A distributed acoustic wave sensing monitoring device and method for ocean wave impact force

Abstract

The present invention discloses a distributed acoustic wave sensing device and method for monitoring wave impact force. The device includes a vibration sensing optical fiber, an anchoring unit, a distributed optical fiber acoustic wave sensing demodulator, a vibration signal processing unit, and an ocean wave impact force inversion unit. The method comprises: fixing the vibration sensing optical fiber to the coast impacted by waves via the anchoring unit; the distributed optical fiber acoustic wave sensing demodulator transmits the monitored ocean wave vibration signal to the vibration signal processing unit for preprocessing; the vibration signal processing unit transmits the processed optical fiber vibration amplitude signal to the ocean wave impact force inversion unit; a calibration experiment obtains an optical fiber vibration amplitude signal and an ocean wave impact force training dataset, constructs an attention convolutional neural network model for training; and the ocean wave impact force inversion unit uses the attention convolutional neural network model to establish a correspondence between the optical fiber vibration amplitude signal and the ocean wave impact force, thereby obtaining the actual ocean wave impact force on the coast. The present invention achieves accurate monitoring of ocean wave impact force on the coast.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean wave monitoring, and in particular to a distributed acoustic wave sensing monitoring device and method for ocean wave impact force. Background Art

[0002] Ocean waves are the propagation of undulating patterns on the sea surface. They are formed by water particles moving from their equilibrium positions, vibrating periodically, and propagating in a specific direction. The vibrations of water particles generate kinetic energy, while the undulations of ocean waves generate potential energy, embodying enormous amounts of energy. When ocean waves reach offshore coasts, they form shorebreaks. These shorebreaks can cause coastal erosion and even collapse, leading to coastline retreat. Coastal collapse and retreat seriously impact the safety of life and property of coastal residents and navigation. The impact of ocean waves on the coast is closely related to the impact force of the waves. Therefore, monitoring the impact force of ocean waves is of great significance for protecting coastal and marine ecosystems.

[0003] Traditional wave monitoring devices include wave buoys, water pressure wave meters and remote sensing wave meters.

[0004] Wave buoys are instruments that drift with the ocean current or observe sea surface amplitude, wave period, wave direction, and wave spectrum at a fixed point. These data, combined with wave theory, can indirectly calculate wave energy. However, this method only measures wave energy, not the impact of waves on the coast.

[0005] A hydrostatic wave meter uses linear wave theory to analyze ocean waves and measure wave parameters. This device accurately monitors long-period waves and is primarily used for shallow-water wave monitoring. Like wave buoys, this wave measurement method cannot measure wave impact forces.

[0006] Remote sensing wavemeters include laser and radar wavemeters. Laser wavemeters offer high ranging accuracy, can continuously record wave fluctuations, and enable all-weather monitoring, but are significantly affected by high temperatures, high salinity, high humidity, and typhoon-induced weather events. Radar wavemeters can measure a wide range of ocean elements, offer high accuracy, and offer high spatial resolution. However, they occupy a large area, are difficult to move, are expensive, and are easily damaged by typhoons and other catastrophic weather events. While these two methods can obtain a variety of wave information and, through certain calculations, can also determine wave energy distribution, neither method can monitor wave impact force.

[0007] How to conduct distributed real-time monitoring of wave impact force is a technical problem that needs to be solved urgently. Summary of the Invention

[0008] Purpose of the Invention: The purpose of the present invention is to provide a distributed acoustic wave sensing device and method for monitoring wave impact force. The device obtains neural network model training data through calibration tests conducted on a simulated indoor coastal model of waves impacting the sea, and designs and trains a designed attention convolutional neural network model, i.e., a wave impact force judgment model. Vibration sensing optical fibers are laid on coastal rock walls, and distributed optical fiber acoustic wave sensing methods are used to collect optical fiber vibration amplitude signals from waves impacting the seashore in real time. After preprocessing the optical fiber vibration amplitude signals, the optical fiber vibration amplitude signals are transmitted to a wave impact force inversion unit, and the trained wave impact force judgment model is used to judge the impact force caused by the waves.

[0009] Technical solution: To achieve the above-mentioned purpose, the distributed acoustic wave sensing monitoring device for wave impact force of the present invention includes a vibration sensing optical fiber, an anchoring unit, a distributed optical fiber acoustic wave sensing demodulator, a vibration signal processing unit and an ocean wave impact force inversion unit; the vibration sensing optical fiber is fixed to the coast surface through the anchoring unit; at the same time, the vibration sensing optical fiber is connected to the distributed optical fiber acoustic wave sensing demodulator through an optical fiber lead; the distributed optical fiber acoustic wave sensing demodulator collects the optical fiber vibration amplitude signal and transmits it to the vibration signal processing unit; the vibration signal processing unit pre-processes the optical fiber vibration amplitude signal to improve the signal-to-noise ratio, and transmits the collected vibration amplitude signal to the ocean wave impact force inversion unit; the ocean wave impact force inversion unit obtains real-time monitoring results of the ocean wave impact force through an ocean wave impact force judgment model.

[0010] The distributed acoustic wave sensing monitoring method for ocean wave impact force of the present invention comprises the following steps:

[0011] Step (1) is to conduct a calibration test on an indoor coastal model: a vibration sensing optical fiber is attached to a wave impact force determination model, a force sensor is attached to the vibration sensing optical fiber, and a wave maker is used to generate waves with different impact forces. The vibration amplitude data on the vibration sensing optical fiber is collected by a distributed optical fiber acoustic wave sensor demodulator, and the impact force monitored by the force sensor is simultaneously recorded; the vibration amplitude data of the vibration sensing optical fiber at the force sensor during each wave impact is divided into a training set, a validation set, and a test set;

[0012] Step (2) is to design and train an attention convolutional neural network model, i.e., a wave impact force judgment model, based on the data collected from the calibration test and the training set, validation set, and test set of step (1), thereby obtaining the functional relationship between the optical fiber vibration amplitude signal and the wave impact force and judging the magnitude of the wave impact force. The process is as follows:

[0013] In step (2.1), since the optical fiber vibration amplitude signal has time dependence, the wave impact force judgment model uses a one-dimensional convolution layer to extract the time characteristics in the signal; that is, the optical fiber vibration amplitude signal is processed by a one-dimensional convolution layer to capture the time dependence characteristics of the signal.

[0014] The specific process is as follows: the one-dimensional convolution layer slides the convolution kernel along the length of the optical fiber vibration amplitude signal, calculates the weighted sum within each window, and extracts the time series characteristics of the optical fiber vibration amplitude signal, thereby improving the accuracy of the wave impact force judgment model.

[0015] In step (2.2), after the one-dimensional convolution layer, the wave impact force judgment model adds an attention mechanism module, which determines the influence weight of the amplitude data in the fiber amplitude signal on the impact force through pooling convolution.

[0016] In step (2.3), after the attention mechanism module and the one-dimensional convolutional layer extract the amplitude time series features, the fully connected layer uses the output of the previous layer as input, uses weights and biases to transform it, and outputs one or more predicted values ​​to judge the final size of the wave impact force.

[0017] In step (2.4), during the training of the wave impact force judgment model, the mean square error (MSE) is used as the loss function to measure the deviation between the predicted value and the true value. By optimizing the loss function, the prediction accuracy of the model is improved. Due to the impact of waves, the optical fiber laid on the limestone coast model undergoes slight deformation, which will change the phase information of the internal optical signal transmission, thereby changing the optical fiber vibration amplitude signal. After model training, the functional relationship between the optical fiber vibration amplitude signal and the wave impact force is as follows:

[0018]

[0019] Where: F is the impact force; A is the optical fiber vibration amplitude signal; λ is the wavelength of the laser pulse; the phase change Δφ; n is the refractive index of the optical fiber; ξ is the elastic-optical coefficient; L g is the gauge length; the pulse interval is ΔT; and f is the model function relationship for predicting the magnitude of the wave impact force.

[0020] Step (3) is to fix the vibration sensing optical fiber to the rocky coast impacted by the waves through an anchoring unit, and check the integrity of the optical fiber line.

[0021] Step (4) connects the vibration sensing optical fiber to the distributed optical fiber acoustic wave sensor demodulator through an optical fiber lead, sets the same sampling parameters as the calibration test, and collects the optical fiber vibration amplitude signal generated by the wave impact vibration sensing optical fiber.

[0022] Step (5) transmits the optical fiber vibration amplitude data collected by the distributed optical fiber acoustic wave sensor demodulator to the vibration signal processing unit, pre-processes the optical fiber vibration amplitude data, and improves the data signal-to-noise ratio.

[0023] In step (6), the vibration signal processing unit transmits the pre-processed optical fiber vibration amplitude data to the wave impact force inversion unit. In the wave impact force inversion unit, the pre-processed optical fiber vibration amplitude data is first accurately divided into multiple time window data of fixed length. Then, each time window data is separately sent to the wave impact force judgment model. The one-dimensional convolution layer of the wave impact force judgment model first extracts the data features of each time window and automatically captures the key time features through multiple filters; then, the attention mechanism module adjusts the weights of the extracted data features and prioritizes the amplitude time series features that are key to predicting the wave impact force. Finally, the fully connected layer combines the attention-weighted features to form a comprehensive feature vector to predict the impact force of the waves in the time window data; the fully connected layer outputs a quantized impact force value, which reflects the actual force of the waves on the optical fiber sensor in the specific time window data.

[0024] In step (1), waves with different impact forces are generated indoors using a wave maker, and the coastal model equipped with vibration sensing optical fibers and force sensors is impacted to obtain optical fiber vibration amplitude data and force sensor data. The impact force monitored by the force sensor is used as a label, and the optical fiber vibration amplitude data at the position of the force sensor during the wave impact period is used as a data set. The data set is divided into a training set, a validation set, and a test set for the neural network model.

[0025] In step (2.4), the wave impact force judgment model uses the mean square error as the loss function to measure the deviation between the predicted value and the true value, and optimizes the loss function; at the same time, the wave impact force judgment model uses the Adam optimizer to update parameters. This optimizer combines momentum and adaptive learning rate adjustment to increase the training speed; it effectively accelerates the training process and helps avoid falling into local minima, thereby ensuring faster and more stable convergence performance.

[0026] In step (3), after the vibration sensing optical fiber is laid, the integrity of the optical fiber line is ensured, and the optical fiber is fixed with an anchoring unit to ensure the stability of the optical fiber position. This ensures that the optical fiber is stably laid on the coast and has the ability to resist seawater corrosion.

[0027] In step (4), ensure that the vibration sensing optical fiber, optical fiber lead and distributed optical fiber acoustic wave sensor demodulator are connected, and the sampling parameters related to the track spacing and sampling frequency of the distributed optical fiber acoustic wave sensor demodulator are designed according to the parameters of the distributed optical fiber acoustic wave sensor demodulator used in the indoor calibration test.

[0028] In step (5), the vibration signal processing unit performs preprocessing operations such as pinching out, de-linear trending, and filtering on the collected optical fiber vibration amplitude signal to improve the signal-to-noise ratio of the collected signal.

[0029] In step (6), the fully connected layer forms a comprehensive feature vector using the attention-weighted features to predict the impact force of the waves in the time window data, and outputs a quantized impact force value through a set threshold or calculation model.

[0030] In step (6), when the wave impact force inversion unit processes the optical fiber vibration amplitude signal, the time length of the time window data is set to half of the typical wave cycle, and each time window data overlaps with the next time window data by half of the time length, so that each wave event appears completely in at least one time window data. Specifically, the time length of the time window data is set to half of the typical wave cycle to ensure that each wave event is fully displayed in at least one window, even if the starting or ending part of the event is located at the boundary of two time window data. In addition, each time window data overlaps with the subsequent time window data by half of the time, reducing the data loss and boundary effect caused by time window switching, which not only improves the continuity and integrity of the data, but also enhances the ability of the wave impact force judgment model to capture the characteristics of each wave event.

[0031] In step (6), the vibration amplitude data contained in each window is first standardized before being input into the wave impact force judgment model to eliminate possible deviations caused by different weather conditions, thereby making a more accurate judgment on the model.

[0032] In step (6), during the wave impact force judgment process, the wave impact force inversion unit first inputs the standardized time window data into the wave impact force judgment model for judgment. The model processes the preprocessed data window through a multi-layer attention convolution layer, learns the key amplitude time series features in the vibration signal, and focuses on the signal part that has the greatest influence on the impact force judgment. The fully connected layer forms a comprehensive feature vector with the attention-weighted features, and uses linear and nonlinear transformations to map it into a judgment value, that is, the judgment value is the impact force of the wave.

[0033] Working principle: The present invention utilizes the characteristics of distributed fiber optic acoustic wave sensing method to achieve long-distance and large-scale monitoring, and can monitor the impact force of waves in coastal areas over long distances and over a large area. Distributed fiber optic acoustic wave sensing technology has a high spatial sampling rate and a wide frequency response range, so it is easy to achieve spatially dense sampling; at the same time, it ensures sufficient data volume, which is convenient for analyzing the distribution of wave impact force in each time period. Distributed fiber optic acoustic wave sensing technology also has the advantages of anti-electromagnetic interference, high durability, and real-time response, which has great advantages for monitoring the impact force of waves along the coast.

[0034] The distributed fiber optic acoustic wave sensing technology used in this invention has a test frequency of 0.1Hz to 40kHz. By capturing the vibration signal of the entire process of wave impact and exploring the relationship between the distributed fiber optic acoustic wave sensing vibration signal and the impact force, combined with a convolutional neural network, distributed real-time monitoring of the wave impact force is achieved.

[0035] The distributed acoustic wave sensing monitoring device and method for the impact force of ocean waves of the present invention utilize an anchoring unit to fix a vibration sensing optical fiber to the surface of the coast impacted by ocean waves; the vibration sensing optical fiber is connected to a distributed optical fiber acoustic wave sensing demodulator via an optical fiber lead; the other end of the distributed optical fiber acoustic wave sensing demodulator is connected to a vibration signal processing unit; the distributed optical fiber acoustic wave sensing demodulator transmits the monitored ocean wave vibration signal to the vibration signal processing unit for preprocessing to improve the signal-to-noise ratio; the vibration signal processing unit transmits the optical fiber vibration amplitude signal obtained after processing to an ocean wave impact force inversion unit; an indoor calibration experiment is performed using a wave maker to obtain the optical fiber vibration amplitude signal and the ocean wave impact The wave impact force inversion unit uses the trained attention convolutional neural network model to establish the corresponding relationship between the optical fiber vibration amplitude signal and the wave impact force. The vibration signal processing unit transmits the pre-processed optical fiber vibration amplitude signal to the wave impact force inversion unit. The wave impact force inversion unit first segments the optical fiber vibration amplitude data into a time series, and then imports the segmented optical fiber vibration amplitude signal into the trained wave impact force judgment model to obtain the corresponding wave impact force monitoring result, thereby obtaining the real impact force of coastal waves in real time and realizing distributed real-time monitoring of coastal wave impact force.

[0036] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0037] (1) The present invention utilizes a convolutional neural network combined with an attention mechanism to learn key amplitude time series features through a wave impact force judgment model, and accurately associates the relationship between the signal features in each time window data and the actual wave impact force. This not only improves the accuracy of the wave impact force judgment model in processing dynamic and complex wave data, but also enhances the accuracy of the prediction, thereby achieving accurate monitoring of wave impact force along the coast.

[0038] (2) The present invention adopts the attention mechanism to prioritize the amplitude timing information that is most critical for judgment, thereby enhancing the nonlinear processing capability of the wave impact force judgment model and improving the accuracy and efficiency of the judgment of the wave impact force.

[0039] (3) The device and method of the present invention are used to lay optical fibers on the shore surface using anchoring units, which reduces costs and fixes the optical fibers. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the composition of the distributed acoustic wave sensing monitoring device for the impact force of ocean waves according to the present invention;

[0041] Figure 2 Schematic diagram of the indoor calibration test of the present invention;

[0042] Figure 3 Schematic diagram of the wave impact force judgment model of the present invention;

[0043] Figure 4 Schematic diagram of the attention mechanism module of the present invention;

[0044] Figure 5 Schematic diagram of the original vibration signal obtained when the ocean wave impacts the optical fiber according to an embodiment of the present invention;

[0045] Figure 6 Schematic diagram of the results of the vibration signal processing unit in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The distributed acoustic wave sensing and monitoring device for wave impact force of the present invention includes a vibration sensing optical fiber 1, an anchoring unit 2, a distributed optical fiber acoustic wave sensing demodulator 3, a vibration signal processing unit 4, and an ocean wave impact force inversion unit 5. The vibration sensing optical fiber 1 is fixed to the surface of a rocky coastline via the anchoring unit 2. The distributed optical fiber acoustic wave sensing demodulator 3 is connected to the vibration sensing optical fiber 1 at one end via an optical fiber lead and to the vibration signal processing unit 4 at the other end. The vibration signal processing unit 4 is connected to the ocean wave impact force inversion unit 5.

[0047] The distributed acoustic wave sensing monitoring method for ocean wave impact force of the present invention comprises the following steps:

[0048] Step (1) is to conduct a calibration test on an indoor coastal model: a vibration sensing optical fiber 1 is attached to the coastal model, a force sensor 7 is attached to the vibration sensing optical fiber 1, and a wave maker 6 is used to generate waves with different impact forces. The vibration amplitude signal on the vibration sensing optical fiber is collected by a distributed optical fiber acoustic wave sensor demodulator 3, and the impact force monitored by the force sensor 7 is simultaneously recorded. The impact force monitored by the force sensor 7 is used as a label, and the vibration amplitude data of the vibration sensing optical fiber at the position of the force sensor during each wave impact is divided into a training set, a validation set, and a test set for the neural network model.

[0049] In step (2), based on the data collected from the calibration test and the training set, validation set, and test set, an attention convolutional neural network model, namely, a wave impact force judgment model, is designed and trained. The wave impact force judgment model uses a convolutional neural network combined with an attention mechanism to judge the magnitude of the wave impact force through the functional relationship between the vibration amplitude signal and the impact force.

[0050] In step (2.1), since the optical fiber vibration amplitude signal is time-dependent, the wave impact force judgment model contains a one-dimensional convolution layer, which extracts the time series characteristics of the signal through the convolution kernel. Unlike conventional convolutional networks, the size and step size of each convolution kernel of the present invention are optimized to match the typical wave cycle, thereby more accurately capturing the time-dependent characteristics related to the wave impact force. This design not only enhances the functional relationship between the optical fiber vibration amplitude signal and the wave impact force, but also improves the response speed and processing efficiency of the wave impact force judgment model.

[0051] In this step (2.1), the wave impact force judgment model includes five sequentially arranged one-dimensional convolution layers, each of which is followed by batch normalization and ReLU activation function. This hierarchical structure helps to extract complex features in the optical fiber vibration amplitude signal while maintaining computational efficiency. In the specific implementation process, each convolution layer uses a set of specific convolution kernels to extract features from the input vibration amplitude signal. These convolution kernels slide on the signal data and calculate the dot product within the coverage area to extract local features. Batch normalization is performed immediately after each convolution layer. Its main function is to normalize the feature map output by the convolution layer and standardize it to a distribution with a mean close to 0 and a variance close to 1. This step improves the stability of the wave impact force judgment model training and accelerates the convergence process. Next, the ReLU activation function is applied to the output of batch normalization, introducing nonlinear processing to enhance the ability of the wave impact force judgment model to process complex signal features. Through the above hierarchical structure design, the wave impact force judgment model deeply explores and extracts complex features in the signal layer by layer. The feature extraction of each convolution layer and the subsequent normalization processing, as well as the nonlinear activation, work together to improve the accuracy of the wave impact force judgment model when processing vibration signals.

[0052] In step (2.2), an attention mechanism module is added to the wave impact force judgment model. This attention mechanism module includes a channel attention mechanism and a spatial attention mechanism. This attention mechanism module not only analyzes the importance of each channel in the fiber vibration amplitude signal, but also determines the spatial contribution of each fiber vibration amplitude signal. This allows the wave impact force judgment model to focus on the amplitude time series signal that is most critical for wave impact force judgment, thereby improving the accuracy of the judgment.

[0053] In this step (2.2), the wave impact force model adds an attention mechanism module after the third and fifth convolutional layers. This attention mechanism module includes a channel attention mechanism module and a spatial attention mechanism module. These two attention mechanisms work together to optimize the wave impact force judgment model's processing of each data point in the fiber vibration amplitude signal. The channel attention mechanism module first uses global average pooling and maximum pooling operations to extract statistical information from each channel. Then, through a fully connected layer, it learns the weight coefficient for each channel, thereby strengthening the response to important feature channels. The spatial attention mechanism module focuses on the spatial distribution of the signal, identifying spatially important feature regions by performing pooling and convolution operations on each feature map. By focusing on spatial regions that contribute significantly to the prediction results, the attention mechanism module further enhances the wave impact force judgment model's ability to distinguish complex wave impact forces. This in turn enables the attention mechanism module to learn and emphasize the most critical influence weights in the fiber amplitude signal on impact force judgment. This enables the wave impact force judgment model to more accurately predict different impact forces when processing wave signals with complex dynamic characteristics, improving prediction accuracy and the practicality of the wave impact force judgment model.

[0054] In step (2.3), after the attention mechanism module and one-dimensional convolutional layer extract amplitude time series features, the wave impact force judgment model uses a fully connected layer to take the output of the previous layer as input, transform it using weights and biases, and output one or more predicted values ​​to judge the final wave impact force. The fully connected layer not only includes linear nodes but also introduces nonlinear processing units such as ReLU or Leaky ReLU to enhance the nonlinear expression capability of the wave impact force judgment model. In addition, the number of nodes and hierarchical structure in the fully connected layer are optimized to ensure the integration and mapping of amplitude time series features to the final impact force judgment value.

[0055] In step (2.4), during the training of the wave impact force judgment model, the mean squared error (MSE) was selected as the loss function because it intuitively quantifies the error between the impact force judgment value and the actual value, and because a regularization term was added during the optimization of the wave impact force judgment model to prevent overfitting. Simultaneously, the Adam optimizer was used for adjustment, and the momentum and adaptive learning rate parameters were set based on the characteristics of the wave data to accelerate convergence and improve the stability and prediction accuracy of the wave impact force judgment model under different sea conditions.

[0056] Step (3), laying the vibration sensing optical fiber 1 through the anchoring unit 2 on the coast impacted by the waves, and checking the integrity of the optical fiber line;

[0057] Step (4) connects the vibration sensing optical fiber 1 to the distributed optical fiber acoustic wave sensor demodulator 3 through an optical fiber lead, checks the smoothness of optical fiber signal transmission, sets the same sampling parameters as the calibration test, and collects the optical fiber vibration amplitude signal generated by the wave impact vibration sensing optical fiber;

[0058] Step (5) transmits the optical fiber vibration amplitude signal data collected by the distributed optical fiber acoustic wave sensor demodulator 3 to the vibration signal processing unit 4, and pre-processes the optical fiber vibration amplitude data to improve the data signal-to-noise ratio.

[0059] In step (6), the vibration signal processing unit 4 transmits the pre-processed optical fiber vibration amplitude data to the wave impact force inversion unit 5. The wave impact force inversion unit 5 divides the pre-processed continuous optical fiber vibration amplitude data into multiple fixed-length time window data, so that the wave impact force judgment model can capture the time-varying characteristics caused by the wave impact, thereby improving the timeliness and accuracy of monitoring. Each time window data is then input into the trained wave impact force judgment model. The wave impact force judgment model uses the internal one-dimensional convolution layer, attention mechanism module and fully connected layer to comprehensively analyze the vibration characteristics of each time window data, thereby judging the magnitude of the wave impact force at the corresponding moment.

[0060] In step 1, the vibration sensing optical fiber 1 is horizontally arranged on the coastal model, and the force sensor 7 is evenly pasted along the vibration sensing optical fiber. A wave maker is used to create waves with different impact forces, and the optical fiber vibration amplitude data and force sensor data obtained by impacting the coastal model are obtained. The impact force monitored by the force sensor is used as the label, and the optical fiber vibration amplitude data at the position of the force sensor during the wave impact time period is used as the data set. The data set is divided into a training set, a validation set, and a test set of the neural network model in a ratio of 8:1:1.

[0061] In step 2, the optical fiber installed on the limestone coast model undergoes slight deformation due to the impact of waves, which changes the phase information of the internal optical signal transmission, thereby changing the optical fiber vibration amplitude signal. After training the wave impact force judgment model, the functional relationship between the optical fiber vibration amplitude signal and the wave impact force is obtained:

[0062]

[0063] Where: F is the impact force; A is the optical fiber vibration amplitude signal; λ is the wavelength of the laser pulse; the phase change Δφ; n is the refractive index of the optical fiber; ξ is the elastic-optical coefficient; L g is the gauge length; the pulse interval is ΔT; and f is the model function relationship.

[0064] In step 2.1, the wave impact force estimation model consists of five one-dimensional convolutional layers, each followed by batch normalization and a Reluctant Unit (ReLU) activation function. This hierarchical structure facilitates the gradual extraction of complex signal features while maintaining computational efficiency. Convolutional layers are not directly connected, but rather transitioned through batch normalization and activation layers, which enhances stability and nonlinearity during the wave impact force model learning process.

[0065] In step 2.2, the wave impact force judgment model adds an attention mechanism module after the third and fifth convolutional layers, respectively. The attention mechanism module includes a channel attention mechanism module and a spatial attention mechanism module. The attention mechanism module determines the influence weight of each amplitude data in the fiber amplitude signal on the impact force judgment through pooling convolution operations, which is crucial for distinguishing complex waves with different impact forces.

[0066] In step 3, after the vibration sensing optical fiber is laid, the integrity of the optical fiber line must be ensured, and the optical fiber must be fixed with an anchor unit to ensure its stable position. Furthermore, the optical fiber must be stably laid on the coast and resistant to seawater corrosion.

[0067] In step 4, ensure that the optical fiber is tightly connected to the distributed fiber optic acoustic wave sensor demodulator, and determine the track spacing and sampling frequency-related sampling parameters of the distributed fiber optic acoustic wave sensor demodulator based on the distributed fiber optic acoustic wave sensor demodulator parameters used in the indoor calibration test.

[0068] In step 5, the vibration signal processing unit performs pinch-out, de-linear trend, and filtering preprocessing operations on the collected optical fiber vibration amplitude signal to improve the signal-to-noise ratio of the collected signal.

[0069] In step 6, the length of the time window data is set to half the typical wave period. Each window overlaps the next window by half its length, ensuring that each wave event appears completely in at least one window, even if its start or end falls at the intersection of two windows. This ensures data continuity and integrity and avoids information loss caused by data segmentation at window boundaries.

[0070] In step 6, the data contained in each time window is first standardized before being input into the wave impact force judgment model to eliminate possible deviations caused by different weather conditions.

[0071] In step 6, the standardized time window data is input into the wave impact force judgment model for judgment. The wave impact force judgment model processes the preprocessed data window through multiple layers of attention convolutional layers, learning the key amplitude time series features in the vibration signal. These amplitude time series features, through the attention mechanism, ensure that the wave impact force judgment model focuses on the signal components that are most influential in judging the impact force magnitude. Next, a fully connected layer integrates these features and uses linear and nonlinear transformations to map them to a specific judgment value, namely the wave impact force magnitude. This process allows the wave impact force judgment model to accurately associate and judge the wave impact force within each time window data, improving the accuracy and reliability of the model's judgment.

[0072] Example

[0073] like Figure 1 As shown, the distributed acoustic wave sensing and monitoring device for wave impact force of the present invention includes a vibration sensing optical fiber 1, an anchoring unit 2, a distributed optical fiber acoustic wave sensing demodulator 3, a vibration signal processing unit 4, and an ocean wave impact force inversion unit 5. The vibration sensing optical fiber 1 is fixed to the coastline via the anchoring unit 2; the distributed optical fiber acoustic wave sensing demodulator 3 is connected to the vibration sensing optical fiber 1 at one end and to the vibration signal processing unit 4 at the other end. The vibration signal processing unit 4 is connected to the ocean wave impact force inversion unit 5. The wave generator 6 is a push-plate wave generator with a servo motor driving a ball screw. It can generate waves with a maximum amplitude of 20 cm and can produce various waveforms.

[0074] The distributed acoustic wave sensing monitoring method and system of the present invention were used to monitor the impact of waves on a certain coast in real time. The selected coast was a limestone coast, 2 km long and 50 m high, with the sea level 30 m from the coastline. Based on the characteristics of the limestone, a cross anchor was selected as the anchoring unit.

[0075] The distributed acoustic wave sensing monitoring method for ocean wave impact force of the present invention comprises the following steps:

[0076] Step (1), use Figure 2 The indoor calibration test apparatus shown was used for calibration tests. A limestone coast model was constructed. A vibration sensing optical fiber was horizontally laid across the coast model. Four force sensors 7 were evenly attached along the vibration sensing optical fiber. The sampling frequency of the distributed fiber demodulator was set to 100 Hz. Waves of varying impact force were generated by a wave generator 6 to impact the limestone coast model, to which the vibration sensing optical fiber was attached. Because the impact of waves lasts from a few seconds to thirty seconds, the time the wave generator emits the wave was used as the starting point for each segment of the effective optical fiber vibration amplitude signal. The optical fiber vibration amplitude signal monitored during the calibration test was sliced ​​into 30-second segments. Each segment of data contained 3000 characteristic optical fiber vibration amplitude data points, resulting in a total of 5000 segments of calibration test data collected.

[0077] At the same time, based on the correspondence between the optical fiber vibration amplitude data and the impact force monitored by the force sensor, each segment of optical fiber vibration amplitude data is labeled with the impact force monitored by the force sensor, and the corresponding feature optical fiber data is divided into training set, validation set, and test set. The data volume ratio is 8:1:1, that is, 4000 segments of training set data, 500 segments of validation set, and 500 segments of test set respectively.

[0078] In step (2), the impact causes the optical fiber installed on the limestone coast model to undergo a slight deformation, which in turn changes the phase information of the internal optical signal transmission, thereby changing the optical fiber vibration amplitude signal. After model training, the functional relationship between the optical fiber vibration amplitude signal and the wave impact force is obtained:

[0079]

[0080] Where: F is the impact force; A is the fiber amplitude; λ is the wavelength of the laser pulse; the phase change Δφ; n is the refractive index of the fiber; ξ is the elastic-optical coefficient; L g is the gauge length; the pulse interval is ΔT; and f is the model function relationship.

[0081] From the above, it can be concluded that based on the data collected from the calibration test, an attention convolutional neural network model, namely the wave impact force judgment model, is designed and trained to obtain the functional relationship between the optical fiber vibration amplitude signal and the wave impact force.

[0082] In step (2.1), because the fiber vibration amplitude signal is time-dependent, the wave impact force estimation model is designed using five one-dimensional convolutional layers to extract the temporal features of the signal. Each layer is followed by batch normalization and a Reluctant Unit (ReLU) activation function. This hierarchical structure facilitates the extraction of complex signal features while maintaining computational efficiency. Convolutional layers are not directly connected, but rather transitioned through batch normalization and activation function layers to maintain stability and nonlinear enhancement during the learning process of the wave impact force estimation model.

[0083] In step (2.2), the wave impact force judgment model adds attention mechanism modules after the third and fifth convolutional layers. These attention mechanism modules include channel attention and spatial attention. The design uses these attention mechanism modules to enhance the network's ability to perceive important features in the input signal. This allows the wave impact force judgment model to focus on the most informative parts of the fiber vibration amplitude data, improving the model's accuracy in judging wave impact force.

[0084] In step (2.3), after the attention mechanism module and the one-dimensional convolutional layer extract the amplitude time series features, the wave impact force judgment model further integrates these features using a fully connected layer. The fully connected layer integrates the local features derived by the wave impact force judgment model to make the final impact force judgment.

[0085] In step (2.4), the wave impact force judgment model training process uses the mean squared error (MSE) as the loss function. This loss function intuitively quantifies the error between the impact force judgment value and the actual value and is suitable for continuous value judgment. The Adam optimizer is also used, which combines the advantages of momentum and adaptive learning rate to improve the training speed of the wave impact force judgment model.

[0086] In step (3), the vibration sensing optical fiber 1 is fixedly laid at a position 0.5 m above the sea level using an anchoring unit 2 to ensure that the waves impact the optical fiber and generate vibration amplitude data. The total length of the optical fiber is 1.5 km. After the laying is completed, the integrity of the optical fiber line must be ensured.

[0087] In step (4), after the vibration sensing optical fiber 1 is connected to the distributed optical fiber acoustic wave sensor demodulator 3 through the optical fiber lead, the smoothness of the optical fiber channel is checked, the sampling frequency of the distributed optical fiber acoustic wave sensor demodulator 3 is set to 100 Hz, the channel spacing is 5 m, and the number of channels is 600, and the optical fiber vibration amplitude data caused by the slight deformation of the optical fiber caused by the waves hitting the coast is collected in real time.

[0088] Step (5), the distributed optical fiber acoustic wave sensor demodulator 3 transmits the optical fiber vibration amplitude signal to the vibration signal processing unit 4, and the extracted raw data is as follows: Figure 5 In the vibration signal processing unit 4, the optical fiber vibration amplitude signal is extinguished to remove the linear trend and improve the signal-to-noise ratio. Since the calibration test found that the wave signal in the 1-50 Hz frequency band is more significant, the wave signal is subjected to a 1-50 Hz bandpass filter. Figure 6 This is the result of the 30s optical fiber vibration amplitude signal being preprocessed by the vibration signal processing unit 4.

[0089] In step (6), the vibration signal processing unit 4 transmits the pre-processed data to the wave impact force inversion unit 5. In order to accurately judge the wave impact force in each 30-second window and associate it with an accurate time tag, the wave impact force inversion unit 5 first performs a highly precise time series segmentation on the 24-hour optical fiber vibration amplitude signal. Each data window is fixed to 30 seconds. By designing a time window with a 15-second overlap (i.e., the start time of each window is 15 seconds later than the previous window), the continuity and integrity of the wave event are ensured to be retained in at least one complete window. The time window data is standardized to eliminate errors caused by temperature and humidity on site. The standardized time window data is input into the wave impact force judgment model for judgment. The wave impact force judgment model processes the pre-processed data window through a multi-layer attention convolution layer to learn the key amplitude time series features in the vibration signal. These amplitude time series features are emphasized through the attention mechanism to ensure that the wave impact force judgment model focuses on the signal part that has the greatest influence on the judgment of the impact force. Then, the fully connected layer integrates these features and uses linear and nonlinear transformations to map them to a specific judgment value, i.e., the impact force of the wave. Each judgment value is accompanied by a time tag that directly corresponds to the actual time of its data window. In this process, the wave impact force judgment model accurately associates and judges the wave impact force within each time window data, improving the accuracy and reliability of the judgment.

[0090] Based on the distributed fiber optic facilities deployed on-site, distributed fiber optic acoustic sensing technology is used to monitor wave impacts in real time, acquiring fiber optic vibration amplitude signals from wave impacts. In indoor calibration experiments, an attention convolutional neural network model was trained to establish a correspondence between fiber optic vibration amplitude data and wave impact forces. The wave impact force inversion unit then segmented the fiber optic vibration amplitude data into time series and imported them into a wave impact force determination model. The model then determined the wave impact force at each moment based on the correspondence between the trained fiber optic vibration amplitude data and wave impact forces, thereby enabling intelligent, real-time monitoring of coastal wave impact forces.

Claims

1. A distributed acoustic wave sensing monitoring method for wave impact force, characterized by: The method is implemented by a distributed acoustic wave sensing monitoring device for ocean wave impact force, the monitoring device comprising a vibration sensing optical fiber (1), an anchoring unit (2), a distributed optical fiber acoustic wave sensing demodulator (3), a vibration signal processing unit (4) and an ocean wave impact force inversion unit (5); the vibration sensing optical fiber (1) is fixed to the surface of the coast via the anchoring unit (2); the vibration sensing optical fiber (1) is connected to the distributed optical fiber acoustic wave sensing demodulator (3) via an optical fiber lead; the distributed optical fiber acoustic wave sensing demodulator (3) transmits the collected vibration amplitude signal of the vibration sensing optical fiber to the ocean wave impact force inversion unit (5) via the vibration signal processing unit (4); The monitoring method comprises the following steps: Step (1) is to lay out a vibration sensing optical fiber (1) on an indoor coastal model for a calibration test, attach a force sensor (7) to the vibration sensing optical fiber (1), and generate waves with different impact forces through a wave maker (6); collect vibration amplitude data on the vibration sensing optical fiber through a distributed optical fiber acoustic wave sensor demodulator (3), and record the impact force monitored by the force sensor; and divide the vibration amplitude data of the vibration sensing optical fiber at the force sensor during each wave impact into a training set, a validation set, and a test set; Step (2) is to design and train an attention convolutional neural network model based on the training set, validation set and test set, i.e., a wave impact force judgment model, thereby obtaining a functional relationship between the optical fiber vibration amplitude signal and the wave impact force, and judging the magnitude of the wave impact force; the process is as follows: In step (2.1), the wave impact force judgment model uses a one-dimensional convolution layer to calculate the weighted sum within each window along the length of the optical fiber vibration amplitude signal by sliding the convolution kernel to extract the time series characteristics of the optical fiber vibration amplitude signal; In step (2.2), an attention mechanism module is added to the wave impact force judgment model. The attention mechanism module determines the influence weight of the amplitude data in the fiber amplitude signal on the impact force through pooling convolution; In step (2.3), after the attention mechanism module and the one-dimensional convolutional layer extract the amplitude time series features, a fully connected layer is used to take the output of the previous layer as input, transform it using weights and biases, and output one or more predicted values ​​to determine the final wave impact force; In step (2.4), the wave impact force judgment model uses the mean square error as the loss function to measure the deviation between the predicted value and the true value, and optimizes the loss function; The functional relationship between the optical fiber vibration amplitude signal and the wave impact force is as follows: Where: F is the impact force of the waves; A is the fiber amplitude; λ is the wavelength of the laser pulse; the phase change Δφ; n is the refractive index of the fiber; ξ is the elastic-optical coefficient; L g is the gauge length; the pulse interval is ΔT; f is the model function relationship; Step (3), fixing the vibration sensing optical fiber (1) to the coast impacted by the waves through the anchoring unit (2); Step (4), connecting the vibration sensing optical fiber (1) to the distributed optical fiber acoustic wave sensor demodulator (3) through an optical fiber lead, setting the same sampling parameters as those in the calibration test, and collecting the optical fiber vibration amplitude signal generated by the wave impact vibration sensing optical fiber; Step (5), transmitting the optical fiber vibration amplitude signal collected by the distributed optical fiber acoustic wave sensor demodulator (3) to the vibration signal processing unit (4), and preprocessing the optical fiber vibration amplitude data to improve the data signal-to-noise ratio; In step (6), the vibration signal processing unit (4) transmits the pre-processed optical fiber vibration amplitude data to the wave impact force inversion unit (5), and the pre-processed optical fiber vibration amplitude signal data is divided into a plurality of time window data of fixed length; the time window data is input into the wave impact force judgment model; the one-dimensional convolution layer of the wave impact force judgment model first extracts the data features of each time window, and then the attention mechanism module adjusts the weights of the data features, and the fully connected layer forms a comprehensive feature vector with the features weighted by attention to predict the impact force of the waves in the time window data, and outputs a quantized impact force value.

2. The distributed acoustic wave sensing monitoring method for ocean wave impact force according to claim 1, characterized in that: In step (1), the magnitude of the impact force monitored by the force sensor is used as a label, and the optical fiber vibration amplitude data at the force sensor during the wave impact period is used as a data set, and the data set is divided into a training set, a validation set, and a test set.

3. The distributed acoustic wave sensing monitoring method for ocean wave impact force according to claim 1, characterized in that: In step (2.4), the wave impact force judgment model uses the mean square error as the loss function to measure the deviation between the predicted value and the true value, and optimizes the loss function; at the same time, the wave impact force judgment model uses the Adam optimizer to update the parameters.

4. The distributed acoustic wave sensing monitoring method for ocean wave impact force according to claim 1, characterized in that: In step (5), the vibration signal processing unit (4) performs pinch-out, de-linear trend, and filtering preprocessing on the collected optical fiber vibration amplitude signal.

5. The distributed acoustic wave sensing monitoring method for ocean wave impact force according to claim 1, characterized in that: In step (6), the fully connected layer forms a comprehensive feature vector using the attention-weighted features to predict the impact force of the waves in the time window data, and outputs a quantized impact force value through a set threshold or calculation model.

6. The distributed acoustic wave sensing monitoring method for ocean wave impact force according to claim 1, characterized in that: In step (6), the length of the time window data is half of the wave period.

7. The distributed acoustic wave sensing monitoring method for ocean wave impact force according to claim 1, characterized in that: In step (6), each time window data overlaps with the subsequent time window data by half of its time length.

8. The distributed acoustic wave sensing monitoring method for ocean wave impact force according to claim 1, characterized in that: In step (6), each time window data is standardized before being input into the wave impact force judgment model.

9. The distributed acoustic wave sensing monitoring method for ocean wave impact force according to claim 1, characterized in that: In step (6), the fully connected layer forms a comprehensive feature vector with the attention-weighted features, and uses linear and nonlinear transformations to map it into a judgment value, which is the impact force of the waves.

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