Wave energy equipment self-adaptive adjustment method and system based on data feedback

Abstract

The invention discloses a wave energy equipment self-adaptive adjustment method and system based on data feedback, and belongs to the technical field of wave energy. According to the method, wave vibration behavior data, weighing block vibration behavior data and liquid volume and pressure information are collected in real time through an acceleration sensor, a flow meter and a pressure sensor; equidistant sampling of wave vibration data is realized by using an interpolation algorithm, and a wave dominant frequency is quantified and a sliding window is set in combination with self-correlation analysis; the mass variation of the weighing block is dynamically calculated based on a sliding mode surface control model, control parameters are updated in real time through a discrete time self-adaptive feedback model, the flow of a water suction pump is adjusted to change the mass of the weighing block, the vibration frequency of the weighing block is made to be matched with the wave dominant frequency, and closed-loop control is formed. According to the invention, the problems of low energy conversion efficiency and weak anti-interference capability caused by frequency mismatch of existing wave energy equipment are solved, and through dynamic quality adjustment and a self-adaptive feedback mechanism, the adaptability of the equipment to a complex marine environment and the energy capture efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wave energy, in particular to a wave energy device adaptive adjustment method and system based on data feedback. BACKGROUND

[0002] As a renewable and clean energy, wave energy has the advantages of wide distribution and large reserves. However, its randomness and instability pose high requirements on the adaptability of energy conversion equipment. Traditional wave energy devices mostly use fixed structures or mechanical adjustment methods, such as adjusting device parameters through spring damping systems or hydraulic devices. However, such methods have the following defects: Insufficient frequency matching capability: the main frequency of waves changes dynamically with the marine environment, and devices with fixed parameters are difficult to track frequency changes in real time, resulting in decreased resonance efficiency; Poor disturbance resistance: external disturbances (such as sudden winds and irregular waves) can easily cause system oscillation, and traditional control algorithms (such as PID) are prone to overshoot or response lag in nonlinear scenarios; Adjustment lag: mechanical adjustment relies on manual preset thresholds or offline calculations, and cannot achieve real-time closed-loop feedback, making it difficult to adapt to high-frequency dynamic environments.

[0003] In recent years, some research has attempted to introduce sensor data feedback mechanisms, such as monitoring wave vibration frequency through acceleration sensors and optimizing device response with mass adjustment devices. However, existing solutions mostly rely on single parameter adjustment or linear control models, and have limited adaptability to complex working conditions, and have not effectively solved the steady-state deviation problem caused by historical error accumulation. Therefore, there is an urgent need for a technical solution that combines real-time data feedback, dynamic mass adjustment, and adaptive control to improve the operating efficiency and stability of wave energy devices. SUMMARY

[0004] The present application proposes a wave energy device adaptive adjustment method and system based on data feedback to address the above technical bottlenecks. By dynamically adjusting the mass of the weighing block, the system achieves accurate frequency tracking, and combines an oscillation suppression mechanism to reduce chattering, ultimately achieving efficient and stable wave energy capture.

[0005] A wave energy device adaptive adjustment system based on data feedback, the system comprises: a data acquisition module, a data preprocessing module, a synovial membrane control module, and a mass dynamic adjustment prediction module; The data acquisition module is used to collect wave vibration behavior data in the sea area, weighing block vibration behavior data, liquid volume introduced and exported by the water pump into the weighing block, and pressure at the liquid inlet and outlet; The data preprocessing module realizes equal-interval sampling of the vibration behavior data of the waves in the sea area by interpolation, and quantifies the main frequency of the vibration behavior of the waves to set a sliding window. The sliding film control module is configured to calculate the mass of the weighing block by importing and exporting the liquid volume in the weighing block during the equal-interval sampling of the amplitude point time nodes of the vibration behavior data, and quantify the frequency of the vibration behavior of the weighing block; and quantify the frequency tracking error by the sliding surface control model. The mass dynamic adjustment prediction module is configured to analyze the mass dynamic adjustment prediction of the weighing block by the mass dynamic adjustment prediction model of the weighing block, update the sliding surface control model and the mass dynamic adjustment prediction model of the weighing block by the adaptive update feedback model under discrete time, and output the mass of the weighing block after the update to control the flow of the water pump, thereby realizing the closed loop of adaptive adjustment of the wave energy equipment.

[0006] Further, the data acquisition module comprises an acceleration sensor, a flow meter and a pressure sensor. The acceleration sensor is configured to acquire the vibration behavior data of the waves in the sea area and the vibration behavior data of the weighing block. The flow meter is configured to acquire the liquid volume in the isolation layer. The pressure sensor is configured to acquire the pressure of the liquid at the inlet and outlet of the conduit.

[0007] Further, the data preprocessing module comprises an interpolation unit and a sliding window unit. The interpolation unit is configured to perform equal-interval sampling of the amplitude point time nodes of the vibration behavior data of the waves by interpolation. The sliding window unit is configured to calculate the autocorrelation value of the time delay by iteration, and capture the time delay when the iteration stops to obtain the main frequency of the vibration behavior of the waves under different time delays.

[0008] Further, the sliding film control module comprises a weighing block vibration frequency quantization unit and a sliding surface control model unit. The weighing block vibration frequency quantization unit is configured to record the mass of the weighing block at the amplitude point time nodes based on the equal-interval amplitude point time nodes to calculate the vibration frequency of the weighing block. The sliding surface control model unit is configured to construct a sliding surface control model based on the main frequency of the vibration behavior of the waves and the vibration frequency of the weighing block to represent the frequency tracking error.

[0009] Further, the mass dynamic adjustment prediction module comprises a mass dynamic adjustment prediction model unit, an adaptive update feedback model unit under discrete time, and a water pump flow control unit. The mass dynamic adjustment prediction model unit is configured to construct a mass dynamic adjustment prediction model of the weighing block based on the sliding mode control model, so as to calculate a mass dynamic adjustment prediction value of the weighing block. The discrete-time adaptive update feedback model unit is configured to construct a discrete-time adaptive update feedback model, so as to feed back the updated cumulative error gain coefficient, the high-low frequency switching gain and the sliding mode surface linear proportionality coefficient to the sliding mode control model and the mass dynamic adjustment prediction model of the weighing block. The water pump flow control unit is configured to update the mass of the weighing block based on the mass dynamic adjustment prediction value of the weighing block, and control the flow of the water pump.

[0010] A data feedback-based adaptive adjustment method for a wave energy device, the method comprising the following steps: In step S100, an acceleration sensor is configured to collect vibration behavior data of waves in a sea area and vibration behavior data of a weighing block, a flow meter is configured to collect the volume of liquid introduced into and discharged from the weighing block by a water pump, and a pressure sensor is configured to collect the pressure at the inlet and outlet of the liquid, respectively. In step S200, the vibration behavior data of waves in the sea area is equally spaced sampled by interpolation, and the main frequency of the vibration behavior of the waves is quantified to set a sliding window. In step S300, the mass of the weighing block is calculated based on the volume of liquid introduced into and discharged from the weighing block, and the frequency of the vibration behavior of the weighing block is quantified during the equally spaced sampling of the amplitude point time node of the vibration behavior data; the frequency tracking error is quantified by the sliding mode control model. In step S400, the mass dynamic adjustment prediction value of the weighing block is analyzed by the mass dynamic adjustment prediction model of the weighing block; the sliding mode control model and the mass dynamic adjustment prediction model of the weighing block are updated by the discrete-time adaptive update feedback model, and the updated mass of the weighing block is output to control the flow of the water pump, thereby realizing the closed loop of adaptive adjustment of the wave energy device.

[0011] Further, the specific implementation process of step S100 comprises: In step S101, the acceleration sensor is configured to collect the vibration behavior data of waves in a sea area and the vibration behavior data of a weighing block, respectively, and to plot the vibration behavior data of waves in a two-dimensional coordinate system, wherein the vibration behavior data of waves includes the vibration amplitude and the amplitude point time node of waves, and the horizontal coordinate value of the two-dimensional coordinate system corresponds to the amplitude point time node, and the vertical coordinate value of the two-dimensional coordinate system corresponds to the vibration amplitude. Step S102: The operation of a water pump is used to introduce and export liquid into the isolation layer. The weighing block is enclosed by a hollow cement block. The weight of the weighing block is adjusted by using an isolation layer in the enclosed hollow internal space. The outer packaging is equipped with a liquid conduit interface. The weight of the weighing block is changed by introducing and exporting liquid into the isolation layer through the conduit interface. The density difference between the liquid and the density of the cement block is within a preset error range. The other end of the conduit interface is connected to a water pump. A pressure sensor is used to collect the pressure of the liquid at the inlet and outlet of the conduit, respectively. A flow meter is used to collect the volume of liquid in the isolation layer.

[0012] Furthermore, the specific implementation process of step S200 includes: Step S201: In a two-dimensional coordinate system, the wave vibration behavior data is sampled at equal intervals between amplitude and time nodes using interpolation, and the wave vibration behavior data is recorded as follows: ,in, This represents the time node of the i-th amplitude point. Indicates the amplitude point time node The vibration amplitude of the downsampled sample; Step S202: Initialize delay And calculate the delay. autocorrelation value Let N represent the total number of amplitude point time nodes, and let Delay Iterative calculation of the autocorrelation value, and A preset autocorrelation threshold is set when the first occurrence of... The iteration stops when the autocorrelation value reaches a threshold, and the time delay is extracted when the iteration stops. ; Based on the continuity of vibration behavior data, a sliding window is constructed, and the scale of the sliding window is set to time delay. , the amplitude point time node The sliding window to which it belongs is denoted as Real-time calculation and updating of the dominant frequency of wave vibration behavior , Indicates in sliding window The dominant frequency of the wave vibration behavior within.

[0013] Furthermore, the specific implementation process of step S300 includes: Step S301: Record the amplitude point time nodes based on equally spaced amplitude point time nodes. The mass of the weighing block And calculate the amplitude point time node. Vibration frequency of the weighing block Where k is the stiffness of the weighing block and is determined by the material of the outer packaging cement block. , represents the quality of the hollow cement block, is the liquid density, is the liquid volume in the isolation layer under the amplitude point time node collected by the flow meter; Step S302: Based on the main frequency of the vibration behavior of the wave and the vibration frequency of the weighing block, a sliding mode surface control model is constructed: ; In the formula, is the sliding mode surface function and represents the frequency tracking error, is the cumulative error gain coefficient, represents the starting time node of the sliding window . In the above method, the first term directly represents the current frequency deviation, and the second term indirectly represents the cumulative historical error, which is used to enhance the steady-state accuracy.

[0014] Further, the specific implementation process of the step S400 includes: Step S401: Based on the sliding mode surface control model, a mass dynamic adjustment prediction model of the weighing block is constructed: ; In the formula, represents the mass dynamic adjustment prediction quantity of the weighing block, if is a positive value, it means increasing the mass, and if is a negative value, it means reducing the mass, represents the high-low frequency switching gain, represents the linear proportional coefficient of the sliding mode surface; In the above method, is a sign function, can generate a high-low frequency switching control quantity, which is used to offset external disturbances and model uncertainties, when , i.e. , , , the mass of the weighing block is reduced, and after the mass of the weighing block is reduced, it is fed back to the vibration frequency algorithm formula of the weighing block, so that the vibration frequency of the weighing block increases, and further , , i.e. , , , the mass of the weighing block is increased, and after the mass of the weighing block is increased, it is fed back to the vibration frequency algorithm formula of the weighing block, so that the vibration frequency of the weighing block decreases, and further ​When , resonance is stable at this time, is small, , the mass of the weighing block does not need to be changed, ; is a smooth control input for reducing the chattering phenomenon; Step S402: Construct an adaptive updating feedback model under discrete time, and substitute the updated cumulative error gain coefficient, the high-low frequency switching gain, and the linear proportional coefficient of the sliding mode surface into the sliding mode surface control model and the mass dynamic adjustment prediction model of the weighing block. The adaptive updating feedback model under discrete time is: ; In the formula, , and are preset adaptive adjustment coefficients and are respectively used to avoid overshoot of the cumulative error gain coefficient , the high-low frequency switching gain , and the linear proportional coefficient of the sliding mode surface , represents the standard deviation of the vibration amplitude of the weighing block within a sliding window , represents the preset maximum allowed vibration standard deviation; In the above method, when the sliding mode surface error is large, i.e., in the case of , resonance is unstable at this time, is large, and the mass of the weighing block needs to be adjusted, then is used to track the steady-state performance and determine the fine adjustment of the mass of the weighing block, at the same time, reflects the size of the oscillation deviation, and the size of the prediction quantity is determined by and . The larger the oscillation deviation is, and are larger, and vice versa; Based on the mass dynamic adjustment prediction quantity of the weighing block, the mass of the weighing block is updated , and the flow rate of the water pump is controlled . In the formula, t represents the independent variable of the time domain, is the rated head of the water pump, is the effective head of the water pump, and , is a preset pipe resistance coefficient, is the liquid pressure at the outlet of the pipe, is the liquid pressure at the inlet of the pipe, h is the vertical height difference between the outlet and the inlet of the conduit, and g is the acceleration of gravity.

[0015] Compared with the prior art, the present application has the beneficial effects that: in the wave energy device adaptive adjustment method and system based on data feedback provided by the present application, the wave vibration behavior data, the weighing block vibration data and the liquid volume and pressure information are collected in real time by using the acceleration sensor, the flow meter and the pressure sensor; the wave vibration data is equally sampled by using the interpolation algorithm, the wave main frequency is quantified by combining the autocorrelation analysis and setting the sliding window; the mass change of the weighing block is dynamically calculated based on the sliding mode control model, the control parameters are updated in real time by using the discrete time adaptive feedback model, the flow of the water pump is adjusted to change the mass of the weighing block, so that the vibration frequency of the weighing block is matched with the wave main frequency, and the closed-loop control is formed. The present application solves the problems of low energy conversion efficiency and weak anti-interference ability of the existing wave energy device caused by frequency mismatch, and through the dynamic mass adjustment and the adaptive feedback mechanism, the adaptability of the device to the complex marine environment and the energy capture efficiency are improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application together with the embodiments thereof, and explain the present application, but do not constitute a limitation of the present application.

[0017] Figure 1 is a step schematic diagram of the wave energy device adaptive adjustment method based on data feedback of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0019] In the present embodiment one: a wave energy device adaptive adjustment system based on data feedback is provided, which comprises: a data acquisition module, a data preprocessing module, a sliding film control module and a mass dynamic adjustment prediction module.

[0020] The data acquisition module is used for collecting the vibration behavior data of the wave in the sea area, the vibration behavior data of the weighing block, the liquid volume introduced into the weighing block by the water pump and the pressure of the liquid inlet and outlet. Exemplarily, the data acquisition module comprises an acceleration sensor, a flow meter and a pressure sensor. The acceleration sensor is used for collecting the vibration behavior data of the wave in the sea area and the vibration behavior data of the weighing block. A flow meter for collecting the volume of liquid in the isolation layer; A pressure sensor for collecting the pressure of liquid at the inlet and outlet of the conduit.

[0021] A data preprocessing module for realizing equal-interval sampling of the vibration behavior data of the waves in the sea area by interpolation and quantifying the dominant frequency of the vibration behavior of the waves to set a sliding window; Illustratively, the data preprocessing module comprises an interpolation unit and a sliding window unit; The interpolation unit realizes equal-interval sampling of the amplitude point time node of the vibration behavior data of the waves by interpolation; The sliding window unit is configured to calculate the autocorrelation value of the time delay by iteration, and capture the time delay when the iteration stops to obtain the dominant frequency of the vibration behavior of the waves under different time delays.

[0022] A sliding mode control module for calculating the mass of the weighing block by importing and exporting the volume of liquid in the weighing block during the equal-interval sampling of the amplitude point time node of the vibration behavior data, and quantifying the frequency of the vibration behavior of the weighing block; and quantifying the frequency tracking error by a sliding mode surface control model; Illustratively, the sliding mode control module comprises a weighing block vibration frequency quantification unit and a sliding mode surface control model unit; The weighing block vibration frequency quantification unit records the mass of the weighing block at the amplitude point time node to calculate the vibration frequency of the weighing block based on the equal-interval amplitude point time node; The sliding mode surface control model unit constructs a sliding mode surface control model based on the dominant frequency of the vibration behavior of the waves and the vibration frequency of the weighing block to represent the frequency tracking error.

[0023] A mass dynamic adjustment prediction module for analyzing the mass dynamic adjustment prediction quantity of the weighing block by a mass dynamic adjustment prediction model of the weighing block, updating the sliding mode surface control model and the mass dynamic adjustment prediction model of the weighing block by a discrete-time adaptive update feedback model, and outputting the mass of the weighing block after the update to control the flow of the water pump and realize the closed loop of adaptive adjustment of the wave energy device; Illustratively, the mass dynamic adjustment prediction module comprises a mass dynamic adjustment prediction model unit, a discrete-time adaptive update feedback model unit, and a water pump flow control unit; The mass dynamic adjustment prediction model unit constructs a mass dynamic adjustment prediction model of the weighing block based on the sliding mode surface control model to calculate the mass dynamic adjustment prediction quantity of the weighing block; The discrete-time adaptive update feedback model unit is configured to construct a discrete-time adaptive update feedback model to feed back the updated cumulative error gain coefficient, high-low frequency switching gain, and sliding mode surface linear proportion coefficient to the sliding mode surface control model and the mass dynamic adjustment prediction model of the weighing block. The water pump flow control unit updates the mass of the weighing block based on the predicted dynamic adjustment of the weighing block's mass and controls the water pump flow rate.

[0024] Please see Figure 1 In this second embodiment: a wave energy device adaptive adjustment method based on data feedback is provided to be applicable to the above-described first embodiment. The method includes the following steps: Step S100: The accelerometer collects the vibration behavior data of the waves in the sea area and the vibration behavior data of the weighing block; the flow meter collects the volume of liquid introduced and exported by the water pump into the weighing block; and the pressure sensor is used to collect the pressure at the liquid inlet and outlet respectively. For example, accelerometers are used to collect vibration behavior data of waves and weighing blocks in the sea area, and the vibration behavior data of waves is plotted in a two-dimensional coordinate system. The vibration behavior data of waves includes the vibration amplitude and amplitude point time node of waves, and the horizontal axis value of the two-dimensional coordinate system corresponds to the amplitude point time node, and the vertical axis value of the two-dimensional coordinate system corresponds to the vibration amplitude. The operation of a water pump enables the introduction and export of liquid through the isolation layer. The weighing block is enclosed by a hollow cement block. The weight of the weighing block is adjusted by using an isolation layer within the sealed hollow internal space. The outer packaging is equipped with a liquid conduit interface. The weight of the weighing block is changed by introducing and exporting liquid through the isolation layer via the conduit interface. The density difference between the liquid and the cement block is within a preset error range. The other end of the conduit interface is connected to a water pump. Pressure sensors are used to collect the pressure of the liquid at the inlet and outlet of the conduit, respectively, and a flow meter is used to collect the liquid volume in the isolation layer.

[0025] Step S200: By interpolation, the vibration behavior data of waves in the sea area are sampled at equal intervals, and the dominant frequency of the wave vibration behavior is quantized in order to set a sliding window; For example, in a two-dimensional coordinate system, wave vibration behavior data is sampled at equal intervals between amplitude and time nodes using interpolation, and the wave vibration behavior data is denoted as... ,in, This represents the time node of the i-th amplitude point. Indicates the amplitude point time node The vibration amplitude of the downsampled sample; Initialization delay And calculate the delay. autocorrelation value N represents the total number of amplitude point time nodes, let Delay The iterative calculation of the autocorrelation value, and A preset autocorrelation threshold is set when the first occurrence of... The autocorrelation value threshold is used to stop iteration, and the time delay is extracted when iteration is stopped ; Based on the continuity of the vibration behavior data, a sliding window is constructed, and the size of the sliding window is set as the time delay , the amplitude point time node is recorded as , the dominant frequency of the wave vibration behavior is calculated and updated in real time , represents the dominant frequency of the wave vibration behavior in the sliding window .

[0026] Step S300: During the amplitude point time node interval sampling process of the vibration behavior data, the mass of the weighing block is calculated by importing and exporting the liquid volume in the weighing block, and the frequency of the vibration behavior of the weighing block is quantified; The frequency tracking error is quantified by the sliding mode control model For example, based on the equally spaced amplitude point time nodes, the amplitude point time nodes of the mass of the weighing block are recorded, and the vibration frequency of the weighing block at the amplitude point time nodes is calculated , wherein k is the stiffness of the weighing block and is calibrated by the material of the peripheral packaging cement block, , represents the mass of the hollow cement block, is the liquid density, is the liquid volume in the isolation layer at the amplitude point time nodes collected by the flowmeter; Based on the dominant frequency of the wave vibration behavior and the vibration frequency of the weighing block, a sliding mode control model is constructed: ; In the formula, is the sliding mode function and represents the frequency tracking error, is the cumulative error gain coefficient, represents the starting time node of the sliding window .

[0027] Step S400: The mass dynamic adjustment prediction model of the weighing block is adjusted, and the mass dynamic adjustment prediction of the weighing block is analyzed; The sliding mode control model and the mass dynamic adjustment prediction model of the weighing block are updated by updating the feedback model in discrete time, and the updated mass of the weighing block is output to control the flow of the water pump, realizing the closed loop of the adaptive adjustment of the wave energy device For example, based on the sliding mode control model, a mass dynamic adjustment prediction model of the weighing block is constructed: ; wherein, represents the mass dynamic adjustment prediction of the weighing block, if is positive, it represents an increase in mass, if is negative, it represents a decrease in mass, represents the high-low frequency switching gain, represents the linear proportional coefficient of the sliding mode surface; The adaptive updating feedback model under discrete time is constructed, and the updated cumulative error gain coefficient, high-low frequency switching gain and linear proportional coefficient of the sliding mode surface are substituted into the sliding mode surface control model and the mass dynamic adjustment prediction model of the weighing block. The adaptive updating feedback model under discrete time is: ; wherein, , and are preset adaptive adjustment coefficients and are respectively used to avoid overshoot of the cumulative error gain coefficient , the high-low frequency switching gain and the linear proportional coefficient of the sliding mode surface , represents the standard deviation of the vibration amplitude of the weighing block within the sliding window , represents the preset maximum allowable vibration standard deviation; It should be noted that the adaptive updating feedback model under discrete time can also be converted into a model under continuous time, but the model under discrete time is more suitable for actual scene operation. The model under continuous time is: ; wherein, , and respectively represent the adaptive updating rates of the cumulative error gain coefficient , the high-low frequency switching gain and the linear proportional coefficient of the sliding mode surface ; Based on the mass dynamic adjustment prediction of the weighing block, the mass of the weighing block is updated , and the flow of the water pump is controlled , wherein t represents the independent variable of the time domain, is the rated head of the water pump, is the effective head of the water pump, and , is a preset conduit resistance coefficient, is the liquid pressure at the outlet of the conduit, is the liquid pressure at the inlet of the conduit, is the vertical height difference between the outlet and the inlet of the conduit, and g is the acceleration of gravity; It should be noted that the preset parameters involved in Example 2 include the difference between the density of the liquid and the density of the cement block being within a preset error range, the autocorrelation value threshold, and the preset adaptive adjustment coefficient. , and ), the maximum permissible standard deviation of vibration ( ) and catheter resistance coefficient ( The results need to be optimized through simulation experiments. In particular, the liquid can be a near-saturated solution of equal mass and low cost. The density of ordinary cement blocks is between 2.2 and 2.5. , Its density is between 1.9 and 2.3. By slightly controlling the density of the solution, it can be made to match the density of the cement block.

[0028] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0029] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data feedback based adaptive adjustment method for wave energy devices, characterized in that, The method comprises the following steps: Step S100: The acceleration sensor collects the vibration behavior data of the waves in the sea area and the vibration behavior data of the weighing block, and the flowmeter collects the volume of the liquid introduced and discharged by the water pump into the weighing block, and the pressure sensor is used to collect the pressure of the liquid inlet and outlet respectively; Step S200: Through interpolation, the vibration behavior data of the waves in the sea area is equally spaced sampled, and the main frequency of the vibration behavior of the waves is quantified to set a sliding window; Step S300: During the equally spaced sampling of the amplitude point time node of the vibration behavior data, the mass of the weighing block is calculated by the volume of the liquid introduced and discharged into the weighing block, and the frequency of the vibration behavior of the weighing block is quantified; the frequency tracking error is quantified through the sliding mode surface control model; Step S400: The mass dynamic adjustment prediction model of the weighing block is used to analyze the mass dynamic adjustment prediction of the weighing block; the sliding mode surface control model and the mass dynamic adjustment prediction model of the weighing block are updated through the adaptive updating feedback model under discrete time, and the updated mass of the weighing block is output to control the flow of the water pump, realizing the closed loop of adaptive adjustment of the wave energy equipment.

2. The method of claim 1, wherein, The specific implementation process of the step S100 comprises: Step S101: The acceleration sensor collects the vibration behavior data of the waves in the sea area and the vibration behavior data of the weighing block respectively, and the vibration behavior data of the waves is depicted in a two-dimensional coordinate system, the vibration behavior data of the waves including the vibration amplitude and the amplitude point time node, and the horizontal coordinate value of the two-dimensional coordinate system corresponds to the amplitude point time node, and the vertical coordinate value of the two-dimensional coordinate system corresponds to the vibration amplitude; Step S102: The operation of introducing and discharging the liquid in the isolation layer is realized through the operation of the water pump, the weighing block is packaged with a hollow cement block as a sealed outer package, the mass of the weighing block is adjusted in levels by the isolation layer in the sealed hollow inner space, and the outer package is provided with a liquid conduit interface, the mass of the weighing block is changed by introducing and discharging the liquid in the isolation layer through the liquid conduit interface, the difference between the density of the liquid and the density of the cement block is within a preset error range, the other end of the liquid conduit interface is connected with the water pump, the pressure sensor is used to collect the pressure of the liquid at the inlet and outlet of the conduit respectively, and the flowmeter is used to collect the volume of the liquid in the isolation layer.

3. The method of claim 1, wherein, The specific implementation process of the step S200 comprises: Step S201: In a two-dimensional coordinate system, the amplitude-time node of the wave vibration behavior data is sampled at equal intervals by interpolation, and the wave vibration behavior data is recorded as wherein, represents the i-th amplitude-time node, represents the amplitude-time node the down-sampled vibration amplitude; Step S202: initialize the time delay , and calculate the autocorrelation value of the time delay , N represents the total number of the amplitude point time nodes, let , perform the iterative calculation of the autocorrelation value of the time delay , and , preset an autocorrelation value threshold, the iteration stops when the autocorrelation value threshold appears for the first time, and the time delay is extracted when the iteration stops ;​ Based on the continuity of the vibration behavior data, a sliding window is constructed, and the scale of the sliding window is set as the time delay , the amplitude point time node is recorded as , the dominant frequency of the wave vibration behavior is calculated and updated in real time , represents the dominant frequency of the wave vibration behavior in the sliding window .

4. The adaptive adjustment method of wave energy device based on data feedback according to claim 3, characterized in that, The specific implementation process of the step S300 comprises: Step S301: Record the amplitude point time nodes based on equally spaced amplitude point time nodes. The mass of the weighing block And calculate the amplitude point time node. Vibration frequency of the weighing block Where k is the stiffness of the weighing block and is determined by the material of the outer packaging cement block. , Indicates the mass of hollow cement blocks. For the density of the liquid, For the amplitude point time node collected by the flow meter The volume of liquid in the lower isolation layer; Step S302: Based on the main frequency of the vibration behavior of the waves and the vibration frequency of the weighing block, a sliding mode surface control model is constructed: ； In the formula, It is a sliding surface function that characterizes the frequency tracking error. This is the cumulative error gain coefficient. Indicates sliding window The starting time node.

5. A data feedback based adaptive adjustment method for a wave energy device according to claim 4, characterized in that, The specific implementation process of the step S400 comprises: Step S401: Based on the sliding mode surface control model, a mass dynamic adjustment prediction model of the weighing block is constructed: ； wherein, represents the mass dynamic adjustment prediction of the weight block, if is positive, it indicates an increase in mass, if is negative, it indicates a decrease in mass, represents the high-low frequency switching gain, represents the linear proportional coefficient of the sliding mode surface; Step S402: An adaptive updating feedback model under discrete time is constructed, and the updated cumulative error gain coefficient, high-low frequency switching gain and sliding mode surface linear proportion coefficient are substituted into the sliding mode surface control model and the mass dynamic adjustment prediction model of the weighing block, and the adaptive updating feedback model under discrete time is: ； wherein, , and are preset adaptive adjustment coefficients and are respectively used to avoid overshoot of the cumulative error gain coefficient , the high-low frequency switching gain and the linear proportion coefficient of the sliding mode surface , represents the standard deviation of the vibration amplitude of the weight block within the sliding window , represents the preset maximum allowable vibration standard deviation; Dynamic adjustment of the mass of the weighing block based on a prediction of the mass of the weighing block and control the flow rate of the pump where t represents the independent variable of the time domain, is the rated head of the pump, is the effective head of the pump, and , is a predetermined duct resistance coefficient, is the liquid pressure at the outlet of the duct, is the liquid pressure at the inlet of the duct, is the vertical height difference between the outlet and the inlet of the duct, and g is the acceleration due to gravity.

6. A data feedback based adaptive adjustment system for wave energy devices, performing the data feedback based adaptive adjustment method according to any of claims 1-5, characterized by, The system comprises a data acquisition module, a data preprocessing module, a sliding film control module and a mass dynamic adjustment prediction module; The data acquisition module is configured to acquire vibration behavior data of waves in the sea area, vibration behavior data of the weighing block, liquid volume introduced into and discharged from the weighing block by the water pump, and pressure of the liquid at the inlet and outlet of the liquid; The data preprocessing module is configured to realize equal-interval sampling of the vibration behavior data of the waves in the sea area by interpolation, and to quantize a main frequency of the vibration behavior of the waves to set a sliding window. The sliding film control module is configured to calculate the mass of the weighing block by the liquid volume introduced into and discharged from the weighing block during equal-interval sampling of the amplitude point time node of the vibration behavior data, and to quantize a frequency of the vibration behavior of the weighing block; and to quantize a frequency tracking error by a sliding surface control model. The mass dynamic adjustment prediction module is configured to analyze a mass dynamic adjustment prediction quantity of the weighing block by a mass dynamic adjustment prediction model of the weighing block, to update the sliding surface control model and the mass dynamic adjustment prediction model of the weighing block by a discrete-time adaptive update feedback model, and to output the mass of the weighing block after the update to control the flow of the water pump and realize a closed loop of adaptive adjustment of the wave energy device.

7. A data feedback based adaptive adjustment system for a wave energy device according to claim 6, characterized in that, The data acquisition module comprises an acceleration sensor, a flow meter, and a pressure sensor. The acceleration sensor is configured to acquire vibration behavior data of waves in the sea area and vibration behavior data of the weighing block. The flow meter is configured to acquire the liquid volume in the isolation layer. The pressure sensor is configured to acquire the pressure of the liquid at the inlet and outlet of the conduit.

8. A data feedback based adaptive adjustment system for a wave energy device according to claim 6, characterized in that, The data preprocessing module comprises an interpolation unit and a sliding window unit. The interpolation unit is configured to perform equal-interval sampling of the amplitude point time node of the vibration behavior data of the waves by interpolation. The sliding window unit is configured to calculate an autocorrelation value of a time delay by iteration, and to capture the time delay when the iteration stops to obtain the main frequency of the vibration behavior of the waves under different time delays.

9. A data feedback based adaptive adjustment system for a wave energy device according to claim 6, characterized in that, The sliding film control module comprises a weighing block vibration frequency quantization unit and a sliding surface control model unit. The weighing block vibration frequency quantization unit is configured to record the mass of the weighing block at the amplitude point time node based on the equal-interval amplitude point time node to calculate the vibration frequency of the weighing block. The sliding surface control model unit is configured to construct a sliding surface control model based on the main frequency of the vibration behavior of the waves and the vibration frequency of the weighing block to represent a frequency tracking error.

10. A data feedback based adaptive adjustment system for a wave energy device according to claim 6, characterized in that, The mass dynamic adjustment prediction module comprises a mass dynamic adjustment prediction model unit, a discrete-time adaptive update feedback model unit, and a water pump flow control unit. The mass dynamic adjustment prediction model unit is configured to construct a mass dynamic adjustment prediction model of the weighing block based on the sliding surface control model to calculate a mass dynamic adjustment prediction quantity of the weighing block. The discrete-time adaptive update feedback model unit is configured to construct a discrete-time adaptive update feedback model to feed back an updated cumulative error gain coefficient, a high-low frequency switching gain, and a sliding surface linear proportionality coefficient to the sliding surface control model and the mass dynamic adjustment prediction model of the weighing block. The water pump flow control unit is configured to update the mass of the weighing block based on the mass dynamic adjustment prediction quantity of the weighing block and to control the flow of the water pump.

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