio; By summarizing the total wave energy, peak energy, spectral width parameter, frequency matching coefficient, and directional energy distribution ratio, a set of wave energy characteristic parameters is obtained.

[0040] The wave energy characteristic parameter extraction method described in this application integrates the current wave surface displacement time series and the predicted wave surface displacement time series of the target sea area to obtain a comprehensive wave surface displacement time series. Based on this series, the variance is calculated to obtain the total wave energy, which is then converted into wave energy per unit area of ​​sea surface. A fast Fourier transform is performed on the comprehensive wave surface displacement time series to obtain the power spectral density function, and the peak frequencies and corresponding peak energies are extracted from it to reflect the energy concentration of the dominant wave components. Furthermore, the peak frequencies are matched with the OWC (Overflow-Cooled Cell) resonant frequencies to obtain frequency matching coefficients, evaluating the wave energy and dynamic response of the OWC under the current OWC structural state. The system achieves synchronous operation; it calculates the spectral width parameter based on the power spectral density function to measure the dispersion of wave energy in the frequency domain and evaluates its impact on the operational stability of the integrated OWC breakwater; it also performs frequency-direction joint analysis on the predicted wave surface displacement time series to obtain the directional energy distribution ratio; finally, it summarizes the total wave energy, spectral peak energy, spectral width parameter, frequency matching coefficient, and directional energy distribution ratio to obtain a complete set of wave energy characteristic parameters; based on this, it achieves accurate quantification of the wave energy characteristics of the breakwater, provides a reliable basis for the adjustment and adaptive control of the air chamber structure parameters, and ensures the long-term stable operation of the system under complex sea conditions.

[0041] In some embodiments, based on wave energy characteristic parameters, sea state level, and predicted wave state vector, target values ​​for the air chamber width B, forewall draft D, and forewall fillet radius R are determined through a preset prediction-control coupling mapping relationship. Then, based on structural constraints and parameter coupling relationships, collaborative optimization is performed to generate air chamber adjustment commands, including the following steps: Wave energy characteristic parameters, sea state level, and predicted wave state vector are input into a pre-constructed prediction-control coupling mapping relationship to calculate the target values ​​of air chamber width B, front wall draft D, and front wall fillet radius R, respectively. The prediction-control coupling mapping relationship function is obtained through joint training of historical sea state data, numerical simulation results, and real-time operation data.

[0042] Specifically, the process of training the prediction-control coupling mapping function includes: collecting historical sea state data, CFD numerical simulation results, and real-time operation data of the fused OWC breakwater, and constructing a training sample set; establishing an input-output mapping relationship between wave state characteristics, wave energy characteristics, and air chamber structure parameters based on the training sample set; solving the parameter matrix Θ in the mapping relationship using the least squares identification method to obtain the initial parameters of the prediction-control coupling mapping function; and updating the parameter matrix Θ online using the recursive least squares (RLS) algorithm based on real-time operation data during system operation to improve the adaptive capability of the mapping relationship to sea state changes.

[0043] The prediction-control coupling mapping function is: , It is the target vector of the air chamber structure parameters that predicts the wave state vector; The mapping relationship is as follows: ,in: , , , , E =[ E w E p M f S b P d ] T ; In the formula, Φ ( W ) is an extended eigenvector with cross terms and nonlinear terms; Θ is a mapping parameter matrix, determined through historical data and online recursive updates. Indicates the first The weight of the influence of the row on the width B of the air chamber. , Indicates the first The weight of the influence of the row on the draft D of the front wall Indicates the first The weight of the influence of the row on the radius R of the front wall fillet; Wave state feature vector This represents the current wave state vector. These are wave energy characteristic parameters. Sea state rating Indicates the current significant wave height. Indicates the current dominant wave cycle. Indicates the current direction of the incident wave. It is the transpose symbol. E w The total energy of the wave. E p For peak energy, M f For frequency matching coefficients, S b For spectral width parameter, P d The proportion of energy distribution in the direction; This indicates that wave height and period together determine the wave energy input; This indicates that wave height and frequency matching together affect the air chamber width; This indicates that the period and resonance matching together affect the draft. / This approximates the steepness of the wave and is suitable for adjusting the radius of the fillet. This represents the interaction term between the spectral width parameter and the proportion of directional energy distribution.

[0044] Specifically, sea states are divided into four levels: Level I, Level II, Level III, Level IV, and Level V. Level I corresponds to low-energy sea states, Level II corresponds to general sea states, Level III corresponds to moderate sea states, Level IV corresponds to high-energy sea states, and Level V corresponds to extreme sea states. Different sea state levels correspond to different air chamber structure parameter adjustment strategies.

[0045] The target values ​​of air chamber width B, front wall draft depth D, and front wall fillet radius R are encapsulated as preliminary control commands. It is then determined whether these commands meet the preset physical constraints. For targets that do not meet the constraints, the settings are optimized to bring them within a feasible range. For example, the air chamber width B is within multiple preset discrete ranges, the front wall draft depth D is within the range of -1.5m to -2.5m, and the front wall fillet radius R is within the range of 0m to 0.4m. Based on the optimized coupling relationship between the air chamber width B, the draft of the front wall D, and the radius of the front wall corner R, a parameter collaborative optimization strategy and adjustment execution sequence are determined, and a structured air chamber adjustment instruction is generated accordingly. The air chamber adjustment instruction includes at least the parameter name, current value, target value, adjustment rate, priority, and completion time limit information. The coupling relationship represents the mutual influence and constraint relationship between the air chamber width B, the draft of the front wall D, and the radius of the front wall corner R.

[0046] The method for generating air chamber adjustment commands described in this application calculates target values ​​for air chamber width, forewall draft, and forewall corner radius by inputting the predicted wave state vector into the prediction-control coupling mapping relationship. These values ​​are then encapsulated into preliminary commands within a feasible range. The adjustment sequence and coordination strategy are determined by combining the interactions of various parameters, generating structured air chamber adjustment commands that include parameter names, current values, target values, adjustment rates, priorities, and completion deadlines. Based on this, precise and coordinated adjustment of the air chamber structure is achieved, providing support for the real-time adaptive operation of integrated OWC breakwaters under complex sea conditions.

[0047] In some embodiments, the method for monitoring wave energy absorption efficiency is as follows: Based on the air pressure change data in the air chamber of the fused OWC breakwater, air pressure characteristic parameters characterizing the air pressure oscillation amplitude were obtained; Based on the air pressure characteristic parameters and the total wave energy, the energy capture parameters of the air chamber are obtained; The wave energy absorption efficiency is obtained based on the energy capture parameters and the total wave energy.

[0048] The wave energy absorption efficiency monitoring method described in this application collects air pressure change data in the air chambers of the integrated OWC breakwater and extracts air pressure characteristic parameters that characterize the air pressure oscillation amplitude; calculates the air chamber energy capture parameters based on the air pressure characteristic parameters and the total wave energy; and obtains the wave energy absorption efficiency by using the ratio of the energy capture parameters to the total wave energy. On this basis, it realizes real-time quantification of the wave energy utilization effect of the breakwater and provides reliable feedback for the adaptive adjustment of the air chamber.

[0049] In some embodiments, the method for monitoring the wave dissipation coefficient is as follows: Simultaneously collect incident wave timing data on the wave-facing side of the breakwater and transmitted wave timing data on the wave-shielding side. Time alignment processing is performed on the incident wave time series data and the transmitted wave time series data to obtain aligned incident wave time series data and transmitted wave time series data. The aligned incident wave time series data and transmitted wave time series data are subjected to spectral decomposition, and the wave reduction coefficients corresponding to each frequency component are calculated respectively. Arrange the wave suppression coefficients corresponding to each frequency component in order of frequency to obtain the frequency distribution characteristics of the wave suppression coefficients. Based on the wave dissipation coefficient and its frequency distribution characteristics, the wave dissipation performance of the breakwater is comprehensively evaluated, and the wave dissipation performance evaluation results are obtained.

[0050] The wave dissipation coefficient monitoring method described in this application involves simultaneously collecting wave time-series data of incident waves on the wave-facing side and transmitted waves on the wave-receiving side of the breakwater, and performing time alignment processing on them. The aligned incident and transmitted wave time-series data are then subjected to spectral decomposition to calculate the wave dissipation coefficient corresponding to each frequency component, and the frequency distribution characteristics of the wave dissipation coefficient are obtained by arranging them in frequency order. Based on the wave dissipation coefficient and its frequency distribution, the wave dissipation performance of the breakwater is comprehensively evaluated to obtain the wave dissipation performance evaluation result. Furthermore, the real-time quantification of the breakwater's wave dissipation efficiency is achieved, providing precise feedback for adaptive air chamber adjustment.

[0051] In some embodiments, the wave dissipation coefficient is calculated as follows: Based on the aligned incident wave time series data and transmitted wave time series data, the corresponding incident wave effective wave height and transmitted wave effective wave height are extracted respectively. The wave-damping coefficient of the breakwater is calculated based on the ratio of the effective wave height of the transmitted wave to the effective wave height of the incident wave.

[0052] The wave dissipation coefficient monitoring method described in this application involves simultaneously collecting wave time-series data of incident waves on the wave-facing side and transmitted waves on the wave-receiving side of the breakwater, and performing time alignment processing on them. The aligned incident and transmitted wave time-series data are then subjected to spectral decomposition to calculate the wave dissipation coefficient corresponding to each frequency component, and the frequency distribution characteristics of the wave dissipation coefficient are obtained by arranging them in frequency order. Based on the wave dissipation coefficient and its frequency distribution, the wave dissipation performance of the breakwater is comprehensively evaluated to obtain the wave dissipation performance evaluation result. Furthermore, the real-time quantification of the breakwater's wave dissipation efficiency is achieved, providing precise feedback for adaptive air chamber adjustment.

[0053] In some embodiments, the method for monitoring the stress state of a structure is as follows: Obtain strain data for key stress-bearing parts of the breakwater, and convert the strain data into corresponding stress data based on the mechanical properties of the structural materials; Obtain deformation data of the breakwater, including settlement, horizontal displacement, tilt angle and local deformation data; The vibration response data of the breakwater under external loads were acquired and integrated into a structural vibration time series. The stress data and deformation state data were analyzed to obtain the stress state analysis results and deformation state analysis results, respectively. Extract vibration characteristic parameters of the breakwater from the structural vibration time series; The stress state analysis results, deformation state analysis results, and vibration characteristic parameters are integrated to obtain the stress state assessment results of the breakwater.

[0054] The structural stress state monitoring method described in this application acquires strain data from key stress-bearing components of the breakwater and converts it into corresponding stresses based on the mechanical properties of the structural materials. Simultaneously, it collects breakwater deformation state data, including settlement, horizontal displacement, tilt angle, and local deformation, as well as vibration response under external loads, integrating these data into a structural vibration time series. The stress and deformation state data are analyzed to obtain stress state analysis results and deformation state analysis results, and vibration characteristic parameters are extracted from the vibration time series. The analysis results and vibration characteristic parameters are then fused to obtain the structural stress state assessment results of the breakwater. Based on this, real-time quantification of the breakwater's structural safety and stability is achieved, providing reliable data support for air chamber adaptive adjustment and long-term breakwater operation.

[0055] In some embodiments, the method for optimizing and updating the air chamber adjustment command based on real-time performance data is as follows: The system acquires real-time performance data during the operation of the air chamber and compares it with a preset performance threshold. When the real-time performance data is lower than the preset performance threshold, it generates performance deviation information. Based on performance deviation information, predictive output data and corresponding observation data characterizing the operating status of the gas chamber are acquired, and a difference analysis is performed on the predictive output data and corresponding observation data within a continuous preset time period. When the deviation between the predictive output data and the corresponding observation data is within a preset allowable error range and exhibits random fluctuations, it is determined that the performance deviation is mainly caused by wave randomness and does not trigger online updates. When the deviation between the predictive output data and the corresponding observation data exceeds the preset allowable error range for a first set time within a continuous time period, the source type of the performance deviation is determined. The corresponding online update path is selected based on the source type of performance deviation: when the performance deviation source is wave state prediction error, the prediction model update path is entered to adaptively update the parameters of the wave state prediction model; when the performance deviation source is prediction-control coupling mapping error, the mapping relationship update path is entered to self-learn and update the prediction-control coupling mapping relationship function. Based on the updated wave state prediction model or prediction-control coupling mapping relationship, the air chamber regulation command is corrected to obtain the updated air chamber regulation command.

[0056] This application sets a continuous time period and a preset allowable error range. The continuous time determination serves as the online update trigger condition to reduce unnecessary updates caused by instantaneous random fluctuations and improve the operational stability of the control system. By determining the source of performance deviation, the objects involved in the online update strategy are limited, and performance deviation analysis and update path selection are performed on the objects requiring online updates. When the operating performance of the control system deteriorates, the update path of the predictive model and the update path of the predictive-control coupling mapping relationship are selected to maintain the long-term adaptive capability of the control system.

[0057] It should be noted that the random fluctuations mentioned are fluctuations generated by the inherent randomness of waves.

[0058] Specifically, the method for determining the source type of performance deviation is as follows: Compare the predicted wave state parameters with the wave state parameters actually observed at the corresponding time. When the deviation between the predicted wave state parameters and the actual observed wave state parameters continues for a second set time and exceeds the corresponding preset allowable error range, the source of the performance deviation is determined to be the wave state prediction error. When the deviation between the predicted wave state parameters and the actual observed wave state parameters is within the corresponding preset allowable error range, and the deviation between the expected performance of the air chamber obtained according to the prediction-control coupling mapping relationship and the actual performance of the air chamber continues for a third set time exceeding the corresponding preset allowable error range, the source of the performance deviation is determined to be the prediction-control coupling mapping relationship error.

[0059] It should be noted that the first, second, and third time settings can be configured according to actual needs.

[0060] The method described in this application for optimizing and updating air chamber regulation commands based on real-time performance data involves acquiring real-time performance data during air chamber operation and comparing it with a preset performance threshold. When the real-time performance falls below the threshold, performance deviation information is generated. Based on the performance deviation information, the predicted output and observed data of the air chamber operation status are acquired, and a difference analysis is performed to determine the source of the performance deviation. Adaptive updates are performed according to the type of deviation source: if it is a prediction error, the wave state prediction model parameters are adaptively updated; if it is a coupling mapping error, the prediction-control coupling mapping relationship is self-learned and updated. The air chamber regulation commands are corrected based on the updated prediction model or coupling mapping relationship to obtain optimized air chamber regulation commands. On this basis, real-time optimization and continuous performance improvement of air chamber regulation are achieved, providing a reliable guarantee for the stable operation of integrated OWC breakwaters in complex sea conditions.

[0061] The purpose of this application is also to provide an integrated OWC breakwater adaptive air chamber control system for implementing the above-mentioned adaptive air chamber control method, comprising a sensing layer, a decision layer, and an execution layer, wherein... The sensing layer is used to monitor the effective wave height, dominant wave period, and incident wave direction in the working sea area of ​​the breakwater in real time, and to construct a standardized current wave state vector based on the monitoring data. :

[0062] in, Indicates the current significant wave height. Indicates the current dominant wave cycle. Indicates the current direction of the incident wave. It is the transpose symbol; The decision-making layer is configured to: generate at least one predicted wave state vector based on a standardized current wave state vector; extract corresponding wave energy characteristic parameters based on the standardized current wave state vector and the predicted wave state vector; determine structured air chamber adjustment commands based on the predicted wave state vector and the wave energy characteristic parameters; monitor the wave energy absorption efficiency, wave dissipation coefficient, and structural stress state of the breakwater in real time during operation and generate real-time performance data; and perform online adaptive updates to the wave state prediction model or prediction-control coupling mapping relationship based on the real-time performance data to optimize the accuracy of the air chamber adjustment commands and the breakwater energy absorption performance. The execution layer implements continuous or discrete adjustments to the air chamber width, front wall draft, and corner radius based on the air chamber adjustment instructions generated by the decision layer. The system works collaboratively through a perception layer, a decision-making layer, and an execution layer to form a closed-loop adaptive control system of "perception-prediction-feature extraction-parameter mapping-optimization decision-execution-feedback update".

[0063] The aforementioned integrated OWC breakwater adaptive air chamber control system achieves real-time monitoring, prediction, and adaptive air chamber adjustment of wave states through the collaborative work of the perception layer, decision layer, and execution layer. The perception layer collects the effective wave height, dominant wave period, and incident wave direction of the breakwater's working area in real time and constructs a standardized current wave state vector. The decision layer generates a predicted wave state vector based on the current wave state vector, extracts wave energy characteristic parameters, and determines structured air chamber adjustment commands accordingly. Simultaneously, it monitors the breakwater's wave energy absorption efficiency, wave dissipation coefficient, and structural stress state in real time to form real-time performance data. Based on the real-time performance data, it adaptively updates the wave state prediction model or prediction-control coupling mapping relationship online to optimize air chamber adjustment accuracy and breakwater energy absorption performance. The execution layer continuously or discretely adjusts the air chamber width, front wall draft, and corner radius according to the air chamber adjustment commands generated by the decision layer, realizing real-time adaptive intelligent operation of the breakwater under complex sea conditions. This improves wave energy capture efficiency, ensures structural safety, and meets the requirements for long-term stable operation.

[0064] In some embodiments, the decision layer includes a short-term wave prediction module, a wave energy characteristic parameter extraction module, a parameter mapping module, a performance monitoring module, and an online update module, wherein: the short-term wave prediction module is used to predict wave states based on a multivariate autoregressive exogenous input model; the wave energy characteristic parameter extraction module is used to extract wave energy characteristic parameters based on the current wave surface displacement time series and the predicted wave surface displacement time series; the parameter mapping module is used to determine the corresponding air chamber structure parameter adjustment relationship based on the wave state prediction results and wave energy characteristic parameters, and generate air chamber structure parameter adjustment instructions; the performance monitoring module is used to monitor the wave energy absorption efficiency, wave dissipation coefficient, and structural stress state during the operation of the breakwater, and generate real-time performance data; the online update module is used to adaptively update the wave state prediction model or prediction-control coupling mapping relationship based on the real-time performance data.

[0065] The aforementioned decision-making layer of this application includes a short-term wave prediction module, a wave energy characteristic parameter extraction module, a parameter mapping module, a performance monitoring module, and an online update module. These modules work collaboratively to achieve wave state prediction and adaptive chamber adjustment. The short-term wave prediction module predicts wave state based on a multivariate autoregressive exogenous input model. The wave energy characteristic parameter extraction module extracts wave energy characteristic parameters based on the current wave surface displacement time series and the predicted wave surface displacement time series. The parameter mapping module determines the chamber structure parameter adjustment relationship based on the predicted wave state and wave energy characteristic parameters, and generates structured adjustment commands. The performance monitoring module monitors the wave energy absorption efficiency, wave dissipation coefficient, and structural stress state of the breakwater in real time during operation, generating real-time performance data. The online update module adaptively updates the wave state prediction model or prediction-control coupling mapping function based on the real-time performance data, achieving dynamic optimization of chamber adjustment and continuous improvement of the breakwater's energy capture performance.

[0066] Specifically, the multivariate autoregressive exogenous input model is expressed by the following formula:

[0067] in, The predicted wave state vector for the next moment; For the first An autoregressive coefficient matrix of order 1, describing the historical values ​​of wave states; This represents the historical wave state vector; For the first Exogenous input coefficient matrix; The historical values ​​of exogenous inputs; The random disturbance term represents the random factors and measurement errors that the model fails to characterize.

[0068] Specifically, to adapt to the dynamic changes in wave statistical characteristics in actual sea conditions, the wave state prediction model parameters are updated online using a recursive least squares algorithm:

[0069]

[0070]

[0071] in, The recursive least squares gain vector controls the response speed of the model parameters to changes in new sea states. This is the forgetting factor, with a value range of 0.95-0.99; The regression vector maps historical wave states and environmental inputs to the parameter space. The value of the parameter covariance matrix at the previous time step; This is the model parameter vector; Indicates in The model parameter vector at time step; Let be the parametric covariance matrix.

[0072] Specifically, to improve the stability and robustness of the prediction results under complex sea conditions, a Kalman filter is introduced in the prediction output stage to correct the prediction results:

[0073]

[0074]

[0075] in, For the first The first time information prediction Time-state vector; This is the state transition matrix; The observation matrix; This is system noise; Kalman gain; For observation; To observe the noise covariance matrix; For the first The state vector at each moment incorporates the observed values; For the first The covariance matrix of the predicted state at each time step; For the first The state vector at each moment incorporates the observed values.

[0076] In some embodiments, the execution layer includes a dynamic adjustment device for the air chamber width, an electric lifting mechanism for the front wall draft depth, and a deformable structure for the front wall fillet radius, wherein: like Figure 2As shown, the dynamic adjustment device for the air chamber width includes an air chamber body 101, a fixed sidewall 102, a movable sidewall 103, a sliding guide rail 104, a lead screw transmission mechanism 105, a drive motor 106, a connecting bracket 107, and a sealing structure 108. The air chamber body 101 serves as the mounting base. The fixed sidewall 102 is rigidly connected to one end of the air chamber body 101, forming the fixed boundary of the air chamber. The sliding guide rail 104 is installed axially inside the air chamber body 101, providing axial guidance for the movable sidewall 103. The movable sidewall 103 and the sliding guide rail 104 form a sliding pair, allowing for axial linear movement along the sliding guide rail 104. The middle part of the movable sidewall 103 is rigidly connected to the lead screw and nut pair of the lead screw transmission mechanism 105, converting the rotational motion of the lead screw into axial linear displacement of the movable sidewall 103. The drive motor 106 is fixed to the other end of the air chamber body 101 via a connecting bracket 107. The output shaft of the drive motor 106 is coaxially connected to the lead screw of the lead screw transmission mechanism 105 to achieve power transmission. The other end of the lead screw of the lead screw transmission mechanism 105 is supported by an adjustable bearing seat (the adjustable structure is enlarged in the figure). The transmission backlash can be reduced or eliminated by adjusting the position of the bearing seat to ensure transmission accuracy. The sealing structure 108 is installed at the radial fit clearance between the movable side wall 103 and the air chamber body 101, and moves synchronously with the movable side wall 103 to maintain the sealing performance of the air chamber during dynamic movement.

[0077] When the dynamic adjustment device for the air chamber width is in operation, the drive motor 106 starts and outputs rotational power, which is transmitted to the lead screw of the lead screw transmission mechanism 105 through the coupling, causing the lead screw to rotate. The lead screw transmission mechanism 105 converts the rotational motion of the lead screw into the axial linear motion of the lead screw nut. The movable sidewall 103, rigidly connected to the nut, moves with high precision along the air chamber axis under the constraint of the sliding guide rail 104, avoiding deviation or jamming during the movement. The forward / reverse rotation of the drive motor 106 corresponds to different rotation directions of the lead screw, driving the movable sidewall 103 to move closer to / away from the fixed sidewall 102, thereby reducing / increasing the cavity distance between the fixed sidewall 102 and the movable sidewall 103, realizing the dynamic adjustment of the air chamber width. Throughout the movement of the movable sidewall 103, the sealing structure 108 always maintains contact with the inner wall of the air chamber body 101, forming a dynamic seal to prevent gas leakage inside the air chamber due to the movement of the sidewall, ensuring a stable pressure environment in the air chamber.

[0078] The air chamber width dynamic adjustment device is driven by a drive motor 106 and is used to switch the air chamber width between a number of preset discrete positions. The discrete positions include at least three gears, which correspond to air chamber widths of 3.6 meters, 4.6 meters and 5.6 meters, respectively.

[0079] like Figure 3As shown, the electric lifting mechanism for the front wall draft includes a front wall structure 201, a lifting support frame 202, an electric drive unit 203, a screw lifting mechanism 204, a guide rail 205, a connecting seat 206, a mounting base 207, and a limiting device 208. The mounting base 207 serves as the installation reference for the entire lifting mechanism and is fixed to the base structure of the float. Both ends of the front wall structure 201 are hinged / fixed to the base structure via the connecting seat 206 to accommodate attitude changes during lifting and adjustment. The lifting support frame 202 is vertically fixed to the mounting base 207, providing installation support and motion constraints for the screw lifting mechanism 204. The output end of the screw lifting mechanism 204 is rigidly connected to the front wall structure 201, transmitting axial driving force to the front wall structure 201 to achieve lifting and adjustment. The guide rail 205 is laid horizontally on the mounting base 207, providing guidance for the horizontal movement of the front wall structure 201. The electric drive unit 203 is installed on the top of the lifting support frame 202. Its power output is divided into two paths: one path transmits vertical lifting power through the screw lifting mechanism 204, and the other path drives the front wall structure 201 to move horizontally along the guide rail 205 through the horizontal transmission mechanism, realizing the dynamic adjustment of the horizontal position of the front wall structure 201. The limit device 208 is installed at both ends of the guide rail 205 and at the limit position of the lifting stroke of the screw lifting mechanism 204. It cooperates with the moving parts of the front wall structure 201. When the parts move to the limit position, the limit signal is triggered, cutting off the driving power and realizing safety protection.

[0080] Specifically, the draft depth adjustment range of the front wall structure is -1.5 meters to -2.5 meters, the lead of the lead screw of the lead screw lifting mechanism 204 is 10-20 mm per revolution, and it is used with a high-resolution encoder to achieve millimeter-level position control accuracy; the position information of the front wall is fed back in real time through an absolute encoder or a magnetostrictive displacement sensor to obtain the absolute position status of the front wall.

[0081] The deformable front wall fillet radius structure adopts a shape memory alloy structure or a hydroforming structure to achieve continuous adjustment of the front wall fillet radius within the range of 0 meters to 0.4 meters, and to coordinate with the air chamber width and front wall draft parameters. Among them, the shape memory alloy structure adjusts the fillet shape by inducing an alloy phase transformation through heating or current excitation, while the hydroforming structure adjusts the curvature of the outer surface by adjusting the internal chamber pressure, thereby achieving continuous adjustment of the fillet radius.

[0082] like Figure 4As shown, the deformable structure with rounded corner radius of the front wall includes a front wall body 301, a rounded corner transition structure 302, a flexible deformation unit 303, a drive adjustment mechanism 304, a support frame 305, and an outer shell structure 306. The front wall body 301 serves as a rigid base, with its corners rigidly connected to the ends of the outer shell structure 306 and the rounded corner transition structure 302, forming the overall outer contour of the front wall. The support frame 305 is fixedly installed on the inner side of the front wall body 301 along the curvature path of the rounded corner, providing an internal support frame for the entire adjustment structure. The flexible deformation unit 303 is installed within a preset area of ​​the support frame 305 and undergoes directional elastic deformation under the constraint of the support frame 305. The support frame 305 restricts the deformation direction of the flexible deformation unit, preventing abnormal deformations such as local protrusions or depressions during the deformation process. The drive adjustment mechanism 304 is fixedly installed on the front wall body 301, and its output end abuts against the flexible deformation unit 30. It is used to apply a driving force to the flexible deformation unit 303 and transmit the driving force to the flexible deformation unit, causing the flexible deformation unit 303 to undergo elastic deformation. The outer shell structure 306 covers the outside of the rounded corner transition structure 302 and is sealed to the front wall body 301. It is used to encapsulate and protect the internal flexible deformation unit 303, drive adjustment mechanism 304 and support frame 305. At the same time, the inner wall of the outer shell structure 306 fits against the outer surface of the rounded corner transition structure 302 and adapts to the curvature change of the rounded corner transition structure 302.

[0083] The flexible deformation unit 303 is driven by the adjustment mechanism 304, which changes the curvature of the rounded corner transition structure 302, thereby adjusting the rounded corner radius. Specifically, the deformation of the flexible deformation unit 303 is directly transmitted to the rounded corner transition structure 302 it is in contact with. When the adjustment mechanism 304 pushes the flexible deformation unit 303 to move outward from the rounded corner, the curvature of the rounded corner transition structure 302 decreases, and the rounded corner radius increases; when the adjustment mechanism 304 pulls the flexible deformation unit 303 to contract inward from the rounded corner, the curvature of the rounded corner transition structure 302 increases, and the rounded corner radius decreases. The constraint effect of the supporting frame 305 ensures that the deformation of the rounded corner transition structure is uniform and continuous, avoiding local stress concentration or abrupt contour changes.

[0084] The aforementioned execution layer of this application includes a dynamic air chamber width adjustment device, an electric lifting mechanism for the front wall draft, and a deformable structure with a rounded corner radius for the front wall. These devices work together to achieve adaptive adjustment of the air chamber structural parameters. The dynamic air chamber width adjustment device is driven by a drive motor and can switch the air chamber width at at least three preset discrete positions, corresponding to 3.6 meters, 4.6 meters, and 5.6 meters respectively. The electric lifting mechanism for the front wall's draft converts rotational motion into vertical lifting of the front wall, with an adjustment range of -1.5 meters to -2.5 meters. Combined with a high-resolution encoder, it achieves millimeter-level positional accuracy and provides real-time feedback on the front wall's position via an absolute encoder or magnetostrictive displacement sensor. The deformable structure for the front wall's rounded corner radius utilizes shape memory alloy or hydroforming technology, enabling continuous adjustment within the range of 0 meters to 0.4 meters. This is coordinated with the air chamber width and the front wall's draft depth for control. The shape memory alloy structure adjusts the rounded corner shape by triggering a phase change through heating or current excitation, while the hydroforming structure adjusts the surface curvature through chamber pressure, achieving continuous controllability of the rounded corner radius. Based on this, precise, adaptive, and coordinated adjustment of the air chamber structural parameters is achieved, thereby improving the breakwater's wave energy absorption efficiency and operational stability.

[0085] The above embodiments are used to explain this application, not to limit it. Any modifications and changes made to this application within the spirit and scope of the claims shall fall within the protection scope of this application.

Claims

1. A fusion-type OWC breakwater adaptive air chamber control method, characterized in that, Includes the following steps: Based on the real-time monitoring results of the effective wave height, dominant wave period and incident wave direction in the working sea area of ​​the breakwater, the current wave state vector is constructed. Input the current wave state vector into the wave state prediction model to obtain at least one predicted wave state vector. Determine the sea state level and its corresponding wave energy characteristic parameters based on the current wave state vector and the predicted wave state vector; Based on wave energy characteristic parameters, sea state level and predicted wave state vector, the target values ​​of air chamber width B, front wall draft D and front wall fillet radius R are determined by a preset prediction-control coupling mapping relationship function. Based on the coupling relationship between the target values, collaborative optimization is performed to generate air chamber adjustment commands. Based on the air chamber adjustment command, the air chamber width, the front wall draft depth, and the front wall corner radius are adaptively adjusted.

2. The adaptive air chamber control method according to claim 1, characterized in that, The method for constructing the wave state prediction model is as follows: The significant wave height, dominant wave period and incident wave direction in the historical sea state database are preprocessed to obtain a historical wave data sequence. Based on the historical wave data sequence, the evolution trends of effective wave height, dominant wave period, and incident wave direction, as well as their interrelationships, are determined. Based on the evolution trend and parameter correlation, a wave state evolution expression is constructed; The parameters to be determined in the wave state evolution expression are identified to minimize the deviation between the expression calculation result and the historical wave data sequence. The wave state evolution expression is verified based on historical wave data sequences from different time periods, and the expression parameters are corrected according to the verification results to obtain the wave state prediction model.

3. The adaptive air chamber control method according to claim 1, characterized in that, The current wave state vector is constructed as follows: Obtain the current wavefront displacement time series; Based on the current wavefront displacement time series, the effective wave height, dominant wave period, and incident wave direction are obtained; The significant wave height, dominant wave period, and incident wave direction are combined into a three-dimensional column vector. The three-dimensional column vector is standardized to obtain the standardized current wave state vector.

4. The adaptive air chamber control method according to claim 1, characterized in that, The method for determining sea state grades is as follows: Extract the current effective wave height and the predicted effective wave height from the current wave state vector and the predicted wave state vector, respectively. Based on a preset range of significant wave height thresholds, and in conjunction with the current significant wave height, the predicted significant wave height, and their changing trends, sea conditions are classified into multiple levels to determine the corresponding sea condition grades. The sea condition grades include at least Level I, Level II, Level III, Level IV, and Level V, where Level V corresponds to extreme sea conditions. The sea condition grades are dynamically corrected based on whether the predicted significant wave height crosses the threshold of an adjacent grade.

5. The adaptive air chamber control method according to claim 4, characterized in that, The method for extracting wave energy characteristic parameters is as follows: The current wavefront displacement time series and the predicted wavefront displacement time series of the target sea area are integrated to obtain the comprehensive wavefront displacement time series; Based on the comprehensive wavefront displacement time series, the total wave energy and power spectral density function are obtained; Extract the peak frequency and corresponding peak energy from the power spectral density function; Obtain the current air cell structure parameters of the integrated OWC breakwater, determine the corresponding current OWC air cell resonance frequency based on the current air cell structure parameters, and perform matching analysis between the spectral peak frequency and the current OWC air cell resonance frequency to obtain the frequency matching coefficient. The spectral width parameter is calculated based on the power spectral density function; Frequency-direction joint analysis was performed on the predicted wavefront displacement time series to obtain the directional energy distribution ratio; By summarizing the total wave energy, peak energy, spectral width parameter, frequency matching coefficient, and directional energy distribution ratio, a set of wave energy characteristic parameters is obtained.

6. The adaptive air chamber control method according to claim 1, characterized in that, The method for generating air chamber regulation commands is as follows: Wave energy characteristic parameters, sea state level, and predicted wave state vector are input into a pre-constructed prediction-control coupling mapping function to calculate the target values ​​of air chamber width B, front wall draft D, and front wall fillet radius R, respectively. The prediction-control coupling mapping function is obtained through joint training of historical sea state data, numerical simulation results, and real-time operation data. The target values ​​of air chamber width B, front wall draft D, and front wall fillet radius R are encapsulated as preliminary control commands. Determine whether the initial control commands meet the preset physical constraints, and optimize the settings of unmet target values ​​to bring them within a feasible range; Based on the coupling relationship between the optimized air chamber width B, the front wall draft D, and the front wall fillet radius R, the parameter collaborative optimization strategy and the adjustment execution sequence are determined, and structured air chamber adjustment commands are generated accordingly.

7. The adaptive air chamber control method according to claim 1, characterized in that, The adaptive air chamber control method further includes the following steps: During the adjustment process, the wave energy absorption efficiency, wave dissipation coefficient and structural stress state of the breakwater are monitored in real time, and the monitoring results are recorded as real-time performance data. Based on real-time performance data, determine whether the adaptive adjustment has reached the preset performance threshold set: if not, optimize and update the air chamber adjustment command based on real-time performance data and execute it until the preset performance threshold set is reached; if it has been reached, maintain the current air chamber adjustment command and continuously monitor the wave state changes.

8. The adaptive air chamber control method according to claim 7, characterized in that, The method for optimizing and updating the air chamber adjustment command based on real-time performance data is as follows: The system acquires real-time performance data during the operation of the air chamber and compares it with a preset performance threshold. When the real-time performance data is lower than the preset performance threshold, it generates performance deviation information. Based on performance deviation information, predictive output data and corresponding observation data characterizing the operating status of the gas chamber are acquired, and a difference analysis is performed on the predictive output data and corresponding observation data within a continuous preset time period. When the deviation between the predictive output data and the corresponding observation data is within a preset allowable error range and exhibits random fluctuations, it is determined that the performance deviation is mainly caused by wave randomness and online updates are not triggered. When the deviation between the predictive output data and the corresponding data exceeds the preset allowable error range for a first set time within a continuous time period, the source type of the performance deviation is determined. The corresponding online update path is selected based on the source type of performance deviation: when the performance deviation source is wave state prediction error, the prediction model update path is entered to adaptively update the parameters of the wave state prediction model; when the performance deviation source is prediction-control coupling mapping error, the mapping relationship update path is entered to self-learn and update the prediction-control coupling mapping relationship function. Based on the updated wave state prediction model or prediction-control coupling mapping relationship, the air chamber regulation command is corrected to obtain the updated air chamber regulation command.

9. An integrated OWC breakwater adaptive air chamber control system, used to implement the adaptive air chamber control method as described in any one of claims 1-8, characterized in that, It includes a perception layer, a decision-making layer, and an execution layer, among which: The perception layer is used to monitor the effective wave height, dominant wave period and incident wave direction in the working sea area of ​​the breakwater in real time, and to construct a standardized current wave state vector. The decision-making layer is configured to: generate at least one predicted wave state vector based on the standardized current wave state vector; extract corresponding wave energy characteristic parameters based on the standardized current wave state vector and the predicted wave state vector; and determine the structured air chamber adjustment command based on the predicted wave state vector and the wave energy characteristic parameters. The execution layer continuously or discretely adjusts the air chamber width, front wall draft depth, and corner radius according to the air chamber adjustment command; The system works collaboratively through the perception layer, decision layer, and execution layer to form a closed-loop adaptive control system of "perception-prediction-feature extraction-parameter mapping-optimization decision-execution-feedback update".

10. The adaptive air chamber control system according to claim 9, characterized in that, The execution layer includes a dynamic adjustment device for the air chamber width, an electric lifting mechanism for the front wall draft, and a deformable structure for the front wall corner radius, wherein: The air chamber width dynamic adjustment device is configured to switch the air chamber width between multiple preset discrete positions; The electric lifting mechanism for the front wall draft is configured to continuously adjust the front wall draft in the vertical direction. The deformable structure with a rounded corner radius of the front wall is configured to continuously adjust the rounded corner radius of the front wall within a predetermined adjustable range, and to adjust it in coordination with the air chamber width and the draft depth of the front wall.

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