A real-time monitoring and early warning system and method for the settlement direction of a breakwater foundation

Abstract

The application relates to the technical fields of structural health monitoring and geotechnical engineering, and discloses a real-time monitoring and early warning system and method for the settlement direction of a breakwater foundation, which comprises a data acquisition module, a storage module and a processing module; the data acquisition module is used for acquiring measurement data of at least one sensor arranged on a breakwater and the foundation thereof in real time; the storage module is used for storing a preset physical-based reduced-order model; the reduced-order model represents the overall deformation law of the breakwater and foundation system through a group of core deformation modes; the processing module is connected with the data acquisition module and the storage module; the processing module comprises a data assimilation module which is used for fusing the measurement data and the reduced-order model in real time. The deformation field reconstruction module is arranged, the problem that traditional monitoring can only acquire discrete point information is effectively solved, real-time and high-precision expansion from sparse measurement to a continuous overall deformation field is realized, and more intuitive and reliable basis for comprehensively evaluating the safety state of a structure is provided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of structural health monitoring and geotechnical engineering, in particular to a breakwater foundation settlement direction real-time monitoring and early warning system and method. BACKGROUND

[0002] As an important port and coastal protection project, the structural safety and stability of the breakwater is crucial to protect the normal operation of the port and the coastal area from wave erosion. Under the action of long-term cyclic loads such as waves and tides and the self-consolidation settlement of the foundation, the breakwater structure and its foundation are prone to uneven deformation, and even may induce soil liquefaction, sliding instability and other catastrophic accidents. Therefore, long-term and effective monitoring and early warning of the deformation of the breakwater foundation, especially the settlement form and development trend, is a key link to ensure its long-term safe service and implement preventive maintenance.

[0003] At present, the deformation monitoring of the breakwater foundation mainly relies on traditional geotechnical engineering and geodetic methods. These methods usually arrange global positioning system (GPS) receivers, levels, inclinometers, settlement gauges and other sensors on the surface or inside the key parts of the breakwater, and obtain displacement or inclination data at specific positions through periodic or continuous measurement. However, this kind of monitoring method based on discrete physical measurement points has its inherent limitations. The information it can provide is only the displacement or strain data of a limited number of measurement points, and it cannot reveal the continuous deformation pattern of the wide area between the measurement points or even the entire foundation. The "sparsity" and "locality" of this information make it difficult for engineering managers to grasp the overall deformation law of the structure from a macroscopic perspective, and it is insufficient to identify early signs of damage with global characteristics such as the formation and penetration of potential shear bands.

[0004] The early warning mechanism based on such discrete data is usually simple, and mostly uses a fixed threshold setting method for a single measurement point. This method often lags behind the warning signal, and usually triggers when the deformation has developed to a large extent and the structure may have irreversible damage, lacking insight and foresight into the early stages of the disaster process, and is difficult to meet the actual needs of preventive maintenance.

[0005] Although high-fidelity numerical simulation methods (such as finite element method) can theoretically accurately calculate the deformation field of the entire system and reveal its internal mechanical mechanism, such models often contain tens of thousands or even millions of degrees of freedom, and the calculation cost is extremely high, requiring a large amount of time for a single simulation operation. This characteristic determines that it cannot be directly applied to online monitoring and early warning systems that require continuous and real-time feedback, and its application is mostly limited to design stage checking or offline post-disaster inversion analysis.

[0006] The prior art is generally plagued by the problem of disconnection between "physical measurement" and "physical model": the former can reflect the real state, but the information is incomplete and the early warning is lagging; the latter can provide complete information, but cannot meet the real-time requirement. Therefore, a new technology is needed to efficiently fuse sparse real-time measurement data with accurate physical model knowledge to realize real-time and accurate inversion of the continuous deformation field of the breakwater foundation, and on this basis, intelligent early warning. SUMMARY

[0007] In view of the deficiencies of the prior art, the present application provides a real-time monitoring and early warning system for the settlement direction of a breakwater foundation, aiming to solve the technical problem that the prior art cannot fuse sparse physical measurement data with complex structural physical models, so as to fail to obtain a continuous three-dimensional deformation field of the breakwater foundation in real time and accurately and to effectively perform early warning.

[0008] To achieve the above object, the present application is implemented by the following technical solution: a real-time monitoring and early warning system for the settlement direction of a breakwater foundation, comprising:

[0009] a data acquisition module for acquiring measurement data of at least one sensor arranged on the breakwater and / or its foundation in real time;

[0010] a storage module for storing a preset, physics-based reduced-order model, which represents the overall deformation law of the breakwater and foundation system through a set of core deformation modes;

[0011] a processing module connected to the data acquisition module and the storage module, the processing module comprising:

[0012] a data assimilation module for real-time fusion of the measurement data and the reduced-order model to optimally estimate modal coefficients representing the contribution degree of the core deformation modes at the current time;

[0013] a deformation field reconstruction module for real-time reconstruction of the continuous three-dimensional deformation field of the breakwater and foundation system based on the optimally estimated modal coefficients and the core deformation modes;

[0014] an early warning module for analysis based on the continuous three-dimensional deformation field to generate an early warning signal.

[0015] Preferably, the reduced-order model stored in the storage module is obtained by the following method: based on a high-fidelity finite element model, a deformation snapshot matrix is generated by applying multiple load cases, and the deformation snapshot matrix is subjected to eigenorthogonal decomposition to extract a set of optimal orthogonal basis vectors as the core deformation modes and form a basis matrix .

[0016] Preferably, the data assimilation module is based on a preset state space model and employs a Kalman filter for optimal estimation; wherein the state space model comprises a state equation for describing the evolution law of the modal coefficients, which is in the form of:

[0017] ;

[0018] wherein, is the modal coefficient vector at time ; is a state transition matrix; is a generalized force vector; is a control input matrix; is process noise.

[0019] Preferably, the data acquisition module further comprises:

[0020] at least one internal state sensor for measuring the internal displacement or strain of the breakwater and foundation system, and the measurement data of the internal state sensor are used for the update process of the data assimilation module; and

[0021] at least one external load sensor for measuring the external load acting on the breakwater, and the measurement data of the external load sensor are used for the prediction process of the data assimilation module after processing.

[0022] Preferably, the processing module is further configured to: project the external load vector collected by the external load sensor into the modal space through a basis matrix composed of the core deformation mode to form the generalized force vector , and the calculation method is:

[0023] ;

[0024] wherein, is the generalized force vector; is the transpose of the core deformation mode matrix; is the external load vector;

[0025] and the generalized force vector is taken as a feedforward control input item of the state space model.

[0026] Preferably, the early warning module is specifically configured to analyze by at least one of the following ways:

[0027] spatially deriving the continuous three-dimensional deformation field to obtain a global strain field, and generating a warning based on the formation, expansion and through-tendency of a high shear strain region in the strain field; or ​

[0028] monitoring the energy distribution of the modal coefficients in real time, and generating a warning based on the abnormal and sustained growth trend of the high-order modal energy representing local complex deformation.

[0029] Preferably, the processing module further comprises an adaptive correction module for diagnosing the degree of mismatch between the reduced-order model and the actual physical state of the breakwater and foundation system.

[0030] Preferably, the adaptive correction module quantifies the degree of mismatch by continuously monitoring the statistical properties of the innovation sequence in the Kalman filter update step , wherein the innovation sequence is used to correct the prior state estimate to obtain the posterior state estimate , and the correction process follows the following equation:

[0031] ;

[0032] wherein, is the Kalman gain; is the observation matrix.

[0033] Preferably, it further comprises an output module for visualizing the continuous three-dimensional deformation field ; wherein the continuous three-dimensional deformation field is reconstructed by linearly combining the optimally estimated modal coefficients and the basis matrix of the core deformation mode , and the reconstruction equation is:

[0034] .

[0035] The present application also provides a real-time monitoring and early warning method for the settlement direction of a breakwater foundation, comprising the following steps:

[0036] pre-building and storing a physics-based reduced-order model, which represents the overall deformation law of the breakwater and foundation system through a set of core deformation modes;

[0037] real-time acquisition of measurement data from at least one sensor deployed on the breakwater and / or its foundation;

[0038] real-time fusion of the measurement data and the reduced-order model through a data assimilation process to optimally estimate the modal coefficients representing the contribution of the core deformation modes at the current time;

[0039] reconstruct a continuous three-dimensional deformation field of the breakwater and foundation system in real time based on the optimal estimated modal coefficients and the core deformation mode; and

[0040] analyze based on the continuous three-dimensional deformation field to generate an early warning signal.

[0041] The application provides a breakwater and foundation settlement direction real-time monitoring and early warning system and method.

[0042] 1、The application sets a deformation field reconstruction module, uses the low-dimensional modal coefficients estimated by the data assimilation module in real time, and performs linear combination with the core deformation mode pre-stored in the storage module and containing the overall deformation law of the system, so that the limitation that the traditional monitoring method can only obtain discrete point information is overcome, real-time reconstruction of a continuous, high-resolution three-dimensional overall deformation field of the breakwater and foundation system from sparse and incomplete sensor point measurement is achieved, and it is possible to comprehensively and intuitively grasp the structure macroscopic deformation posture and internal state.

[0043] 2、The application sets an intelligent early warning module, performs in-depth and physics-based secondary analysis on the reconstructed continuous three-dimensional deformation field, instead of relying on a simple single-point displacement threshold, so that a more reliable and more forward-looking early warning signal can be generated based on the formation and penetration trend of a potential shear failure surface in the strain field or based on abnormal growth of high-order modal energy representing local instability precursors, and the intelligent level and timeliness of early warning are significantly improved.

[0044] 3、The application pre-constructs and stores a physics-based reduced-order model in the offline stage, converts the calculation task in the online stage from solving a high-fidelity finite element model with extremely high degrees of freedom and huge calculation cost to a lightweight operation that only needs to iteratively solve low-dimensional modal coefficients, thereby greatly reducing the complexity and time cost of online calculation, and making real-time and continuous monitoring and analysis of large geotechnical structures technically possible.

[0045] 4、The application sets a data assimilation module and optimally adopts a Kalman filter framework, dynamically and optimally fuses the reduced-order model prediction process containing physical laws and the sensor measurement update process containing real-world information, thereby effectively overcoming the error accumulation problem that may exist in a single physical model and the partiality and noise interference problem that exists in a single sensor measurement, and obtaining a more accurate and robust state estimation result than simply relying on a model or simply relying on measurement.

[0046] 5、The application can diagnose the mismatching degree between the preset reduced order model and the actual physical state of the structure which has changed due to time effect, disaster and other factors in real time and quantitatively by further configuring an adaptive correction module to continuously monitor the statistical characteristics of the residual (i.e. innovation sequence) between the model prediction and the real measurement in the data assimilation process, thereby providing a clear scientific basis for the maintenance and update of the model and ensuring the long-term reliability and vitality of the monitoring and early warning system. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 It is a schematic diagram of the system architecture of the application;

[0048] Figure 2 It is a schematic diagram of the method flow of the application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the accompanying drawings of the application. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the application.

[0050] Please refer to the accompanying drawings of the application Figure 1 The embodiment of the application provides a real-time monitoring and early warning system for the settlement direction of a breakwater foundation, comprising:

[0051] A data acquisition module is configured to acquire measurement data of at least one sensor arranged on the breakwater and / or the foundation thereof in real time.

[0052] A storage module is configured to store a preset physical-based reduced order model, wherein the reduced order model represents the overall deformation law of the breakwater and foundation system through a set of core deformation modes.

[0053] A processing module is connected to the data acquisition module and the storage module, and comprises:

[0054] A data assimilation module is configured to fuse the measurement data and the reduced order model in real time to optimally estimate modal coefficients representing the contribution degree of the core deformation modes at the current time.

[0055] A deformation field reconstruction module is configured to reconstruct a continuous three-dimensional deformation field of the breakwater and foundation system in real time based on the optimally estimated modal coefficients and the core deformation modes.

[0056] An early warning module is configured to analyze the continuous three-dimensional deformation field to generate an early warning signal.

[0057] In this embodiment, the data acquisition module as the sensing antenna of the monitoring and early warning system is the bridge connecting the physical world of the breakwater entity and the digital twin system. Its core function is to comprehensively, reliably and in real time obtain the measurement data of at least one sensor arranged on the breakwater and / or foundation.

[0058] The measurement data is multi-source and heterogeneous, which not only contains internal information reflecting the deformation state of the structure itself, but also covers external environmental factors driving the deformation of the structure. Therefore, in order to enable the subsequent processing module to perform effective model-data double-driven analysis, the data acquisition module in this embodiment is preferably designed to be able to obtain and distinguish two types of key information.

[0059] The data acquisition module can include a sub-module for obtaining internal state data. This sub-module is responsible for monitoring the response behavior of the breakwater and foundation system, i.e., the actual deformation of the internal structure under various loads.

[0060] In order to accurately capture complex deformation, this sub-module can integrate multiple types of sensors, including but not limited to:

[0061] Global Navigation Satellite System (GNSS) receivers arranged on the top of the breakwater or other key structural points to obtain high-precision three-dimensional absolute displacement;

[0062] Static leveling systems arranged along specific profiles to monitor the relative elevation changes between multiple measurement points caused by uneven settlement with high precision;

[0063] Array inclinometers buried in the foundation to obtain the lateral displacement distribution of the foundation soil at different depths, which is crucial for identifying potential sliding surfaces;

[0064] And distributed Fiber Bragg Grating (FBG) sensor arrays buried along the surface or inside the structure to obtain the strain distribution of key parts of the structure.

[0065] At each discrete calculation time step of the system, the data acquisition module will collect the measurement values of different types and formats collected from all the above internal state sensors, perform time alignment and formatting processing, and finally integrate them into a unified observation vector containing part of the real state information of the system at the current time. This observation vector is the most direct and key basis for the subsequent data assimilation module to update the state (i.e., model correction).

[0066] In addition, in order to improve the predictability and physical authenticity of the system, the data acquisition module also preferably includes a submodule for acquiring external load data. This submodule is responsible for monitoring the main external driving force causing the deformation of the breakwater, providing real-time input for subsequent physical model prediction.

[0067] In the marine environment, such external loads are mainly dynamic water pressure caused by waves and tides. Therefore, this submodule can include a dynamic water pressure sensor array arranged on the water surface of the breakwater and a tide gauge.

[0068] It is worth noting that, due to the infeasibility of implementing full sensor coverage on a wide structure surface, the external load sensors are usually sparsely arranged. Therefore, the data acquisition module or the processing module closely coupled therewith needs to first reconstruct an approximately continuous external load vector acting on the entire water surface based on the real-time pressure data of these sparse measurement points through a spatial interpolation algorithm (for example, radial basis function network or Kriging interpolation method can be used) .

[0069] This external load vector is not directly used for subsequent calculations, but needs to be converted into a driving term effective for the reduced-order model. For this purpose, the system uses the pre-set core deformation mode (i.e. POD basis matrix ) in the storage module to project the external load vector in the physical space to the low-dimensional modal space, thereby calculating the generalized force vector . The calculation process follows the following formula:

[0070] ;

[0071] wherein, is the generalized force vector; is the transpose of the core deformation mode matrix; is the external load vector; each component of the obtained generalized force vector represents the driving strength of the external total load on the corresponding core deformation mode. This vector will be input into the state prediction equation of the data assimilation module as a feedforward control term, to guide the model to make a more physically reasonable prediction of the deformation state at the next time.

[0072] The data acquisition module in this embodiment is not a simple data collection unit, but an intelligent and clearly divided data preprocessing front end. It synchronously and classifiedly acquires the internal state and external load, and performs necessary reconstruction and projection transformation on the external load data, providing two crucial inputs for the subsequent processing module: the observation vector for model correction, and the generalized force vector for model prediction.This design lays a solid data foundation for the core idea of the whole system model-data double drive.

[0073] In this embodiment, the storage module constitutes the digital core and knowledge base of the monitoring and early warning system of the application. Its key role is to provide a pre-set, efficient and accurate physical-based reduced-order model that can represent the overall deformation law of the breakwater and foundation system for the online real-time operation of the system.

[0074] It should be noted that directly using a traditional high-fidelity finite element model (FEM) for calculation in a real-time monitoring system is not feasible in engineering because of its extremely high degree of freedom, huge calculation cost, and inability to meet the real-time requirements. Therefore, the application generates a lightweight surrogate model, i.e., the reduced-order model (ROM), in the offline stage before the system runs through a series of mathematical transformations, and solidifies it in the storage module. The core of the reduced-order model is to capture and express most of the deformation behavior of the original high-fidelity model through a limited number of core deformation modes.

[0075] Specifically, the generation process of the reduced-order model stored in the storage module preferably includes the following steps:

[0076] First, a high-fidelity finite element model that can accurately reproduce the real physical process is established. This model is constructed based on the geometric structure of the breakwater, material parameters, and geotechnical engineering investigation reports. Its control equation can rigorously describe the consolidation process of the interaction between the solid skeleton and the pore fluid in the saturated porous medium, for example, the Biot consolidation theory can be used, and its basic form can be expressed as:

[0077] ;

[0078] where, is the effective stress tensor of the soil skeleton; is the pore water pressure scalar; is the unit tensor; is the body force vector; is the permeability matrix; is the displacement vector of the soil skeleton. This high-fidelity model is the data source and physical basis for generating the reduced-order model.

[0079] Second, based on the above high-fidelity model, a deformation snapshot database covering various potential deformation states in the entire life cycle of the system is generated. This process involves a large number of numerical simulation operations by applying a series of parameterized load cases and boundary conditions to the high-fidelity model (e.g., simulating different levels of wave loads, different combinations of tidal level changes, foundation parameter uncertainties, etc.). The first The global displacement response vector of the model containing all degrees of freedom is obtained by the sub-simulation . As a "snapshot" sample, after the sub-simulation is completed , all snapshot samples are combined column by column to construct a high-dimensional snapshot matrix .

[0080] ;

[0081] wherein, is the snapshot matrix; is the i-th column vector of the snapshot matrix . It is a high-dimensional vector, and the dimension is ; ; is the total number of snapshots; and is the total number of node degrees of freedom of the original high-fidelity finite element model used to generate the deformation snapshot. Subsequently, the snapshot matrix

[0082] is processed in a dimension reduction manner to extract the most important core deformation mode. In the embodiment, the Proper Orthogonal Decomposition (POD) method is preferably adopted. The method aims to find a set of optimal orthogonal bases, so that the projection energy of the snapshot sample on the set of bases is most concentrated. Since the number of degrees of freedom of the high-fidelity model is much larger than the number of snapshots , in order to improve the calculation efficiency, the Method of Snapshots is adopted here. The calculation steps of the method are as follows: first, the autocorrelation matrix of the snapshot matrix is calculated: ;

[0083] ;

[0084] This step converts the analysis of a huge matrix into the analysis of a much smaller square matrix by calculating the correlation between all snapshots, which greatly reduces the calculation complexity. Then, the eigenvalue problem of the autocorrelation matrix is solved:

[0085] ;

[0086] wherein, is a diagonal matrix composed of eigenvalues , and the numerical size represents the "energy" contained by the corresponding mode or the contribution degree to the overall deformation; is the corresponding eigenvector matrix, which provides the coefficients for linearly combining the original snapshots into the final core deformation mode.

[0087] According to the characteristic value Sort by size in descending order and select the first indivual The mode corresponding to the maximum eigenvalue is selected to ensure that most of the system deformation energy is captured. The modes constitute the core deformation mode that can capture the system deformation information with the highest efficiency. The core deformation modes are combined into a POD basis matrix , which is calculated by the following formula:

[0088] ;

[0089] in, and They are respectively The eigenvector matrix and diagonal matrix of the largest eigenvalues.

[0090] At this point, the core content actually stored in the storage module is the low-dimensional POD basis matrix that contains the inherent deformation law of the system. Its physical meaning is that the overall deformation field of the foundation at any time is complex and high-dimensional. , can be approximated with high precision as A linear combination of core deformation modes:

[0091] ;

[0092] in, is a low-dimensional vector that varies with time and is called the modal coefficient.

[0093] Therefore, by presetting the reduced-order model (i.e., the POD basis matrix) in the storage module ), the present invention successfully transforms a complex, high-dimensional vector The problem is transformed into a simple one to solve the low-dimensional modal coefficient vector The basis matrix This module is repeatedly called by the processing module during the online monitoring phase. It serves as the basis for the data assimilation module to construct the observation equation, a bridge for the deformation field reconstruction module to restore low-dimensional modal coefficients to a high-dimensional continuous deformation field, and a projection operator for calculating the effect of external loads on the driving effects of various modes. This module is the technical prerequisite for achieving real-time and efficient operation of the entire system.

[0094] In this embodiment, the processing module serves as the computational and decision-making hub of the monitoring and early warning system described herein. Its hardware can be a central processing unit (CPU), digital signal processor (DSP), or application-specific integrated circuit (ASIC), while its software is embodied as a series of collaborative algorithms. This module, bidirectionally connected to the data acquisition module and storage module, is responsible for executing the core technical solution of the present invention: the deep integration of offline physical knowledge with online measured data to achieve real-time, accurate understanding and early warning of the breakwater foundation's condition.

[0095] To achieve this goal, the processing module is preferably designed to include several logically independent and functionally progressive sub-modules, including a data assimilation module, a deformation field reconstruction module, an early warning module, and an adaptive correction module.

[0096] First, the processing module receives the real-time data stream from the data acquisition module, including the observation vector and external load data; at the same time, it calls the core reduced-order model, the POD basis matrix, from the storage module This information will serve as the basic input for subsequent calculations.

[0097] The data assimilation module is the core hub connecting the physical model and the real measurement data. Its technical goal is to dynamically fuse the reduced-order model provided by the storage module, which contains the system's prior physical knowledge, with the sparse measurement data provided by the data acquisition module, which reflects the current real state of the system, through a stable and efficient algorithm framework. Finally, the modal coefficient vector representing the contribution of the core deformation mode at the current moment is solved in real time in an optimal estimation manner. .

[0098] Specifically, to achieve the aforementioned technical effects, the data assimilation module in this embodiment preferably uses the Kalman filter as its core algorithm architecture. This choice is based on the Kalman filter's optimality and computational efficiency when handling linear Gaussian system state estimation problems. The entire data assimilation process is constructed within a state space framework, and real-time tracking of modal coefficients is achieved through continuous, cyclical iterations of the "prediction" and "update" steps.

[0099] Step 1: Prediction

[0100] This step occurs at each time step The core task is to execute at the beginning of the The optimal state estimation of the current moment is completely dependent on the preset physical model. The state of the system. This process does not involve any actual measurement at the current moment, but is purely based on the deduction of model rules. First, the module predicts the state. It calls the posterior state estimate of the previous moment. , and use the state transfer matrix solidified in the storage module It is forward propagated in time. At the same time, it integrates the generalized force vector obtained by processing the external load sensor As feedforward control input to improve the accuracy of prediction. This process calculates the prior state estimate of the current moment , its mathematical expression is:

[0101] ;

[0102] in: is the prior state estimation vector at time k; For the moment The posterior state estimation vector is the final result of the previous iteration; is the state transfer matrix, which describes the modal coefficients from time arrive The inherent evolutionary laws of For the moment The generalized force vector is the projection of the external load in the modal space; To control the input matrix, it applies the influence of the generalized forces to the state vector.

[0103] Secondly, the module performs error covariance prediction. While predicting the state, the uncertainty of its estimate will also increase due to the passage of time and the inherent uncertainty of the model. This step is to quantify the growth of this uncertainty and calculate the prior error covariance matrix The calculation formula is as follows:

[0104] ;

[0105] in: For the moment The prior error covariance matrix of ; For the moment The posterior error covariance matrix of ; is the process noise covariance matrix, which represents the errors and uncertainties in the physical model itself that cannot be accurately modeled.

[0106] Step 2: Update

[0107] After the prediction step is completed, once the data acquisition module provides the current time Real sensor measurement data , the update step is immediately initiated. The core task of this step is to revise the prior estimate given by the prediction step using the real external information, so as to obtain a more accurate posterior estimate.

[0108] First, the module calculates a crucial weight matrix - the Kalman gain . The optimality of this gain matrix lies in its ability to perfectly balance the credibility of model prediction (reflected by ) and the credibility of measurement data (reflected by the measurement noise covariance ). Its calculation formula is:

[0109] ;

[0110] where, is the Kalman gain matrix; is the observation matrix, which establishes the linear mapping relationship between the state vector that cannot be directly observed (modal coefficients ) and the measurement vector that can be directly observed (sensor readings ), i.e. ; is the measurement noise covariance matrix, representing the statistical characteristics of the measurement error existing in the sensor itself.

[0111] Second, the module performs the core state update operation. It uses the Kalman gain just calculated to revise the prior state estimate , to obtain the posterior state estimate that has fused the measurement information. This is the final output of the data assimilation module at the current time, representing the optimal estimate value of the modal coefficients. Its mathematical expression is:

[0112] ;

[0113] The in the formula is called the innovation sequence or measurement residual, which reflects the difference between the real measurement and the model prediction, and is the fundamental driving force for state revision. Finally, in order to complete the closed loop of iteration and provide input for the prediction of the next time step, the module also needs to update the error covariance. Since new measurement information has been introduced, the uncertainty of the system state should naturally decrease. This step calculates the posterior error covariance matrix to quantify this reduction in uncertainty:

[0114] ;

[0115] where, is the identity matrix.

[0116] Through the above-mentioned "prediction-update" cycle, the data assimilation module realizes the dynamic optimal fusion of the physical model prediction and the real data measurement, thereby being able to continuously and stably output the modal coefficient vector with high precision, and providing reliable data input for the subsequent deformation field reconstruction and early warning analysis module.

[0117] The deformation field reconstruction module is a key functional unit in the processing module. Its main function is to receive and analyze the optimal estimation result output by the data assimilation module, and based on the result and the pre-set reduced-order model in the storage module, to restore the abstract low-dimensional state description to the physically intuitive and complete continuous three-dimensional deformation field of the breakwater and foundation system in real time and efficiently.

[0118] The deformation field reconstruction module, as a "decoder", performs the inverse operation of the offline reduced-order process. The input information it receives mainly has two parts:

[0119] One is the posterior state estimation output by the data assimilation module at time , i.e. the optimal modal coefficient vector . The dimension of this vector is very low (for ), but each component of it accurately quantifies the contribution degree or "weight" of the corresponding core deformation mode to the total deformation of the system at the current time.

[0120] The other is the POD basis matrix , which is the core of the reduced-order model, called by it from the storage module. Each column of this matrix is a high-dimensional vector describing a basic deformation form. The running mechanism of this module is based on the basic principle of the reduced-order model, i.e. any complex deformation field at any time can be linearly superimposed by a group of core deformation modes. Therefore, the reconstruction process is greatly simplified in calculation, and only one matrix and vector multiplication operation needs to be performed. Its mathematical expression form is as follows:

[0121] ;

[0122] Wherein, is the input column vector provided by the data assimilation module, containing modal coefficients; is the POD basis matrix called from the storage module, wherein is the total number of degrees of freedom of the original high-fidelity finite element model, is the number of core deformation modes. The th column of this matrix represents the th is the high-dimensional displacement vector computed by the module as output. The dimension of this vector is identical to the original high-fidelity finite element model, and each of its components corresponds to the displacement value of a node in the model at one degree of freedom (e.g. X, Y, or Z direction).

[0123] By performing the above operations, the module is able to instantaneously assimilate the data module output at each time step into a set of abstract modal coefficients, "unfold" or "up-sample" into a complete deformation field covering the entire breakwater and its foundation, containing degrees of freedom.

[0124] The setup of this deformation field reconstruction module is of vital importance. It enables the critical leap from sparse, discrete sensor point measurements to continuous, global body deformation state awareness. Traditional monitoring methods can only obtain displacement values at limited measurement points, and cannot know the deformation of the region between measurement points and the deep structure. The present invention overcomes this limitation through this module, which can generate a high-resolution, visualized three-dimensional deformation cloud chart.

[0125] More importantly, the continuous three-dimensional deformation field output by this module is not only for the final visualized display, but also the necessary data basis for subsequent in-depth safety assessment and intelligent early warning. Only by obtaining the continuous displacement field, it is possible to obtain the global strain field, stress field or rotation field and other physical derived quantities more indicative of the structure safety state through further calculation (e.g. spatial derivation). Therefore, the deformation field reconstruction module is an indispensable bridge connecting the "state estimation" and "intelligent early warning" two links, and its efficiency and accuracy are the premise to ensure that the entire system can perform real-time, in-depth analysis.

[0126] The early warning module is the final decision and output unit of the processing module, and its technical purpose is to perform real-time, intelligent assessment and grading on the safety state of the monitored object. The module receives the high-resolution continuous three-dimensional deformation field generated by the deformation field reconstruction module, and performs in-depth, physically-based analysis to generate a more reliable and forward-looking early warning signal that goes beyond the traditional single-point threshold method.

[0127] The input of this early warning module is the global displacement vector computed by the deformation field reconstruction module at each time step covering the entire breakwater and its foundation. It is important to emphasize that only based on this displacement vector Setting thresholds for individual node displacement values ​​for early warning is not sufficient to reveal the complex failure mechanism within the structure. Therefore, the early warning module of the present invention is preferably designed to use at least one or more of the following analysis strategies to generate early warning signals. A preferred analysis method is field derivative analysis. This method aims to extract physical derivatives that are more indicative of structural stability from the continuous displacement field. The early warning module is embedded with a numerical differentiation algorithm that can be used based on the input displacement field. Calculate the strain tensor field of the entire domain Under the small deformation assumption, the relationship between the strain tensor and the displacement gradient can be expressed as:

[0128] ;

[0129] in, is the displacement vector field After obtaining the strain field, the module can further calculate key indicators such as maximum principal strain, volumetric strain or shear strain.

[0130] In soil mechanics, foundation instability often manifests as shear failure. Therefore, a core function of this early warning module is to automatically identify and track the evolution of high shear strain areas. The module has a preset shear strain safety threshold based on material properties or design specifications. During operation, it continuously compares the calculated global shear strain field with this threshold to identify the formation, expansion, and penetration trends of high shear strain areas. When the range, connectivity, or peak value of the high shear strain area meets the preset danger criteria (for example, the formation of a continuous potential sliding zone), the module determines that the system is at risk of instability and generates a warning signal of the corresponding level.

[0131] As a supplement to the above analysis, another preferred analysis method is modal energy analysis. This method does not directly analyze the displacement field, but starts from the intrinsic mode of system deformation in order to capture earlier signs of instability. For this purpose, this module can be directly connected to the data assimilation module to obtain the optimal modal coefficient vector output by it. Theoretically, the total deformation energy of the system is proportional to the sum of the squares of the modal coefficients, and the energy allocated to the first Core deformation modes Energy on It is proportional to the square of its corresponding modal coefficient:

[0132] ;

[0133] The pre-warning module is configured with an algorithm for calculating the energy of each mode and its proportion in the total energy at each time step in real time. Generally, most of the deformation energy of the system is concentrated in a few low-order modes representing the overall deformation, while the energy proportion of high-order modes representing local complex deformation is extremely low.

[0134] The core pre-warning logic of the module is to continuously monitor the distribution pattern of such modal energy. When the energy proportion of one or more high-order modes is found to have an abnormal and sustained growth trend, even if the total deformation (i.e., the displacement reading of all measuring points) at the moment is still within the safe range, the module will identify it as an important early warning signal. This is because the transfer of energy from low-order modes to high-order modes often indicates that the structure is developing a local and more complex deformation mechanism, such as the initiation of local damage or nonlinear behavior, which is a precursor to macro instability. When the energy proportion of a certain high-order mode or its growth rate exceeds the dynamic baseline set based on historical data statistics, the module generates a high-priority pre-warning signal.

[0135] In summary, the pre-warning module in this embodiment, through the organic combination of field-derived quantity analysis and modal energy analysis, has built a multi-dimensional and multi-level comprehensive pre-warning system from macro geometry to internal energy pattern. It not only can judge the impending dangerous state, but also can capture subtle and structural changes to provide valuable predictive information for the safe operation of the breakwater, thus realizing the transition from "passive response" to "active prediction".

[0136] To ensure the long-term effectiveness of the system, the processing module also preferably integrates an adaptive correction module. The function of this module is to diagnose the degree of mismatch between the reduced-order model in the storage module and the real ground that has changed physically over time. The module achieves diagnosis by continuously monitoring the statistical properties of the innovation sequence produced by the data assimilation module (Kalman filter) in the update step (i.e., the difference between the actual measurement and the model predicted measurement). In the ideal state where the model perfectly matches reality, the innovation sequence should be zero-mean white noise. When the degree of mismatch increases, the statistical properties of the sequence will deviate from the ideal state. The module quantifies the degree of mismatch by calculating statistical quantities such as normalized innovation square (NIS) and performing chi-square test. When the mismatch index continuously and significantly exceeds its theoretical confidence interval within a set time window, the module determines that the current reduced-order model has failed and generates a correction signal prompting the operator to update the reduced-order model, thus realizing closed-loop adaptive maintenance of the system.

[0137] Please refer to Figure 2 , the application also provides a real-time monitoring and pre-warning method for the settlement direction of a breakwater foundation. The method comprises the following steps:

[0138] a physics-based reduced-order model is pre-constructed and stored, which represents the overall deformation behavior of the breakwater and foundation system by a set of core deformation modes;

[0139] measurement data of at least one sensor disposed on the breakwater and / or its foundation are acquired in real time;

[0140] a data assimilation process is performed to optimally estimate modal coefficients representing the contribution of the core deformation modes at the current time instant by fusing the measurement data and the reduced-order model in real time;

[0141] a continuous three-dimensional deformation field of the breakwater and foundation system is reconstructed in real time based on the optimally estimated modal coefficients and the core deformation modes; and

[0142] analysis is performed based on the continuous three-dimensional deformation field to generate a warning signal;

[0143] The method of the embodiment can be used to implement the system embodiment described above, and has similar principles and technical effects, which will not be described here again.

[0144] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring and early warning system for breakwater foundation settlement direction, characterized in that: include: a data acquisition module, configured to acquire measurement data from at least one sensor disposed on the breakwater and / or its foundation in real time; a storage module for storing a preset, physically based reduced-order model, wherein the reduced-order model characterizes the overall deformation law of the breakwater and foundation system through a set of core deformation modes; A processing module, connected to the data acquisition module and the storage module, comprising: a data assimilation module for fusing the measurement data with the reduced-order model in real time to optimally estimate modal coefficients representing the contribution of the core deformation mode at the current moment; A deformation field reconstruction module is used to reconstruct the continuous three-dimensional deformation field of the breakwater and foundation system in real time based on the optimal estimated modal coefficients and the core deformation mode; The early warning module is used to analyze the continuous three-dimensional deformation field to generate an early warning signal.

2. A real-time monitoring and early warning system for breakwater foundation settlement direction according to claim 1, characterized in that: The reduced-order model stored in the storage module is obtained by applying multiple load cases based on a high-fidelity finite element model to generate a deformation snapshot matrix. , and perform intrinsic orthogonal decomposition on the deformation snapshot matrix to extract a set of optimal orthogonal basis vectors as the core deformation mode and form a basis matrix .

3. A real-time monitoring and early warning system for breakwater foundation settlement direction according to claim 1, characterized in that: The data assimilation module is based on a preset state space model and uses a Kalman filter for optimal estimation; wherein the state space model includes a state equation for describing the evolution law of the modal coefficients, which is in the form of: ； in, For the moment The modal coefficient vector of ; is the state transfer matrix; is the generalized force vector; is the control input matrix; is the process noise.

4. A real-time monitoring and early warning system for breakwater foundation settlement direction according to claim 3, characterized in that: The data acquisition module further specifically includes: At least one internal state sensor is used to measure the internal displacement or strain of the breakwater and foundation system, and its measurement data An update process for the data assimilation module; and At least one external load sensor is used to measure the external load acting on the breakwater, and the measurement data thereof is processed and used in the prediction process of the data assimilation module.

5. A real-time monitoring and early warning system for breakwater foundation settlement direction according to claim 4, characterized in that: The processing module is further configured to: convert the external load vector collected by the external load sensor into , the basis matrix formed by the core deformation mode , projected into the modal space to form the generalized force vector , which is calculated as follows: ； in, is the generalized force vector; is the transpose of the core deformation mode matrix; is the external load vector; And the generalized force vector As a feedforward control input to the state-space model.

6. A real-time monitoring and early warning system for breakwater foundation settlement direction according to claim 1, characterized in that: The early warning module is specifically configured to perform analysis in at least one of the following ways: performing spatial differentiation of the continuous three-dimensional deformation field to obtain a global strain field, and generating an early warning based on the formation, expansion, and penetration trends of high shear strain regions in the strain field; or The energy distribution of the modal coefficients is monitored in real time, and an early warning is generated based on an abnormal and sustained growth trend of high-order modal energy representing local complex deformation.

7. A real-time monitoring and early warning system for breakwater foundation settlement direction according to claim 3, characterized in that: The processing module further includes an adaptive correction module for online diagnosis of a mismatch between the reduced-order model and an actual physical state of the breakwater and foundation system.

8. A real-time monitoring and early warning system for breakwater foundation settlement direction according to claim 7, characterized in that: The adaptive correction module is to continuously monitor the innovation sequence in the Kalman filter update step The statistical characteristics of the mismatch are used to quantify the mismatch; wherein the innovation sequence is used to estimate the prior state Correction is performed to obtain the posterior state estimate , and its correction process follows the following equation: ； in, is the Kalman gain; is the observation matrix.

9. A real-time monitoring and early warning system for breakwater foundation settlement direction according to claim 1, characterized in that: Also includes an output module, the output module is used to Perform visual display; wherein the continuous three-dimensional deformation field The modal coefficients obtained by the optimal estimate are The basis matrix of the core deformation pattern The reconstruction equation is: 。 10. A real-time monitoring and early warning method for breakwater foundation settlement direction, characterized in that: A system for real-time monitoring and early warning of breakwater foundation settlement direction according to any one of claims 1 to 9, comprising the following steps: Pre-building and storing a physics-based reduced-order model, wherein the reduced-order model characterizes the overall deformation law of the breakwater and foundation system through a set of core deformation modes; acquiring in real time measurement data from at least one sensor disposed on the breakwater and / or its foundation; fusing the measured data with the reduced-order model in real time through a data assimilation process to optimally estimate modal coefficients representing the contribution of the core deformation mode at the current moment; reconstructing a continuous three-dimensional deformation field of the breakwater and foundation system in real time based on the optimally estimated modal coefficients and the core deformation mode; and An analysis is performed based on the continuous three-dimensional deformation field to generate a warning signal.

Images