Wave impact intelligent locking protection method and system based on artificial intelligence

Abstract

The application discloses a wave impact intelligent locking protection method and system based on artificial intelligence and belongs to the technical field of wave energy. Wave oscillation waveform data is collected, a wave peak time node is identified and time periods are divided, a vibration body reciprocating vibration state is monitored and a waveform is fitted; a negative vibration characteristic waveform is extracted, an observation sampling frequency of a pressure monitoring sensor is quantified; a load pressure value and a time node at a negative half-wave form trough are captured, a double-scale wave window model is constructed to quantize high-frequency energy degree; a load sampling frequency is calculated based on the high-frequency energy degree, the sampling frequency is calibrated through an intelligent locking protection model and is fed back to the sensor. The application combines time domain and energy domain characteristics of a vibration frequency, effectively solves the problems of rigid sampling strategy, one-sided feature analysis and missing calibration mechanism in the prior art, and improves the adaptability and efficiency of intelligent locking protection under a wave impact environment.

Description

Technical Field

[0001] This invention relates to the field of wave energy technology, specifically to an intelligent wave impact protection method and system based on artificial intelligence. Background Technology

[0002] In fields such as marine engineering, port facilities and ship equipment, the dynamic load of wave impact on the vibrating body and its base is an important factor leading to structural fatigue and damage. In the existing technology, the monitoring and protection system for wave impact usually uses a fixed sampling frequency to monitor the load pressure of the vibrating body, which is difficult to adapt to the dynamic characteristics of wave impact such as periodicity, nonlinearity and uneven energy distribution in real time.

[0003] Specifically, traditional methods have the following technical problems:

[0004] The sampling strategy is singular: the sampling method based on a fixed time interval cannot dynamically adjust the sampling frequency according to the energy characteristics of wave impact (such as high-frequency impact areas), which may lead to data loss in high-energy impact scenarios or resource waste in low-energy scenarios.

[0005] Incomplete feature extraction: It relies solely on the time-domain features of the vibrating body (such as vibration frequency), ignoring the influence of high-frequency energy distribution in the negative vibration waveform on the load pressure, making it difficult to accurately capture the instantaneous high-intensity load of wave impact;

[0006] Insufficient intelligent calibration capability: The lack of an adaptive calibration mechanism for the sampling frequency of pressure monitoring sensors makes it impossible to dynamically optimize the monitoring strategy by combining real-time load pressure and benchmark values, resulting in limited response accuracy and efficiency of the locking protection system. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent wave impact protection method and system based on artificial intelligence to solve the problems mentioned in the background art.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] An artificial intelligence-based intelligent wave impact locking protection system, comprising: a data acquisition module, a feature extraction module, a frequency calculation module, and an intelligent calibration control module;

[0010] The data acquisition module is used to acquire wave oscillation waveform data and the reciprocating vibration state of the vibrating body, generate the reciprocating waveform of the vibrating body, and acquire the load pressure value of the vibrating body acting on the base through the pressure monitoring sensor.

[0011] The feature extraction module, based on the negative vibration feature waveform, captures the load pressure value and time node at the trough of the negative half-waveform, constructs a dual-scale wavelet window model, and quantifies the high-frequency energy of each negative half-waveform.

[0012] The frequency calculation module calculates the observation sampling frequency based on the reciprocating waveform and the load sampling frequency based on the high-frequency energy level.

[0013] The intelligent calibration control module is used to construct an intelligent locking protection model to calculate the calibration sampling frequency and feed it back to the pressure monitoring sensor to adjust the sampling strategy.

[0014] Furthermore, the data acquisition module includes a wave monitoring unit, a vibration monitoring unit, and a pressure monitoring unit;

[0015] The wave monitoring unit is used to collect wave oscillation waveform data, identify periodic wave peak time nodes, and divide the sampling period.

[0016] The vibration monitoring unit is used to monitor the reciprocating vibration state of the vibrating body during the sampling period, and to fit the vibration amplitude to form a reciprocating waveform with the non-impact static position as the reference plane.

[0017] The pressure monitoring unit is used to collect the load pressure value of the vibrating body acting on the base.

[0018] Furthermore, the feature extraction module includes a negative half-wave processing unit, a dual-scale window construction unit, and an energy calculation unit;

[0019] The negative half-wave processing unit is used to extract the negative envelope of the reciprocating waveform, obtain the negative half-wave, and count the number of negative half-waves within the sampling period.

[0020] The dual-scale window construction unit is used to set the time node scale and the load pressure scale, construct a rectangular window and map it to the negative half waveform, and determine the time and pressure value range of the load pressure value within the window.

[0021] The energy measurement unit is used to calculate the ratio of the overlapping area of ​​the rectangular window and the negative half-wave to the total area of ​​the window, so as to obtain the high-frequency energy measurement to characterize the energy distribution characteristics of the negative half-wave.

[0022] Furthermore, the frequency calculation module includes an observation frequency calculation unit and a load frequency calculation unit;

[0023] The observation frequency calculation unit is used to calculate the observation sampling frequency based on the sampling period duration and the number of negative half-waves.

[0024] The load frequency calculation unit calculates the load sampling frequency by weighting the time interval between adjacent valleys based on high-frequency energy.

[0025] Furthermore, the intelligent calibration control module includes a weight adaptive unit, a fusion calibration unit, and a feedback adjustment unit;

[0026] The weighted adaptive unit dynamically allocates the fusion weights of the observation sampling frequency and the load sampling frequency based on the real-time load pressure value and the benchmark load pressure value.

[0027] The fusion calibration unit is used to dynamically adjust the calibration sampling frequency through an intelligent locking protection model;

[0028] The feedback adjustment unit is used to feed back the calibration sampling frequency to the pressure monitoring sensor, driving the pressure monitoring sensor to collect the load pressure value according to the calibration sampling frequency.

[0029] A wave impact intelligent locking protection method based on artificial intelligence, the method includes the following steps:

[0030] S1: Collect wave oscillation waveform data and identify the time nodes in the oscillation waveform data that represent the periodic wave peak characteristics; divide the time period by adjacent wave peak time nodes, continuously monitor the reciprocating vibration state of the vibrating body caused by the wave action in each time period, and fit it into the reciprocating waveform of the vibrating body vibration.

[0031] S2: Based on the time nodes of adjacent wave peaks in the wave oscillation waveform data, the reciprocating waveform of the vibrating body is analyzed, the waveform characterizing the negative vibration characteristics of the vibrating body is extracted, and the observation sampling frequency of the pressure monitoring sensor within the period is quantified. The pressure monitoring sensor is used to collect the load pressure value generated when the vibrating body acts on the vibrating body base under the action of wave impact.

[0032] S3: Based on the waveform of negative vibration characteristics, capture the load pressure value collected by the pressure monitoring sensor at the trough of the negative half-wave and the time node triggered by the load pressure value, and construct a dual-scale wavelet window model to quantify the high-frequency energy of each negative half-wave.

[0033] S4: Based on high-frequency energy, the load sampling frequency of the pressure monitoring sensor within the cycle is quantified; an intelligent locking protection model is constructed, and the calibration sampling frequency is calculated and fed back to the pressure monitoring sensor based on the observed sampling frequency and the load sampling frequency.

[0034] Furthermore, the fitting method for the reciprocating waveform of the vibrating body is as follows:

[0035] Collect the oscillation waveform of the wave and mark the time point when each wave peak is generated in the oscillation waveform;

[0036] The reciprocating behavior of the vibrating body is recorded between two adjacent wave crest time points, and the reciprocating behavior of the vibrating body is characterized in a two-dimensional vibration coordinate system:

[0037] Using the plane at which the vibrating body is stationary without wave impact as the reference plane, the vibration amplitude of the vibrating body relative to the reference plane is recorded during the vibration process, and the vibration amplitude is fitted to a two-dimensional vibration coordinate system, wherein the horizontal axis of the two-dimensional vibration coordinate system corresponds to the time node, and the vertical axis of the two-dimensional vibration coordinate system corresponds to the vibration amplitude, thereby obtaining the reciprocating waveform of the vibrating body.

[0038] Furthermore, the quantization method for the observation sampling frequency of the pressure monitoring sensor within the period is as follows:

[0039] Let any two adjacent peak time points be denoted as... and Where i is the label of the time node, and the sampling period of the pressure monitoring sensor is formed between two adjacent peak time nodes, denoted as . ;

[0040] The negative envelope of the reciprocating waveform of the vibrating body is extracted to obtain the negative half-waveform. The number of samples of the negative half-waveform between two adjacent peak time nodes is counted and denoted as . ;

[0041] Based on the sampling period and the number of samples, the observation sampling frequency of the pressure monitoring sensor within the period is calculated. .

[0042] Furthermore, the quantization method for the high-frequency energy of the negative half-wave is as follows:

[0043] Capture during the sampling period The load pressure value collected by the pressure monitoring sensor at the trough of the xth negative half-wave and the time node triggered by the load pressure value are denoted as follows: and ,in, ;

[0044] A dual-scale wavelet window model is configured, wherein the dual scales include a horizontal scale and a vertical scale, and the horizontal scale corresponds to the time node scale. The longitudinal dimension corresponds to the load pressure dimension. A rectangular window is constructed using the dual-scale method, and this rectangular window is mapped onto the negative half-wave. The horizontal and vertical value ranges of the rectangular window mapped onto the x-th negative half-wave are then respectively... and This yields the rectangular window mapped in the x-th negative half-waveform. ;

[0045] Based on the dual-scale wavelet window model, the high-frequency energy of the x-th negative half-waveform is calculated. In the formula, Represents a rectangular window The area overlapping with the x-th negative half-waveform. Represents a rectangular window The area, and .

[0046] Furthermore, the quantization method for the calibration sampling frequency is as follows:

[0047] Calculate the sampling period based on high-frequency energy. Load sampling frequency of internal pressure monitoring sensor ;

[0048] Based on observation sampling frequency and load sampling frequency A smart locking protection model was constructed, the sampling frequency of the pressure monitoring sensor was calibrated, and the calibration sampling frequency was calculated. In the formula, The sensitivity parameter is the preset value. The load pressure value is collected in real time by the pressure monitoring sensor. The baseline load pressure value is the preset value; the calibration sampling frequency will be set. Feedback is sent to the pressure monitoring sensor.

[0049] In the above method, the observation sampling frequency can reflect the negative vibration frequency of the vibrating body within the sampling period and characterize the time domain characteristics of the vibration frequency. The load sampling frequency, by weighting the time interval of the load pressure concentration area with high-frequency energy, characterizes the energy domain characteristics of the vibration frequency. In this invention, the observation frequency provides the basic quantization of the vibration frequency, and the load frequency provides the frequency correction under high-energy impact. The combination of the two can simultaneously capture the vibration regularity and impact intensity.

[0050] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides an artificial intelligence-based intelligent locking protection method and system for wave impact, which collects wave oscillation waveform data, identifies wave peak time nodes and divides time periods, monitors the reciprocating vibration state of the vibrating body and fits the waveform; extracts negative vibration feature waveforms, quantifies the observation sampling frequency of the pressure monitoring sensor; captures the load pressure value and time node at the trough of the negative half-waveform, constructs a dual-scale wavelet window model to quantify high-frequency energy; calculates the load sampling frequency based on the high-frequency energy, calibrates the sampling frequency through the intelligent locking protection model, and feeds it back to the sensor. The system includes data acquisition, feature extraction, frequency calculation, and intelligent calibration control modules, realizing dynamic monitoring and sampling strategy optimization of the load pressure of the vibrating body under wave impact. This invention combines the time domain and energy domain characteristics of vibration frequency, improving the adaptability and efficiency of intelligent locking protection under wave impact environments. Attached Figure Description

[0051] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0052] Figure 1 This is a schematic diagram illustrating the steps of an artificial intelligence-based intelligent locking protection method for wave impacts according to the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In this first embodiment: an artificial intelligence-based intelligent wave impact locking protection system is provided, which includes: a data acquisition module, a feature extraction module, a frequency calculation module, and an intelligent calibration control module;

[0055] The data acquisition module is used to collect wave oscillation waveform data and the reciprocating vibration state of the vibrating body, generate the reciprocating waveform of the vibrating body, and collect the load pressure value of the vibrating body acting on the base through the pressure monitoring sensor.

[0056] The data acquisition module includes a wave monitoring unit, a vibration monitoring unit, and a pressure monitoring unit.

[0057] The wave monitoring unit is used to collect wave oscillation waveform data, identify periodic wave peak time nodes, and divide the sampling period;

[0058] The vibration monitoring unit is used to monitor the reciprocating vibration state of the vibrating body during the sampling period. It uses the non-impact static position as a reference plane and fits the vibration amplitude to form a reciprocating waveform.

[0059] The pressure monitoring unit is used to collect the load pressure value of the vibrating body acting on the base;

[0060] The feature extraction module, based on the negative vibration feature waveform, captures the load pressure value and time node at the trough of the negative half-waveform, constructs a dual-scale wavelet window model, and quantifies the high-frequency energy of each negative half-waveform.

[0061] The feature extraction module includes a negative half-wave processing unit, a dual-scale window construction unit, and an energy metric calculation unit.

[0062] The negative half-wave processing unit is used to extract the negative envelope of the reciprocating waveform, obtain the negative half-wave, and count the number of negative half-waves within the sampling period.

[0063] A dual-scale window construction unit is used to set the time node scale and the load pressure scale, construct a rectangular window and map it to the negative half waveform, and determine the time and pressure value range of the load pressure value within the window.

[0064] The energy measurement unit is used to calculate the ratio of the overlapping area of ​​the rectangular window and the negative half-wave to the total area of ​​the window, so as to obtain the high-frequency energy measurement to characterize the energy distribution characteristics of the negative half-wave.

[0065] The frequency calculation module calculates the observation sampling frequency based on the reciprocating waveform and the load sampling frequency based on the high-frequency energy level.

[0066] The frequency calculation module includes an observation frequency calculation unit and a load frequency calculation unit.

[0067] The observation frequency calculation unit is used to calculate the observation sampling frequency based on the sampling period duration and the number of negative half-waves.

[0068] The load frequency calculation unit calculates the load sampling frequency by weighting the time interval between adjacent troughs based on high-frequency energy.

[0069] The intelligent calibration control module is used to build an intelligent locking protection model to calculate the calibration sampling frequency and feed it back to the pressure monitoring sensor to adjust the sampling strategy.

[0070] The intelligent calibration control module includes a weight adaptive unit, a fusion calibration unit, and a feedback adjustment unit.

[0071] The weighted adaptive unit dynamically allocates the fusion weights of the observation sampling frequency and the load sampling frequency based on the real-time load pressure value and the baseline load pressure value.

[0072] A fusion calibration unit is used to dynamically adjust the calibration sampling frequency through an intelligent lockout protection model;

[0073] The feedback adjustment unit is used to feed back the calibration sampling frequency to the pressure monitoring sensor, driving the pressure monitoring sensor to collect the load pressure value according to the calibration sampling frequency.

[0074] Please see Figure 1 In this second embodiment: an artificial intelligence-based intelligent locking protection method for wave impact is provided, applicable to the first embodiment described above. The method includes the following steps:

[0075] S1: Collect wave oscillation waveform data and identify the time nodes in the oscillation waveform data that represent the periodic wave peak characteristics; divide the time period by adjacent wave peak time nodes, continuously monitor the reciprocating vibration state of the vibrating body caused by the wave action in each time period, and fit it into the reciprocating waveform of the vibrating body vibration.

[0076] For example, the fitting method for the reciprocating waveform of the vibrating body is as follows:

[0077] Collect the oscillation waveform of the wave and mark the time point when each wave peak is generated in the oscillation waveform;

[0078] The reciprocating behavior of the vibrating body is recorded between two adjacent wave crest time points, and the reciprocating behavior of the vibrating body is characterized in a two-dimensional vibration coordinate system:

[0079] Using the plane where the vibrating body is stationary without wave impact as the reference plane, the vibration amplitude of the vibrating body relative to the reference plane is recorded during the vibration process, and the vibration amplitude is fitted to the two-dimensional vibration coordinate system, where the horizontal axis of the two-dimensional vibration coordinate system corresponds to the time node, and the vertical axis of the two-dimensional vibration coordinate system corresponds to the vibration amplitude, thus obtaining the reciprocating waveform of the vibrating body.

[0080] S2: Based on the time nodes of adjacent wave peaks in the wave oscillation waveform data, the reciprocating waveform of the vibrating body is analyzed, the waveform that characterizes the negative vibration characteristics of the vibrating body is extracted, and the observation sampling frequency of the pressure monitoring sensor within the period is quantified. The pressure monitoring sensor is used to collect the load pressure value generated when the vibrating body acts on the vibrating body base under the action of wave impact.

[0081] For example, the quantization method for the observation sampling frequency of the pressure monitoring sensor within a period is as follows:

[0082] Let any two adjacent peak time points be denoted as... and Where i is the label of the time node, and the sampling period of the pressure monitoring sensor is formed between two adjacent peak time nodes, denoted as . ;

[0083] The negative envelope of the reciprocating waveform of the vibrating body is extracted to obtain the negative half-waveform. The number of samples of the negative half-waveform between two adjacent peak time nodes is counted and denoted as . ;

[0084] Based on the sampling period and the number of samples, the observation sampling frequency of the pressure monitoring sensor within the period is calculated. .

[0085] S3: Based on the waveform of negative vibration characteristics, capture the load pressure value collected by the pressure monitoring sensor at the trough of the negative half-wave and the time node triggered by the load pressure value, and construct a dual-scale wavelet window model to quantify the high-frequency energy of each negative half-wave.

[0086] For example, the quantization method for the high-frequency energy of the negative half-wave is as follows:

[0087] Capture during the sampling period The load pressure value collected by the pressure monitoring sensor at the trough of the xth negative half-wave and the time node triggered by the load pressure value are denoted as follows: and ,in, ;

[0088] A dual-scale wavelet window model is set up, with two scales: a horizontal scale and a vertical scale. The horizontal scale corresponds to the time node scale. The longitudinal dimension corresponds to the load pressure dimension. A rectangular window is constructed using a dual-scale method and mapped onto the negative half-wave. The horizontal and vertical ranges of the rectangular window mapped onto the x-th negative half-wave are then respectively... and This yields the rectangular window mapped in the x-th negative half-waveform. ;

[0089] Based on the dual-scale wavelet window model, the high-frequency energy of the x-th negative half-waveform is calculated. In the formula, Represents a rectangular window The area overlapping with the x-th negative half-waveform. Represents a rectangular window The area, and .

[0090] S4: Based on high-frequency energy, the load sampling frequency of the pressure monitoring sensor within the cycle is quantified; an intelligent locking protection model is constructed, and the calibration sampling frequency is calculated and fed back to the pressure monitoring sensor based on the observed sampling frequency and the load sampling frequency;

[0091] For example, the quantization method for calibrating the sampling frequency is as follows:

[0092] Calculate the sampling period based on high-frequency energy. Load sampling frequency of internal pressure monitoring sensor ;

[0093] Based on observation sampling frequency and load sampling frequency A smart locking protection model was constructed, the sampling frequency of the pressure monitoring sensor was calibrated, and the calibration sampling frequency was calculated. In the formula, The sensitivity parameter is the preset value. The load pressure value is collected in real time by the pressure monitoring sensor. The baseline load pressure value is the preset value; the calibration sampling frequency will be set. Feedback is sent to the pressure monitoring sensor;

[0094] For example, under high load impact, Much larger ,at this time It can approach 0, with the load sampling frequency dominating (focusing on high-frequency impact signals). While calibrating the load sampling frequency, it can also instruct the damping coefficient of the vibrating body base to increase, thereby achieving high-frequency sampling while resisting impact. For low-load impact, much smaller , It can approach 1, with the observation sampling frequency being dominant (optimizing sampling efficiency in low-frequency scenarios). While calibrating the observation sampling frequency, it can also instruct the damping coefficient of the vibrating body base to decrease.

[0095] 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.

[0096] 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 wave impact intelligent locking protection method based on artificial intelligence, characterized in that, The method includes the following steps: S1: Collect wave oscillation waveform data and identify the time nodes in the oscillation waveform data that represent the periodic wave peak characteristics; divide the time period by adjacent wave peak time nodes, continuously monitor the reciprocating vibration state of the vibrating body caused by the wave action in each time period, and fit it into the reciprocating waveform of the vibrating body vibration. S2: Based on the time nodes of adjacent wave peaks in the wave oscillation waveform data, the reciprocating waveform of the vibrating body is analyzed, the waveform characterizing the negative vibration characteristics of the vibrating body is extracted, and the observation sampling frequency of the pressure monitoring sensor within the period is quantified. The pressure monitoring sensor is used to collect the load pressure value generated when the vibrating body acts on the vibrating body base under the action of wave impact. S3: Based on the waveform of negative vibration characteristics, capture the load pressure value collected by the pressure monitoring sensor at the trough of the negative half-wave and the time node triggered by the load pressure value, and construct a dual-scale wavelet window model to quantify the high-frequency energy of each negative half-wave. S4: Based on high-frequency energy, the load sampling frequency of the pressure monitoring sensor within the cycle is quantified; an intelligent locking protection model is constructed, and the calibration sampling frequency is calculated and fed back to the pressure monitoring sensor based on the observed sampling frequency and the load sampling frequency; The quantization method for the high-frequency energy of the negative half-wave is as follows: Capture during the sampling period The load pressure value collected by the pressure monitoring sensor at the trough of the xth negative half-wave and the time node triggered by the load pressure value are denoted as follows: and ,in, ; A dual-scale wavelet window model is configured, wherein the dual scales include a horizontal scale and a vertical scale, and the horizontal scale corresponds to the time node scale. The longitudinal dimension corresponds to the load pressure dimension. A rectangular window is constructed using the dual-scale method, and this rectangular window is mapped onto the negative half-wave. The horizontal and vertical value ranges of the rectangular window mapped onto the x-th negative half-wave are then respectively... and This yields the rectangular window mapped in the x-th negative half-waveform. ; Based on the dual-scale wavelet window model, the high-frequency energy of the x-th negative half-waveform is calculated. In the formula, Represents a rectangular window The area overlapping with the x-th negative half-waveform. Represents a rectangular window The area, and ; The quantization method for the calibration sampling frequency is as follows: Calculate the sampling period based on high-frequency energy. Load sampling frequency of internal pressure monitoring sensor ; Based on observation sampling frequency and load sampling frequency A smart locking protection model was constructed, the sampling frequency of the pressure monitoring sensor was calibrated, and the calibration sampling frequency was calculated. In the formula, The sensitivity parameter is the preset value. The load pressure value is collected in real time by the pressure monitoring sensor. The baseline load pressure value is the preset value; the calibration sampling frequency will be set. Feedback is sent to the pressure monitoring sensor.

2. The wave impact intelligent locking protection method based on artificial intelligence according to claim 1, characterized in that, The fitting method for the reciprocating waveform of the vibrating body is as follows: Collect the oscillation waveform of the wave and mark the time point when each wave peak is generated in the oscillation waveform; The reciprocating behavior of the vibrating body is recorded between two adjacent wave crest time points, and the reciprocating behavior of the vibrating body is characterized in a two-dimensional vibration coordinate system: Using the plane at which the vibrating body is stationary without wave impact as the reference plane, the vibration amplitude of the vibrating body relative to the reference plane is recorded during the vibration process, and the vibration amplitude is fitted to a two-dimensional vibration coordinate system, wherein the horizontal axis of the two-dimensional vibration coordinate system corresponds to the time node, and the vertical axis of the two-dimensional vibration coordinate system corresponds to the vibration amplitude, thereby obtaining the reciprocating waveform of the vibrating body.

3. The wave impact intelligent locking protection method based on artificial intelligence according to claim 1, characterized in that, The quantization method for the observation sampling frequency of the pressure monitoring sensor within the cycle is as follows: Let any two adjacent peak time points be denoted as... and Where i is the time node number, and the sampling period of the pressure monitoring sensor is formed between two adjacent peak time nodes, denoted as . ; The negative envelope of the reciprocating waveform of the vibrating body is extracted to obtain the negative half-waveform. The number of samples of the negative half-waveform between two adjacent peak time nodes is counted and denoted as . ; Based on the sampling period and the number of samples, the observation sampling frequency of the pressure monitoring sensor within the period is calculated. .

4. An artificial intelligence-based intelligent wave impact protection system, executing the intelligent wave impact protection method as described in any one of claims 1-3, characterized in that, The system includes: a data acquisition module, a feature extraction module, a frequency calculation module, and an intelligent calibration control module; The data acquisition module is used to acquire wave oscillation waveform data and the reciprocating vibration state of the vibrating body, generate the reciprocating waveform of the vibrating body, and acquire the load pressure value of the vibrating body acting on the base through the pressure monitoring sensor. The feature extraction module, based on the negative vibration feature waveform, captures the load pressure value and time node at the trough of the negative half-waveform, constructs a dual-scale wavelet window model, and quantifies the high-frequency energy of each negative half-waveform. The frequency calculation module calculates the observation sampling frequency based on the reciprocating waveform and the load sampling frequency based on the high-frequency energy level. The intelligent calibration control module is used to construct an intelligent locking protection model to calculate the calibration sampling frequency and feed it back to the pressure monitoring sensor to adjust the sampling strategy.

5. The wave impact intelligent locking protection system based on artificial intelligence according to claim 4, characterized in that, The data acquisition module includes a wave monitoring unit, a vibration monitoring unit, and a pressure monitoring unit; The wave monitoring unit is used to collect wave oscillation waveform data, identify periodic wave peak time nodes, and divide the sampling period. The vibration monitoring unit is used to monitor the reciprocating vibration state of the vibrating body during the sampling period, and to fit the vibration amplitude to form a reciprocating waveform with the non-impact static position as the reference plane. The pressure monitoring unit is used to collect the load pressure value of the vibrating body acting on the base.

6. The wave impact intelligent locking protection system based on artificial intelligence according to claim 4, characterized in that, The feature extraction module includes a negative half-wave processing unit, a dual-scale window construction unit, and an energy calculation unit. The negative half-wave processing unit is used to extract the negative envelope of the reciprocating waveform, obtain the negative half-wave, and count the number of negative half-waves within the sampling period. The dual-scale window construction unit is used to set the time node scale and the load pressure scale, construct a rectangular window and map it to the negative half waveform, and determine the time and pressure value range of the load pressure value within the window. The energy measurement unit is used to calculate the ratio of the overlapping area of ​​the rectangular window and the negative half-wave to the total area of ​​the window, so as to obtain the high-frequency energy measurement to characterize the energy distribution characteristics of the negative half-wave.

7. The wave impact intelligent locking protection system based on artificial intelligence according to claim 4, characterized in that, The frequency calculation module includes an observation frequency calculation unit and a load frequency calculation unit; The observation frequency calculation unit is used to calculate the observation sampling frequency based on the sampling period duration and the number of negative half-waves. The load frequency calculation unit calculates the load sampling frequency by weighting the time interval between adjacent valleys based on high-frequency energy.

8. The wave impact intelligent locking protection system based on artificial intelligence according to claim 4, characterized in that, The intelligent calibration control module includes a weight adaptive unit, a fusion calibration unit, and a feedback adjustment unit; The weighted adaptive unit dynamically allocates the fusion weights of the observation sampling frequency and the load sampling frequency based on the real-time load pressure value and the benchmark load pressure value. The fusion calibration unit is used to dynamically adjust the calibration sampling frequency through an intelligent locking protection model; The feedback adjustment unit is used to feed back the calibration sampling frequency to the pressure monitoring sensor, driving the pressure monitoring sensor to collect the load pressure value according to the calibration sampling frequency.

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