Wave energy oscillation system of bead type structure and active oscillation method

Abstract

The invention discloses a beaded structure wave energy oscillation system and an active oscillation method. The beaded structure wave energy oscillation system comprises a beaded type wave energy oscillation generator, a control module and a power generation module. The generator integrates an oscillation detection module and an active oscillation device, monitors wave amplitude in real time through a built-in sensor, and triggers active oscillation or current locking control according to a comparison result of the amplitude and a set threshold value. And the control module adopts a multi-algorithm collaborative optimization strategy of predictive control, fuzzy control and stability control, calculates optimal power parameters required by the active vibration isolation device in real time by dynamically analyzing amplitude data, and generates an accurate power input instruction. And the power generation module intelligently supplies energy to the active oscillation device according to the instruction to form a closed-loop control system. Through collaborative optimization of a multi-dimensional control algorithm, the problems that a traditional wave energy device is low in energy conversion efficiency and poor in operation stability are effectively solved, and the energy capture efficiency and equipment reliability of the system under the complex sea condition are improved.

Description

Technical Field

[0001] The present invention relates to the field of wave energy power generation, and in particular to a wave energy oscillation system with a beaded structure and an active oscillation method. Background Art

[0002] With the growing global demand for renewable energy, ocean wave energy, as a new type of clean energy with abundant reserves and widespread distribution, has attracted much attention in recent years. Wave energy power generation technology has significant development potential by capturing the kinetic energy of the periodic movement of seawater and converting it into electrical energy. At present, mainstream wave energy conversion devices mainly include oscillating water column type, float type, pendulum type and other structures. These devices usually rely on the natural force of waves to drive the movement of mechanical components, thereby driving the generator to work. However, traditional technologies generally have problems such as low energy conversion efficiency, poor environmental adaptability, and insufficient structural stability. Especially in complex sea conditions, the randomness and mutation of wave amplitudes will cause the device to oscillate with different amplitudes, which will cause mechanical fatigue, a sharp drop in power generation efficiency, and even equipment damage.

[0003] In the existing technology, some wave energy devices attempt to adjust the oscillation amplitude through passive damping or simple feedback control, but such methods are difficult to dynamically adapt to real-time changes in wave parameters, and the control accuracy is limited. For example, a single control algorithm (such as PID control) is prone to response lag or overshoot when dealing with nonlinear characteristics of waves, resulting in unstable energy capture efficiency. In addition, traditional devices lack active protection mechanisms for extreme working conditions. When the wave amplitude exceeds the safe range, the equipment life may be shortened due to overload operation. Therefore, there is an urgent need for a simple and accurate active adjustment method that enables wave energy generation devices to maintain the optimal positive force to increase oscillation for a long time and further improve energy capture efficiency. Summary of the Invention

[0004] Purpose of the invention: To solve the problems mentioned in the background technology, the present invention discloses a wave energy oscillation system with a beaded structure and an active oscillation method. The control module adopts a multi-algorithm collaborative optimization control strategy to implement optimal power supply instructions for the active vibration isolation system, so as to maintain the optimal power of the wave energy power generation device under the premise of low energy loss and improve the energy capture efficiency.

[0005] Technical solution:

[0006] The present invention discloses a wave energy oscillation system with a beaded structure, the system comprising a beaded wave energy oscillation generator, a control module and a power generation module;

[0007] The beaded wave energy oscillation generator includes an oscillation detection module and an active oscillation device. The oscillation detection module is provided with a sensor for detecting the amplitude currently acting on the beaded wave energy oscillation generator and inputting the amplitude into the control module. If the amplitude is less than the set value h min , the active oscillation device operates, if the amplitude is greater than or equal to the set value h max , then start the locking control and lock the current input;

[0008] The input end of the control module is connected to the oscillation detection module. The control module adopts a multi-algorithm collaborative optimization control strategy, integrating a predictive control algorithm, a fuzzy control algorithm, and a stability control algorithm. Based on the input amplitude of the oscillation detection module, the control module calculates the optimal power required by the current active vibration isolation device, generates an optimal power input instruction, and transmits it to the power generation module.

[0009] The power generation module is connected to the output end of the control module, receives the optimal power input instruction transmitted by the control module to transmit power, and is connected to the active oscillation module to supply energy to the active oscillation device for active oscillation.

[0010] Furthermore, the control module multi-algorithm collaborative optimization control strategy is specifically as follows:

[0011] The power required by the active oscillation device is used as the optimal solution for the coordinated output of multiple algorithms. The dynamic compensation term K(t) is output based on the predictive control algorithm. The fuzzy control algorithm is used to adjust the gain in real time according to K(t) to obtain the final control quantity u(t) of the active oscillation device. The power generation efficiency η(t) is defined as the ratio of the theoretical wave energy input power to the actual power generation power. The stability parameter S(t) is obtained based on the stability control algorithm.

[0012] The control quantity u(t), feedback index efficiency η(t), and stability S(t) are normalized and combined into a multidimensional feature vector V(t) = [u(t), η(t), S(t)] T , as the decision output;

[0013] The following logic branches are triggered based on the output feature vector V(t):

[0014] High frequency disturbance mode (u>u th ): Preferentially adopt the weighted output of the predictive control algorithm to suppress phase lag and dynamically adjust the x(k) matrix gain;

[0015] Nonlinear time-varying mode (S>S th ): Fuzzy control algorithm is combined with rolling optimization correction to reduce cumulative error and dynamically match the initial reference value U0 through the rule table;

[0016] Steady-state mode (η≥η th And S≤S th): Fixed weight distribution, maintaining efficient and stable operation and reducing computational overhead;

[0017] Among them, u th 、S th ,η th To calibrate the theoretical values of control quantity, feedback index efficiency and stability based on the measured sea condition data;

[0018] According to V(t) trigger logic branch, define the algorithm weight coefficient, weight coefficient ω predict (t),ω fuzzy (t),ω adaptive (t) Dynamically allocate algorithm priority:

[0019]

[0020] The optimal solution is output by the weighted output multi-algorithm collaboratively as the power required by the active oscillation device. If u * (t) * (t) th , repeat the process of defining the tracking error to output the power required by the wave energy oscillation power generation module until the optimal solution is output.

[0021] Furthermore, the predictive control algorithm outputs the dynamic compensation term K(t) as follows:

[0022] Design a time-varying controller based on state observation so that the closed-loop system satisfies:

[0023]

[0024] Define tracking error e(t):

[0025]

[0026] Where x_desired(t) represents the desired state trajectory at time t;

[0027] According to the real-time changing characteristics of the error signal e(t), the dynamic compensation term K(t) is used to make the system output quickly converge to the set value range [0,1], suppress the phase lag caused by the randomness of the waves, and synchronize the power generation module with the wave frequency. The dynamic compensation term is:

[0028] K(t)=α·exp(-βt)·|e(t)|+γ∫e(t)dt

[0029] Where α is the attenuation coefficient, β is the exponential factor, γ is the integral gain coefficient, and |e(t)| is the current absolute value of the error.

[0030] Furthermore, the specific steps for obtaining u(t) and η(t) are as follows:​

[0031] Construct fuzzy rule table based on expert experience:

[0032] IF E is High ANDΔE is Negative,THEN U=K(t)*Medium

[0033] Perform rule matching according to the fuzzy rule table to generate the initial reference value U0 of closed-loop control;

[0034] According to K(t), the gain is adjusted in real time to process the nonlinear and time-varying characteristics of the wave, and the control quantity U(t) of the primary regulation link of the active oscillation module is obtained:

[0035]

[0036] in, represents the fuzzy reasoning synthesis operation, K(t) is the dynamic compensation term, U0 is the initial reference value of the closed-loop control, D(t) is the time-varying coefficient matrix, and ω(t) is the wave disturbance;

[0037] The nonlinear state-space equation dynamically updates the system gain matrix to describe the coupling relationship between wave energy input and system oscillation:

[0038]

[0039] in, is the system displacement and velocity, wave energy capture related quantity, ω(t) is the wave disturbance, A(t), B(t), D(t) are the time-varying coefficient matrices; the discrete integral term ∫e(t)dt of the dynamic compensation term K(t) is calculated to generate the final control quantity of the active oscillation module driving signal:

[0040] u(t)=[α·exp(-β|e(t)|)+γ∑e(j)Δt]·U(t)+D(t)ω(t)

[0041] Where α is the attenuation coefficient, β is the exponential factor, γ is the integral gain coefficient, |e(t)| is the current error absolute value, ∑e(j)Δt is the cumulative sum of historical errors, Δt is the sampling period, j is the discrete time point, U(t) is the intermediate variable coupling term, and D(t) is the time-varying coefficient matrix;

[0042] The theoretical wave energy input power is defined as:

[0043]

[0044] Where ρ(k) is the collected energy density, A wake (t) is the wave amplitude, f wave (t) is the wave frequency;

[0045] Actual power generation P output (k) Evaluate power generation efficiency using sensor measurements:

[0046]

[0047] Furthermore, the stability parameter S(t) is obtained as follows:

[0048] Based on the tracking error e(t) = x desired (t)-x(t), define the sequential error term and the cumulative error term, and obtain the stability control algorithm S(t):

[0049]

[0050] Among them, α S and β S is the adjustment coefficient, which is used to balance the weight of the sequential and cumulative errors.

[0051] Furthermore, the locking control operation process is as follows:

[0052] The active oscillation method uses a linear motor that can receive electrical signals and convert them into linear motion, generating an Ampere force:

[0053] F=nBILssinα

[0054] Where n is the number of turns of the coil, B is the magnetic field strength, I is the current, L is the length of the coil that cuts the magnetic field, and α is the angle between the current direction and the magnetic field direction; the equilibrium equation is:

[0055]

[0056] Among them, U is the driving voltage, R is the resistance value, L h is the inductance value; combining the above two equations, we can get the transfer function:

[0057]

[0058] Under lockout control, the transfer function needs to take into account the influence of the lockout condition. When the lockout condition is met, the current I in the transfer function is forced to zero, so the transfer function becomes:

[0059] F(s)=0h≥h max

[0060] When the lockout condition is not met, the transfer function returns to:

[0061]

[0062] Among them, L his the inductance value, S is the complex frequency domain variable, R is the resistance value, h is the oscillation module position, h max is the lockout threshold.

[0063] Furthermore, the present invention also discloses a wave energy active oscillation method of a beaded structure, the active oscillation method comprising the following steps:

[0064] S1 starts the bead-type wave energy oscillation generator, and the oscillation detection module captures the current amplitude through the sensor;

[0065] S2 defines the amplitude setting value h min and h max , if the current amplitude is less than the set value h min , the active oscillation device will start to work. If the current amplitude is greater than or equal to the set value h max , then the locking control is started and the current input is locked; the current amplitude of S2 is less than the set value h min :

[0066] The control module receives the input amplitude of the oscillation detection module and adopts a multi-algorithm collaborative optimization control strategy. It integrates the predictive control algorithm, fuzzy control algorithm and stability control algorithm to obtain the control quantity u(t), feedback index efficiency η(t) and stability S(t). After normalization, the combined multidimensional feature vector V(t) = [u(t), η(t), S(t)] T , as the decision output and triggering the logic branch, the optimal solution is outputted by the weighted output multi-algorithm as the power required by the active oscillation device;

[0067] The S3 control module generates the optimal power input command and transmits it to the power generation module;

[0068] The S4 power generation module is an active oscillation device that performs active oscillation to maximize energy capture efficiency.

[0069] Beneficial effects:

[0070] 1. The present invention adopts a modular design. By independently controlling the oscillation device to actively isolate vibration, the wave energy power generation module can maintain the optimal oscillation state for a longer period of time and maintain the optimal positive force for a long time. On the one hand, it improves the energy capture efficiency, reduces the degree to which the power generation device is affected by weather, and improves the robustness of the overall system.

[0071] 2. The present invention obtains dynamic compensation terms by integrating a predictive control algorithm and combines it with an input fuzzy control algorithm. The gain is adjusted in real time according to the dynamic compensation terms. The dual algorithms work together to obtain the final control quantity of the active oscillation device. It can perform forward-looking optimization through the dynamic model of the predictive control algorithm, and at the same time use the fuzzy control algorithm to deal with nonlinearity, time-varying characteristics and uncertainty disturbances, forming a double-layer robustness. Combined with the normalized stability characteristics of the stable control algorithm, the final power command regulation is more accurate and stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a schematic diagram of the control module workflow of the present invention;

[0073] Figure 2 This is a schematic diagram of the multi-algorithm collaborative optimization control strategy algorithm of the present invention;

[0074] Figure 3 This is a schematic structural diagram of a beaded wave energy oscillation generator according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0076] like Figure 1 As shown, the present invention discloses a wave energy oscillation system with a beaded structure, the system includes a beaded wave energy oscillation generator, a control module and a power generation module;

[0077] The beaded wave energy oscillation generator includes an oscillation detection module and an active oscillation device. The oscillation detection module is equipped with a sensor to detect the amplitude currently acting on the beaded wave energy oscillation generator and input the amplitude to the control module. If the amplitude is less than the set value h min , the active oscillation device operates, if the amplitude is greater than or equal to the set value h max , then start the locking control and lock the current input;

[0078] The input end of the control module is connected to the oscillation detection module. The control module adopts a multi-algorithm collaborative optimization control strategy, integrating the predictive control algorithm, fuzzy control algorithm and stability control algorithm. Based on the input amplitude of the oscillation detection module, the control module calculates the optimal power required by the current active vibration isolation device, generates the optimal power input instruction, and transmits it to the power generation module.

[0079] like Figure 2As shown in the figure, the multi-algorithm collaborative optimization control strategy of the control module is as follows:

[0080] The power required by the active oscillation device is used as the optimal solution for the coordinated output of multiple algorithms. The dynamic compensation term K(t) is output based on the predictive control algorithm. The fuzzy control algorithm is used to adjust the gain in real time according to K(t) to obtain the final control quantity u(t) of the active oscillation device. The power generation efficiency η(t) is defined as the ratio of the theoretical wave energy input power to the actual power generation power. The stability parameter S(t) is obtained based on the stability control algorithm.

[0081] The control quantity u(t), feedback index efficiency η(t), and stability S(t) are normalized and combined into a multidimensional feature vector V(t) = [u(t), η(t), S(t)] T , as the decision output;

[0082]

[0083] Among them, u min and u max is the minimum and maximum value of the control quantity, η(t) is the feedback index efficiency, α S and β S is the adjustment coefficient, |e(t)| is the current error absolute value, Σ|e(j)|·Δt is the cumulative sum of historical errors, Δt is the sampling period, j is the discrete time point, S max Theoretical maximum value of stability parameter;

[0084] The following logic branches are triggered based on the output feature vector V(t):

[0085] High frequency disturbance mode (u>u th ): Preferentially adopt the weighted output of the predictive control algorithm to suppress phase lag and dynamically adjust the x(k) matrix gain;

[0086] Nonlinear time-varying mode (S>S th ): Fuzzy control algorithm is combined with rolling optimization correction to reduce cumulative error and dynamically match the initial reference value U0 through the rule table;

[0087] Steady-state mode (η≥η th And S≤S th ): Fixed weight distribution, maintaining efficient and stable operation and reducing computational overhead;

[0088] Among them, u th 、S th ,η th To calibrate the theoretical values of control quantity, feedback index efficiency and stability based on the measured sea state data (theoretical);

[0089] According to V(t) trigger logic branch, define the algorithm weight coefficient, weight coefficient ω predict (t),ω fuzzy (t),ω adaptive (t) Dynamically allocate algorithm priority:

[0090]

[0091] The power required for the active oscillation device is to output the optimal solution through the weighted output multi-algorithm collaborative output:

[0092] u * (t) = ω fuzzy (t)u(t)+ω predict (t)η(t)+ω adaptive (t)S(t)+D(t)ω(t)

[0093] If u * (t) * (t) th , repeat the process of defining the tracking error to output the power required by the wave energy oscillation power generation module until the optimal solution is output.

[0094] The dynamic compensation term K(t) output by the predictive control algorithm is as follows:

[0095] Design a time-varying controller based on state observation so that the closed-loop system satisfies:

[0096]

[0097] Define tracking error e(t):

[0098]

[0099] Where x_desired(t) represents the desired state trajectory at time t;

[0100] According to the real-time changing characteristics of the error signal e(t), the dynamic compensation term K(t) is used to make the system output quickly converge to the set value range [0,1], suppress the phase lag caused by the randomness of the waves, and synchronize the power generation module with the wave frequency. The dynamic compensation term is:

[0101] K(t)=α·exp(-βt)·|e(t)|+γ∫e(t)dt

[0102] Where α is the attenuation coefficient, β is the exponential factor, γ is the integral gain coefficient, and |e(t)| is the current error absolute value;

[0103] The specific steps for obtaining u(t) and η(t) are as follows:

[0104] ​Construct fuzzy rule table based on expert experience:

[0105] IF E is High ANDΔE is Negative,THEN U=K(t)*Medium

[0106] Perform rule matching according to the fuzzy rule table to generate the initial reference value U0 of closed-loop control;

[0107] According to K(t), the gain is adjusted in real time to process the nonlinear and time-varying characteristics of the wave, and the control quantity U(t) of the primary regulation link of the active oscillation module is obtained:

[0108]

[0109] in, represents the fuzzy reasoning synthesis operation, K(t) is the dynamic compensation term, U0 is the initial reference value of the closed-loop control, D(t) is the time-varying coefficient matrix, and ω(t) is the wave disturbance;

[0110] The nonlinear state-space equation dynamically updates the system gain matrix to describe the coupling relationship between wave energy input and system oscillation:

[0111]

[0112] in, is the system displacement and velocity, wave energy capture related quantity, ω(t) is the wave disturbance, A(t), B(t), D(t) are time-varying coefficient matrices;

[0113] The discretization form of the system gain matrix is:

[0114] x(k+1)=x(k)+Δt[A(k)sin(ωk)+B(k)(K(k)·U(k)+D(k)w(k))]

[0115] Where Δt is the sampling period, and the fuzzy selector updates U(k) in real time according to e(t) = x_desired(t) - x(t);

[0116] Calculate the discrete integral term ∫e(t)dt of the dynamic compensation term K(t):

[0117] ∫e(t)dt≈Σe(j)Δt=e(t)Δt+Σe(j)Δt(j=0→k-1)

[0118] The final control quantity for generating the active oscillation module drive signal is:

[0119] u(t)=[α·exp(-β|e(t)|)+γ∑e(j)Δt]·U(t)+D(t)ω(t)

[0120] Where α is the attenuation coefficient, β is the exponential factor, γ is the integral gain coefficient, |e(t)| is the current error absolute value, ∑e(j)Δt is the cumulative sum of historical errors, Δt is the sampling period, j is the discrete time point, U(t) is the intermediate variable coupling term, D(t) is the time-varying coefficient matrix, and ω(t) is the wave disturbance;

[0121] The theoretical wave energy input power is defined as:

[0122]

[0123] Where ρ(k) is the collected energy density, A wake (t) is the wave amplitude, f wave (t) is the wave frequency;

[0124] Actual power generation P output (k) Evaluate power generation efficiency using sensor measurements:

[0125]

[0126] Based on the tracking error e(t) = x desired (t)-x(t), define the sequential error term and the cumulative error term, and obtain the stability control algorithm S(t):

[0127]

[0128] Among them, α S and β S is the adjustment coefficient, which is used to balance the weight of the sequential and cumulative errors.

[0129] The wave energy oscillation power generation module adopts active and passive oscillation methods to achieve oscillation control. When the amplitude is greater than or equal to the set value h max , then the locking control is started to lock the current input. The specific process is as follows:

[0130] The active oscillation method uses a linear motor that can receive electrical signals and convert them into linear motion, generating an Ampere force:

[0131] F=nBILsinα

[0132] Where n is the number of turns of the coil, B is the magnetic field strength, I is the current, L is the length of the coil that cuts the magnetic field, and α is the angle between the current direction and the magnetic field direction; the equilibrium equation is:

[0133]

[0134] Among them, U is the driving voltage, R is the resistance value, L h is the inductance value; combining the above two equations, we can get the transfer function:

[0135]

[0136] Under latching control, the transfer function needs to take into account the influence of the latching condition. When the latching condition is met, the current I in the transfer function is forced to zero, so the transfer function becomes:

[0137] F(s)=0h≥h max

[0138] When the lockout condition is not met, the transfer function returns to:

[0139]

[0140] Where n is the number of turns of the coil, B is the magnetic field strength, L is the length of the coil that cuts the magnetic field, α is the angle between the current direction and the magnetic field direction, and L h is the inductance value, S is the complex frequency domain variable, R is the resistance value, h is the oscillation module position, h max is the lockout threshold;

[0141] The power generation module is connected to the output end of the control module, receives the optimal power input instruction transmitted by the control module and transmits power. The power generation module is connected to the active oscillation module, and supplies energy to the active oscillation device for active oscillation.

[0142] The present invention also discloses a wave energy active oscillation method of a beaded structure, the method steps are as follows:

[0143] S1 starts the bead-type wave energy oscillation generator, and the oscillation detection module captures the current amplitude through the sensor;

[0144] S2 defines the amplitude setting value h min and h max , if the current amplitude is less than the set value h min , the active oscillation device will start to work. If the current amplitude is greater than or equal to the set value h max , then the locking control is started and the current input is locked; the current amplitude of S2 is less than the set value h min :

[0145] The control module receives the input amplitude of the oscillation detection module and adopts a multi-algorithm collaborative optimization control strategy. It integrates the predictive control algorithm, fuzzy control algorithm and stability control algorithm to obtain the control quantity u(t), feedback index efficiency η(t) and stability S(t). After normalization, the combined multidimensional feature vector V(t) = [u(t), η(t), S(t)] T , as the decision output and triggering the logic branch, the optimal solution is outputted by the weighted output multi-algorithm as the power required by the active oscillation device;

[0146] The S3 control module generates the optimal power input command and transmits it to the power generation module;

[0147] The S4 power generation module is an active oscillation device that performs active oscillation to maximize energy capture efficiency.

[0148] The beaded wave energy oscillation generator used in this embodiment is as follows: Figure 3 As shown, the beaded overall structure presents a center-and-spoke beaded structure.

[0149] The central circular component is the main floating body, and six wave energy power generation mechanisms are distributed around it. They are connected to the floating body through arm-like connecting structures, forming a beaded structure.

[0150] The floating body adopts a multi-section modular design to ensure increased wave energy transfer efficiency;

[0151] The oscillator is installed inside the float and is an inverted conical stainless steel structure. Its mass distribution has been optimized by finite element analysis, with the center of gravity tilted downward to improve heave response sensitivity. When the float absorbs wave energy and generates heaving motion, the oscillator will move up and down under the influence of the float.

[0152] The cylindrical cam mechanism adopts a variable-lead spiral groove design to convert the vertical swing motion of the oscillator into continuous unidirectional rotation of the gear shaft. The cylindrical cam mechanism cooperates with the oscillator to convert the up and down linear motion of the oscillator into rotational motion. The cylindrical cam mechanism drives the gear shaft to rotate, and the gear shaft drives the permanent magnet rotor to rotate, cutting the magnetic lines of force generated by the winding to generate electricity.

[0153] The above description of the embodiments enables one skilled in the art to implement or use the present invention. Various modifications to the embodiments will be readily apparent to those skilled in the art. The general principles of the present invention may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention should not be limited to the embodiments shown herein, but should encompass the widest range consistent with the principles and novel features disclosed herein.

Claims

1. A wave energy oscillation system with a beaded structure, characterized in that: The system includes a beaded wave energy oscillation generator, a control module and a power generation module; The beaded wave energy oscillation generator includes an oscillation detection module and an active oscillation device. The oscillation detection module is provided with a sensor for detecting the amplitude currently acting on the beaded wave energy oscillation generator and inputting the amplitude into the control module. If the amplitude is less than the set value h min , the active oscillation device operates, if the amplitude is greater than or equal to the set value h max , then start the locking control and lock the current input; The input end of the control module is connected to the oscillation detection module. The control module adopts a multi-algorithm collaborative optimization control strategy, integrating a predictive control algorithm, a fuzzy control algorithm, and a stability control algorithm. Based on the input amplitude of the oscillation detection module, the control module calculates the optimal power required by the current active vibration isolation device, generates an optimal power input instruction, and transmits it to the power generation module. The power generation module is connected to the output end of the control module, receives the optimal power input instruction transmitted by the control module to transmit power, and is connected to the active oscillation module to supply energy to the active oscillation device for active oscillation.

2. The wave energy oscillation system of the beaded structure according to claim 1 is characterized in that: The control module multi-algorithm collaborative optimization control strategy is as follows: The power required by the active oscillation device is used as the optimal solution for the collaborative output of multiple algorithms. The dynamic compensation term K(t) is output based on the predictive control algorithm. The fuzzy control algorithm is used to adjust the gain in real time according to K(t) to obtain the final control quantity u(t) of the active oscillation device. The ratio of theoretical wave energy input power to actual power generation is defined as power generation efficiency η(t); the stability parameter S(t) is obtained according to the stability control algorithm; The control quantity u(t), feedback index efficiency η(t), and stability S(t) are normalized and combined into a multidimensional feature vector V(t) = [u(t), η(t), S(t)] T , as the decision output; The following logic branches are triggered based on the output feature vector V(t): High frequency disturbance mode (u>u th ): Preferentially adopt the weighted output of the predictive control algorithm to suppress phase lag and dynamically adjust the x(k) matrix gain; Nonlinear time-varying mode (S>S th ): Fuzzy control algorithm is combined with rolling optimization correction to reduce cumulative error and dynamically match the initial reference value U0 through the rule table; Steady-state mode (η≥η th And S≤S th ): Fixed weight distribution, maintaining efficient and stable operation and reducing computational overhead; Among them, u th 、S th ,η th To calibrate the theoretical values of control quantity, feedback index efficiency and stability based on the measured sea condition data; According to V(t) trigger logic branch, define the algorithm weight coefficient, weight coefficient ω predict (t),ω fuzzy (t),ω adaptive (t) Dynamically allocate algorithm priority: The optimal solution is output by the weighted output multi-algorithm collaboratively as the power required by the active oscillation device. If u * (t) * (t) th , repeat the process of defining the tracking error to output the power required by the wave energy oscillation power generation module until the optimal solution is output.​ 3. The wave energy oscillation system of the beaded structure according to claim 2, characterized in that: The predictive control algorithm outputs the dynamic compensation term K(t) as follows: Design a time-varying controller based on state observation so that the closed-loop system satisfies: Define tracking error e(t): Where x_desired(t) represents the desired state trajectory at time t; According to the real-time changing characteristics of the error signal e(t), the dynamic compensation term K(t) is used to make the system output quickly converge to the set value range [0,1], suppress the phase lag caused by the randomness of the waves, and synchronize the power generation module with the wave frequency. The dynamic compensation term is: K(t)=α·exp(-βt)·|e(t)|+γ∫e(t)dt Where α is the attenuation coefficient, β is the exponential factor, γ is the integral gain coefficient, and |e(t)| is the current absolute value of the error.

4. The wave energy oscillation system of the beaded structure according to claim 3 is characterized in that: The specific steps for obtaining u(t) and η(t) are as follows: Construct fuzzy rule table based on expert experience: IF E is High AND ΔE is Negative,THEN U=K(t)*Medium Perform rule matching according to the fuzzy rule table to generate the initial reference value U0 of closed-loop control; According to K(t), the gain is adjusted in real time to process the nonlinear and time-varying characteristics of the wave, and the control quantity U(t) of the primary regulation link of the active oscillation module is obtained: in, represents the fuzzy reasoning synthesis operation, K(t) is the dynamic compensation term, U0 is the initial reference value of the closed-loop control, D(t) is the time-varying coefficient matrix, and ω(t) is the wave disturbance; The nonlinear state-space equation dynamically updates the system gain matrix to describe the coupling relationship between wave energy input and system oscillation: in, is the system displacement and velocity, wave energy capture related quantity, ω(t) is the wave disturbance, A(t), B(t), D(t) are the time-varying coefficient matrices; the discrete integral term ∫e(t)dt of the dynamic compensation term K(t) is calculated to generate the final control quantity of the active oscillation module driving signal: u(t)=[α·exp(-β|e(t)|)+γ∑e(j)Δt]·U(t)+D(t)ω(t) Where α is the attenuation coefficient, β is the exponential factor, γ is the integral gain coefficient, |e(t)| is the current error absolute value, ∑e(j)Δt is the cumulative sum of historical errors, Δt is the sampling period, j is the discrete time point, U(t) is the intermediate variable coupling term, and D(t) is the time-varying coefficient matrix; The theoretical wave energy input power is defined as: Where ρ(k) is the collected energy density, A wake (t) is the wave amplitude, f wave (t) is the wave frequency; Actual power generation P output (k) Evaluate power generation efficiency using sensor measurements:

5. The wave energy oscillation system of the beaded structure according to claim 4 is characterized in that: The stability parameter S(t) is obtained as follows: Based on the tracking error e(t) = x desired (t)-x(t), define the sequential error term and the cumulative error term, and obtain the stability control algorithm S(t): Among them, α S and β S is the adjustment coefficient, which is used to balance the weight of the sequential and cumulative errors.

6. The wave energy oscillation system of the beaded structure according to claim 1, characterized in that: The locking control operation process is as follows: The active oscillation method uses a linear motor that can receive electrical signals and convert them into linear motion, generating an Ampere force: F=nBILssinα Where n is the number of turns of the coil, B is the magnetic field strength, I is the current, L is the length of the coil that cuts the magnetic field, and α is the angle between the current direction and the magnetic field direction; the equilibrium equation is: Among them, U is the driving voltage, R is the resistance value, L h is the inductance value; combining the above two equations, we can get the transfer function: Under lockout control, the transfer function needs to take into account the influence of the lockout condition. When the lockout condition is met, the current I in the transfer function is forced to zero, so the transfer function becomes: F(s)=0h≥h max When the lockout condition is not met, the transfer function returns to: Among them, L h is the inductance value, S is the complex frequency domain variable, R is the resistance value, h is the oscillation module position, h max is the lockout threshold.

7. A wave energy active oscillation method of a beaded structure according to any one of claims 1 to 6, characterized in that: The active oscillation method comprises the following steps: S1 starts the bead-type wave energy oscillation generator, and the oscillation detection module captures the current amplitude through the sensor; S2 defines the amplitude setting value h min and h max , if the current amplitude is less than the set value h min , the active oscillation device will start to work. If the current amplitude is greater than or equal to the set value h max , then start the locking control and lock the current input; S2 current amplitude is less than the set value h min : The control module receives the input amplitude of the oscillation detection module and adopts a multi-algorithm collaborative optimization control strategy. It integrates the predictive control algorithm, fuzzy control algorithm and stability control algorithm to obtain the control quantity u(t), feedback index efficiency η(t) and stability S(t). After normalization, the combined multidimensional feature vector V(t) = [u(t), η(t), S(t)] T , as the decision output and triggering the logic branch, the optimal solution is outputted by the weighted output multi-algorithm as the power required by the active oscillation device; The S3 control module generates the optimal power input command and transmits it to the power generation module; The S4 power generation module is an active oscillation device that performs active oscillation to maximize energy capture efficiency.

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