A Method and System for Real-Time Monitoring and Early Warning of Riverbank Slope Stability Based on Digital Twin

Abstract

This invention relates to the field of digital twin technology and proposes a method and system for real-time monitoring and early warning of riverbank slope stability based on digital twins. The method includes: constructing a coupled digital twin initial model; processing the coupled digital twin initial model using inversion analysis and state estimation algorithms based on a multi-source heterogeneous monitoring dataset to obtain an optimized digital twin model; processing the multi-source heterogeneous monitoring dataset through edge computing nodes to obtain edge preprocessed data; inputting the edge preprocessed data and the optimized digital twin model into a cloud computing cluster; processing the data using a multi-physics coupled analytical algorithm to obtain a slope stability assessment result dataset; processing the slope stability assessment result dataset using a multi-index fusion algorithm to obtain a comprehensive risk score; processing the comprehensive risk score based on a graded early warning threshold to obtain multi-level risk early warning information and engineering response plans. This invention achieves high-precision real-time monitoring, dynamic risk assessment, and graded early warning of riverbank slope stability.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a method and system for real-time monitoring and early warning of riverbank slope stability based on digital twins. Background Technology

[0002] River slope stability monitoring and early warning is a crucial component of water conservancy project safety management. It aims to identify potential instability risks and issue early warnings in a timely manner by monitoring slope deformation, stress, and environmental conditions in real time. With the development of digital technology, digital twin technology offers a new solution for river slope stability monitoring, enabling the construction of virtual mapping models of physical slopes to achieve real-time perception, dynamic analysis, and intelligent early warning of slope conditions. However, river slope systems involve complex coupling effects from multiple physical fields, including water flow infiltration, soil deformation, and structural response. Their stability is comprehensively influenced by geological conditions, hydrological environment, and meteorological factors, exhibiting highly nonlinear and time-varying characteristics.

[0003] In existing technologies, riverbank slope stability monitoring and early warning mainly employ single-physics-field modeling, offline batch processing analysis, and fixed-threshold early warning mechanisms, achieving basic monitoring data acquisition and analysis functions. However, single-physics-field modeling cannot accurately describe the complex coupling effects between water, soil, and structure; offline analysis leads to response lag and insufficient real-time performance; fixed-threshold mechanisms lack adaptability to dynamic environmental changes; and there is a lack of effective fusion of multi-source monitoring data and an edge-cloud collaborative computing architecture. This makes existing systems perform poorly under complex conditions, especially when facing multi-field coupling effects, real-time requirements, and dynamic environmental changes, making it difficult to achieve high-precision, real-time slope stability monitoring and early warning. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for real-time monitoring and early warning of riverbank slope stability based on digital twins. This method addresses the problems of existing technologies failing to accurately describe the complex coupling effects between water, soil, and structure; offline analysis modes resulting in response lag and insufficient real-time performance; fixed threshold mechanisms lacking adaptability to dynamic environmental changes; and the lack of effective fusion of multi-source monitoring data and edge-cloud collaborative computing architecture. These issues make existing systems perform poorly under complex conditions, especially when facing multi-field coupling effects, real-time requirements, and dynamic environmental changes, making it difficult to achieve high-precision, real-time slope stability monitoring and early warning.

[0005] The technical solution of this invention is implemented as follows: In a first aspect, this invention provides a method for real-time monitoring and early warning of riverbank slope stability based on digital twins, comprising the following steps: By processing the geometric characteristic parameters, geological stratification data, and support structure parameters of the riverbank slope using a unified multiphysics equation model of water, soil, and structure, a coupled digital twin initial model is obtained. The on-site monitoring data is processed by a distributed sensor network acquisition system to obtain a multi-source heterogeneous monitoring dataset. Based on the multi-source heterogeneous monitoring dataset, the coupled digital twin initial model is processed by inversion analysis and state estimation algorithms to obtain an optimized digital twin model. By processing multi-source heterogeneous monitoring datasets through edge computing nodes, edge preprocessed data is obtained. The edge preprocessed data and digital twin optimization model are then input into the cloud computing cluster and processed through a multi-physics field coupled analytical algorithm to obtain the slope stability assessment result dataset. The slope stability assessment result dataset is processed by a multi-index fusion algorithm to obtain a comprehensive risk score. The comprehensive risk score is then processed based on a graded early warning threshold to obtain multi-level risk early warning information and corresponding engineering treatment plans.

[0006] Based on the above technical solutions, preferably, the step of processing the geometric characteristic parameters, geological stratification data, and support structure parameters of the riverbank slope through a unified multiphysics equation model of water-soil-structure to obtain a coupled digital twin initial model includes: By processing point cloud data of riverbank slope surface and borehole geological profile data using UAV oblique photogrammetry and ground laser scanning registration algorithm, a three-dimensional geometric solid model and layered structure model of riverbank slope are obtained. The three-dimensional geometric solid model and layered structural model of the river slope were meshed using a multiphysics coupling discretization algorithm, resulting in a coupled digital twin initial model including discrete elements of the water flow field, finite element elements of the soil stress field, and contact elements of the structure-soil interface.

[0007] Based on the above technical solutions, preferably, the step of processing the geometric characteristic parameters, geological stratification data, and support structure parameters of the riverbank slope through a unified multiphysics equation model of water-soil-structure to obtain a coupled digital twin initial model further includes: The point cloud data from UAV oblique photography and ground laser scanning were registered using an iterative nearest point registration algorithm. The sampling points of the borehole profile were then gridded using a Kriging interpolation algorithm to obtain gridded borehole data. The registered point cloud data and the gridded borehole data were then fused to obtain a three-dimensional geometric solid model of the riverbank slope. The three-dimensional geometric solid model of the river slope was partitioned using a finite element-finite volume hybrid mesh generation algorithm. Specifically, tetrahedral finite element elements were used for the soil domain, hexahedral finite volume elements were used for the water domain, and contact elements were used for the soil-structure interface, resulting in a coupled digital twin initial model.

[0008] Based on the above technical solutions, preferably, the step of processing on-site monitoring data through a distributed sensor network acquisition system to obtain a multi-source heterogeneous monitoring dataset, and then processing the coupled digital twin initial model through inversion analysis and state estimation algorithms based on the multi-source heterogeneous monitoring dataset to obtain an optimized digital twin model, includes: Real-time monitoring of riverbank slopes is achieved through a distributed multi-level sensor network, resulting in field monitoring data including GNSS displacement data, tilt deformation data, pore water pressure data, and soil-structure interface contact stress data. The field monitoring data is then processed based on a data quality control algorithm to obtain a standardized multi-source heterogeneous monitoring dataset. The multi-source heterogeneous monitoring dataset and the coupled digital twin initial model are processed by the Bayesian parameter inversion algorithm to obtain the posterior probability distribution of the model parameters. The model parameters are then updated based on the maximum a posteriori estimation criterion to obtain a digital twin optimized model that is synchronized with the actual slope condition.

[0009] Based on the above technical solutions, preferably, the step of processing on-site monitoring data through a distributed sensor network acquisition system to obtain a multi-source heterogeneous monitoring dataset, and then processing the coupled digital twin initial model through inversion analysis and state estimation algorithms based on the multi-source heterogeneous monitoring dataset to obtain an optimized digital twin model, further includes: The on-site monitoring data is denoised using wavelet denoising algorithm, and sensor fault data is identified using outlier detection algorithm. The missing data is interpolated using multi-sensor data fusion algorithm to obtain a complete and standardized multi-source heterogeneous monitoring dataset with time series. The Markov chain Monte Carlo sampling algorithm is used to extract samples from the posterior distribution of model parameters, and the convergence of the sampling is judged by a convergence diagnosis algorithm. Based on the sampling results, the expected values ​​of the model parameters are calculated as the updated values ​​of the model parameters.

[0010] Based on the above technical solutions, preferably, the step of processing multi-source heterogeneous monitoring datasets through edge computing nodes to obtain edge preprocessed data, inputting the edge preprocessed data and digital twin optimization model into a cloud computing cluster, and processing them through a multiphysics coupled analytical algorithm to obtain a slope stability assessment result dataset, including: The multi-source heterogeneous monitoring dataset is filtered, compressed, and feature extracted in real time using a data preprocessing algorithm deployed on edge computing nodes to obtain a set of key feature parameters after dimensionality reduction. A lightweight stability assessment model is then used to conduct a preliminary security assessment of the key feature parameter set at the edge, resulting in edge preprocessed data and a preliminary risk level. The edge preprocessed data and the digital twin optimization model are distributed to the cloud parallel computing cluster through a task scheduling algorithm. The multiphysics fully coupled finite element algorithm is used for numerical solution to obtain a dataset of slope stability assessment results including stress distribution, displacement field and safety factor.

[0011] Based on the above technical solutions, preferably, the step of processing the slope stability assessment result dataset through a multi-index fusion algorithm to obtain a comprehensive risk score, and processing the comprehensive risk score based on a graded early warning threshold to obtain multi-level risk early warning information and corresponding engineering response plans includes: The slope stability assessment result dataset is processed by a multi-level fuzzy comprehensive evaluation algorithm. The displacement rate index, stress state index and hydrological condition index are normalized and weighted to obtain a multi-dimensional comprehensive risk score including immediate risk value, trend risk value and comprehensive risk value. The multi-dimensional comprehensive risk score is graded by a dynamic threshold adjustment algorithm. The warning threshold is dynamically adjusted according to historical risk data and current environmental conditions to generate four levels of risk warning information, including green safety, yellow caution, orange warning, and red danger. The corresponding engineering disposal plan is matched based on the expert knowledge base.

[0012] Secondly, the present invention also provides a real-time monitoring and early warning system for riverbank slope stability based on digital twins, the system comprising: The initial model building module is used to process the geometric characteristic parameters, geological stratification data and support structure parameters of the river slope through the water-soil-structure multiphysics unified equation model to obtain a coupled digital twin initial model. The model inversion optimization module is used to process field monitoring data through a distributed sensor network acquisition system to obtain a multi-source heterogeneous monitoring dataset. Based on the multi-source heterogeneous monitoring dataset, the coupled digital twin initial model is processed through inversion analysis and state estimation algorithms to obtain an optimized digital twin model. The edge processing and parsing module is used to process the multi-source heterogeneous monitoring dataset through edge computing nodes to obtain edge preprocessed data. The edge preprocessed data and the digital twin optimization model are input into the cloud computing cluster and processed by the multi-physics field coupled analytical algorithm to obtain the slope stability assessment result dataset. The slope assessment and early warning module is used to process the slope stability assessment result dataset through a multi-index fusion algorithm to obtain a comprehensive risk score. Based on the graded early warning threshold, the comprehensive risk score is processed to obtain multi-level risk early warning information and corresponding engineering treatment plans.

[0013] Thirdly, the present invention also provides an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; The processor, memory, and communication interface communicate with each other through the bus. The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to implement the steps of a method for real-time monitoring and early warning of riverbank slope stability based on digital twins.

[0014] Fourthly, the present invention also provides a computer-readable storage medium storing computer instructions that enable a computer to implement steps such as those of a digital twin-based method for real-time monitoring and early warning of riverbank slope stability.

[0015] The method and system for real-time monitoring and early warning of riverbank slope stability based on digital twins of the present invention have the following advantages over the prior art: (1) By integrating multi-physics unified modeling, inversion analysis optimization, edge-cloud collaborative computing and dynamic early warning mechanism, the model parameters are dynamically updated based on inversion analysis and state estimation algorithm. The edge-cloud collaborative computing architecture is adopted, and multi-index fusion and dynamic threshold mechanism are introduced to realize the coupled modeling of water flow field, stress field and displacement field, ensuring that the digital twin model is highly synchronized with the actual slope state, and realizing high-precision real-time monitoring, dynamic risk assessment and graded early warning of river slope stability; (2) By using the multi-source point cloud registration and Kriging interpolation algorithm of UAV oblique photography and ground laser scanning, combined with the finite element-finite volume hybrid mesh discretization method and the water flow permeation-soil stress coupling equation set, a digital twin initial model that can accurately describe the multi-physics interaction of water-soil-structure was constructed, and the high-precision reconstruction of the three-dimensional geometric model of the river slope was realized. (3) The finite element-finite volume hybrid mesh generation algorithm realizes the accurate discretization of different physical domains. The soil domain adopts the tetrahedral finite element method, the water domain adopts the hexahedral finite volume method, and the interface adopts the contact element method. Combined with the water content-dependent nonlinear permeation equation and the soil stress balance equation considering the coupling of pore water pressure, a mathematical model that can accurately describe the water-soil interaction mechanism is established, which improves the numerical stability of multi-physics coupling calculation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1This is a flowchart of a method for real-time monitoring and early warning of riverbank slope stability based on digital twins according to the present invention. Figure 2 This is a structural diagram of a real-time monitoring and early warning system for riverbank slope stability based on digital twins, according to the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This invention provides a method for real-time monitoring and early warning of riverbank slope stability based on digital twins, comprising the following steps: By processing the geometric characteristic parameters, geological stratification data, and support structure parameters of the riverbank slope using the unified multiphysics equations model of water-soil-structure, a coupled digital twin initial model containing the flow field, stress field, and displacement field is obtained. The on-site monitoring data is processed by a distributed sensor network acquisition system to obtain a multi-source heterogeneous monitoring dataset. Based on the multi-source heterogeneous monitoring dataset, the coupled digital twin initial model is processed by inversion analysis and state estimation algorithms to obtain a digital twin optimized model with dynamically updated parameters. By processing multi-source heterogeneous monitoring datasets through edge computing nodes, noise-reduced and compressed edge preprocessed data is obtained. The edge preprocessed data and digital twin optimization model are input into the cloud computing cluster and processed by multi-physics field coupled analytical algorithm to obtain the slope stability assessment result dataset. The slope stability assessment result dataset is processed by a multi-index fusion algorithm to obtain a comprehensive risk score that includes displacement rate, stress state and hydrological conditions. The comprehensive risk score is then processed based on graded early warning thresholds to obtain multi-level risk early warning information and corresponding engineering treatment plans.

[0020] Specifically, this embodiment integrates multi-physics unified modeling, inversion analysis optimization, edge-cloud collaborative computing, and dynamic early warning mechanisms. Based on inversion analysis and state estimation algorithms, it dynamically updates model parameters, adopts an edge-cloud collaborative computing architecture, and introduces multi-index fusion and dynamic threshold mechanisms to achieve coupled modeling of flow field, stress field, and displacement field. This ensures that the digital twin model is highly synchronized with the actual slope state, enabling high-precision real-time monitoring, dynamic risk assessment, and graded early warning of river slope stability.

[0021] The process involves using a unified multiphysics equation model of water, soil, and structure to process the geometric characteristic parameters, geological stratification data, and support structure parameters of the riverbank slope, resulting in a coupled digital twin initial model, including: By processing point cloud data of riverbank slope surface and borehole geological profile data using UAV oblique photogrammetry and ground laser scanning registration algorithm, a three-dimensional geometric solid model and layered structure model of riverbank slope are obtained. The three-dimensional geometric solid model and layered structural model of the river slope were meshed using a multiphysics coupling discretization algorithm, resulting in a coupled digital twin initial model including discrete elements of the water flow field, finite element elements of the soil stress field, and contact elements of the structure-soil interface.

[0022] Specifically, this embodiment uses multi-source point cloud registration and Kriging interpolation algorithm through UAV oblique photography and ground laser scanning, combined with finite element-finite volume hybrid mesh discretization method and water flow permeation-soil stress coupling equation set, to construct a digital twin initial model that can accurately describe the multi-physics interaction of water-soil-structure, and realize high-precision reconstruction of the three-dimensional geometric model of riverbank slope.

[0023] The process of processing the geometric characteristic parameters, geological stratification data, and support structure parameters of the riverbank slope using a unified multiphysics equation model of water, soil, and structure to obtain a coupled digital twin initial model also includes: The point cloud data from UAV oblique photography and ground laser scanning were registered using an iterative nearest point registration algorithm. The sampling points of the borehole profile were then gridded using a Kriging interpolation algorithm to obtain gridded borehole data. The registered point cloud data and the gridded borehole data were then fused to obtain a three-dimensional geometric solid model of the riverbank slope. The three-dimensional geometric solid model of the river slope is partitioned using a finite element-finite volume hybrid mesh generation algorithm. Specifically, tetrahedral finite element elements are used for the soil domain, hexahedral finite volume elements are used for the water domain, and contact elements are used for the soil-structure interface. This achieves multi-physics coupling discretization and yields the initial coupled digital twin model.

[0024] In one specific embodiment, the coupled digital twin initial model includes a water flow permeability equation and a land stress balance equation, wherein the water flow permeability equation is: ; ; in, For soil moisture content, For hydraulic head, Since water content depends on the permeability coefficient, For spatiotemporally related water source collection, For spatial gradient operators, For time variables, The saturated permeability coefficient, This is the penetration index; The land stress balance equation is: ; in, For spatial gradient operators, It is a displacement vector. For the land stiffness tensor, Pore ​​water pressure, It has a dual porosity coefficient. For land density, This is the acceleration due to gravity.

[0025] In one specific embodiment, for a large riverbank slope project (slope height 50 meters, slope length 200 meters), traditional modeling methods suffer from insufficient multiphysics coupling accuracy due to a single data source and fixed mesh type. This embodiment provides a solution, the steps of which are as follows: Accurate registration and fusion of multi-source data: High-precision point cloud data (accuracy ±2cm) of the slope surface is obtained by using UAV oblique photography, and local detailed point cloud data (accuracy ±5mm) is obtained by combining it with ground laser scanning. The two point clouds are registered at the millimeter level through an iterative nearest point registration algorithm, and the registration error is controlled within ±3mm. At the same time, spatial interpolation is performed on 15 borehole sampling points deployed along the slope using a three-dimensional kriging interpolation algorithm to construct a continuous geological stratification model. The interpolation accuracy is improved by more than 40% compared with traditional two-dimensional interpolation.

[0026] Intelligent generation of hybrid meshes: The finite element-finite volume hybrid mesh generation algorithm is adopted, and the meshes are partitioned according to the characteristics of different physical domains: the soil domain adopts tetrahedral finite element elements (approximately 1.2 million elements) to adapt to complex geometry, the water domain adopts hexahedral finite volume elements (approximately 800,000 elements) to ensure mass conservation, and the soil-structure interface adopts specialized contact elements (approximately 150,000 elements). This achieves seamless connection of different mesh types, improves the mesh quality index by 25%, and increases the computational efficiency by 30%.

[0027] The coupled equation precisely describes the established water content-dependent nonlinear permeability equation. It can accurately describe the changes in the permeability characteristics of unsaturated soil, combined with the soil stress balance equation considering the coupling effect of pore water pressure. This study provides a mathematical description of the water-soil bidirectional coupling mechanism, which improves the computational accuracy by 45% compared to the traditional decoupling method and effectively solves the accuracy loss problem caused by linearization in the traditional method.

[0028] The process involves acquiring field monitoring data through a distributed sensor network system to obtain a multi-source heterogeneous monitoring dataset. Based on this dataset, an optimized digital twin model is obtained by processing the coupled initial digital twin model using inversion analysis and state estimation algorithms. This includes: Real-time monitoring of riverbank slopes is achieved through a distributed multi-level sensor network, resulting in field monitoring data including GNSS displacement data, tilt deformation data, pore water pressure data, and soil-structure interface contact stress data. The field monitoring data is then processed based on a data quality control algorithm to obtain a standardized multi-source heterogeneous monitoring dataset. The multi-source heterogeneous monitoring dataset and the coupled digital twin initial model are processed by the Bayesian parameter inversion algorithm to obtain the posterior probability distribution of the model parameters. The model parameters are then updated based on the maximum a posteriori estimation criterion to obtain a digital twin optimized model that is synchronized with the actual slope condition.

[0029] Specifically, this embodiment achieves accurate discretization of different physical domains through a finite element-finite volume hybrid mesh generation algorithm. The soil domain adopts a tetrahedral finite element method, the water domain adopts a hexahedral finite volume method, and the interface adopts a contact element method. Combined with the water content-dependent nonlinear permeability equation and the soil stress balance equation considering the coupling of pore water pressure, a mathematical model that can accurately describe the water-soil interaction mechanism is established, which improves the numerical stability of multi-physics coupling calculation.

[0030] The process of acquiring field monitoring data through a distributed sensor network system to obtain a multi-source heterogeneous monitoring dataset, and then using inversion analysis and state estimation algorithms to process the coupled digital twin initial model to obtain an optimized digital twin model, further includes: The on-site monitoring data is denoised using wavelet denoising algorithm, and sensor fault data is identified using outlier detection algorithm. The missing data is interpolated using multi-sensor data fusion algorithm to obtain a complete and standardized multi-source heterogeneous monitoring dataset with time series. The Markov chain Monte Carlo sampling algorithm is used to extract samples from the posterior distribution of model parameters, and the convergence of the sampling is judged by a convergence diagnosis algorithm. Based on the sampling results, the expected value of the model parameters is calculated as the updated value of the model parameters, thereby realizing the dynamic calibration of the digital twin model.

[0031] In one specific embodiment, the calculation formula of the Bayesian parameter inversion algorithm is: ; in, For the posterior distribution of the parameters, Let be the likelihood function. For the prior distribution of parameters, For evidence function, The parameter vector to be inverted, For observation data vectors; The weight allocation calculation formula for the multi-sensor data fusion is as follows: ; ; in, For the first i The fusion weights of individual sensors, For the first i The measurement standard deviation of each sensor, For the first i The measurement values ​​of each sensor, This refers to the merged data values.

[0032] In one specific embodiment, for a riverbank slope monitoring project (deploying 60 sensors, including 20 GNSS displacement sensors, 15 inclinometers, 15 pore water pressure gauges, and 10 stress gauges), traditional data processing methods face problems such as severe noise interference, frequent sensor failures, and delayed parameter updates. This embodiment provides an intelligent data quality control and dynamic model calibration solution, with the following steps: Intelligent data quality control: A 5-level wavelet decomposition based on the db4 wavelet basis is used to denoise the field monitoring data. A soft threshold denoising algorithm improves the signal-to-noise ratio from 12dB to 28dB, representing a 133% improvement in noise reduction. Simultaneously, an improved outlier detection algorithm based on the 3σ criterion, combined with sliding window statistical features, is used to automatically identify sensor fault data, achieving a fault detection accuracy of 96.5%. To address the issue of missing data, a weighted data fusion algorithm based on measurement accuracy is employed, considering the historical accuracy of each sensor. Dynamic weight allocation This enables the priority use of high-precision sensor data, and the fusion interpolation accuracy is improved by 42% compared with the traditional mean interpolation method.

[0033] Efficient parameter inversion and model calibration: Bayesian parameter inversion is performed using the Adaptive Markov Chain Monte Carlo (AMCMC) sampling algorithm, and the Gelman-Rubin convergence diagnostic criterion is applied. Determine the sampling convergence and ensure the posterior distribution of the parameters. The AMCMC algorithm provides reliable estimation; compared with the traditional fixed step size MCMC method, the convergence speed is improved by 60% and the parameter estimation accuracy is improved by 35%; at the same time, a sliding window parameter update mechanism is established, and the expected value of the model parameters is recalculated every 4 hours based on the latest observation data, realizing the dynamic calibration of the digital twin model, and the model prediction accuracy is improved by 52% compared with the static parameter model.

[0034] Algorithm Collaborative Optimization: Through a three-level data quality control process of wavelet denoising, anomaly detection, and data fusion, combined with a closed-loop feedback mechanism of adaptive MCMC parameter inversion, a complete technical chain of "data quality control → dynamic parameter update → model accuracy improvement" was constructed, achieving dual assurance of monitoring data quality and model reliability, and providing a high-quality digital foundation for accurate assessment of slope stability.

[0035] The process involves processing multi-source heterogeneous monitoring datasets through edge computing nodes to obtain edge preprocessed data. This edge preprocessed data and the digital twin optimization model are then input into a cloud computing cluster and processed using a multiphysics coupled analytical algorithm to obtain a slope stability assessment result dataset, including: The multi-source heterogeneous monitoring dataset is filtered, compressed, and feature extracted in real time using a data preprocessing algorithm deployed on edge computing nodes to obtain a set of key feature parameters after dimensionality reduction. A lightweight stability assessment model is then used to conduct a preliminary security assessment of the key feature parameter set at the edge, resulting in edge preprocessed data and a preliminary risk level. The edge preprocessed data and the digital twin optimization model are distributed to the cloud parallel computing cluster through a task scheduling algorithm. The multiphysics fully coupled finite element algorithm is used for numerical solution to obtain a dataset of slope stability assessment results including stress distribution, displacement field and safety factor.

[0036] Specifically, this embodiment achieves real-time acquisition of multi-dimensional monitoring data such as GNSS displacement, tilt deformation, pore water pressure, and interface contact stress through a distributed multi-level sensor network. Combined with a data quality control algorithm, it ensures the standardized processing of multi-source heterogeneous data. Furthermore, it employs a Bayesian parameter inversion algorithm to achieve dynamic updating and optimization of digital twin model parameters, thereby improving the synchronization accuracy and dynamic adaptability of the digital twin model with the actual slope condition.

[0037] The process involves processing the slope stability assessment result dataset using a multi-index fusion algorithm to obtain a comprehensive risk score. This comprehensive risk score is then processed based on tiered early warning thresholds to obtain multi-level risk warning information and corresponding engineering response plans, including: The slope stability assessment result dataset is processed by a multi-level fuzzy comprehensive evaluation algorithm. The displacement rate index, stress state index and hydrological condition index are normalized and weighted to obtain a multi-dimensional comprehensive risk score including immediate risk value, trend risk value and comprehensive risk value. The multi-dimensional comprehensive risk score is graded by a dynamic threshold adjustment algorithm. The warning threshold is dynamically adjusted according to historical risk data and current environmental conditions to generate four levels of risk warning information, including green safety, yellow caution, orange warning, and red danger. The corresponding engineering disposal plan is matched based on the expert knowledge base.

[0038] Specifically, this embodiment uses a multi-level fuzzy comprehensive evaluation algorithm to integrate multiple indicators such as displacement rate, stress state, and hydrological conditions, and constructs a multi-dimensional scoring system that includes immediate risk, trend risk, and comprehensive risk. It also uses a dynamic threshold adjustment algorithm combined with historical risk data and environmental conditions to achieve adaptive updating of the warning threshold, and establishes a four-level warning mechanism of green safety, yellow attention, orange warning, and red danger, thereby improving the accuracy of slope stability risk assessment.

[0039] The process of processing the slope stability assessment result dataset using a multi-index fusion algorithm to obtain a comprehensive risk score, and then processing the comprehensive risk score based on a graded early warning threshold to obtain multi-level risk early warning information and corresponding engineering response plans, also includes: The weight coefficients of displacement rate index, stress state index and hydrological condition index are calculated by the analytic hierarchy process. The trapezoidal fuzzy membership function is used to fuzzify each index. The weighted fuzzy operator is used to fuse multiple indices to obtain a comprehensive risk score that reflects the overall stability of the slope. By using time series analysis algorithms to predict trends in historical risk data, and combining current meteorological conditions and slope status with an adaptive threshold update algorithm to dynamically adjust the warning threshold, and by retrieving matching treatment plans from the engineering treatment knowledge base, targeted engineering treatment plans are obtained.

[0040] Specifically, this embodiment achieves precise allocation of weight coefficients for displacement rate, stress state, and hydrological conditions through the analytic hierarchy process (AHP), and realizes accurate fusion calculation of multiple indicators by using trapezoidal fuzzy membership functions and weighted fuzzy operators. Furthermore, it combines time series analysis algorithms to predict trends in historical risk data, constructs an adaptive threshold update mechanism and an engineering disposal knowledge base matching system, thereby improving the scientific nature of the comprehensive risk score and the intelligence level of early warning threshold adjustment.

[0041] Please see Figure 2 The present invention also provides a real-time monitoring and early warning system for riverbank slope stability based on digital twins, the system comprising: The initial model building module is used to process the geometric characteristic parameters, geological stratification data and support structure parameters of the river slope through the water-soil-structure multiphysics unified equation model to obtain a coupled digital twin initial model. The model inversion optimization module is used to process field monitoring data through a distributed sensor network acquisition system to obtain a multi-source heterogeneous monitoring dataset. Based on the multi-source heterogeneous monitoring dataset, the coupled digital twin initial model is processed through inversion analysis and state estimation algorithms to obtain an optimized digital twin model. The edge processing and parsing module is used to process the multi-source heterogeneous monitoring dataset through edge computing nodes to obtain edge preprocessed data. The edge preprocessed data and the digital twin optimization model are input into the cloud computing cluster and processed by the multi-physics field coupled analytical algorithm to obtain the slope stability assessment result dataset. The slope assessment and early warning module is used to process the slope stability assessment result dataset through a multi-index fusion algorithm to obtain a comprehensive risk score. Based on the graded early warning threshold, the comprehensive risk score is processed to obtain multi-level risk early warning information and corresponding engineering treatment plans.

[0042] Specifically, the riverbank slope stability real-time monitoring and early warning system based on digital twins in this embodiment realizes the fully automated processing of the entire process from digital twin model construction, multi-source data inversion and update, edge-cloud collaborative computing to comprehensive risk early warning by constructing an initial model construction module, a model inversion and optimization module, an edge processing and analysis module, and a slope assessment and early warning module, thus forming a complete riverbank slope stability real-time monitoring and early warning system.

[0043] The present invention also discloses an electronic device, comprising: at least one processor, at least one memory communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to implement a method for real-time monitoring and early warning of riverbank slope stability based on digital twin.

[0044] This invention also discloses a computer-readable storage medium storing computer instructions that enable the computer to implement all or part of the steps of the real-time monitoring and early warning method for riverbank slope stability based on digital twins as described in this embodiment of the invention. The storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time monitoring and early warning of riverbank slope stability based on digital twins, characterized in that, Includes the following steps: By processing the geometric characteristic parameters, geological stratification data, and support structure parameters of the riverbank slope using a unified multiphysics equation model of water, soil, and structure, a coupled digital twin initial model is obtained. The process involves using a unified multiphysics equation model of water, soil, and structure to process the geometric characteristic parameters, geological stratification data, and support structure parameters of the riverbank slope, resulting in a coupled digital twin initial model, including: By processing point cloud data of riverbank slope surface and borehole geological profile data using UAV oblique photogrammetry and ground laser scanning registration algorithm, a three-dimensional geometric solid model and layered structure model of riverbank slope are obtained. The three-dimensional geometric solid model and layered structural model of the river slope are meshed using a multiphysics coupling discretization algorithm to obtain a coupled digital twin initial model including discrete elements of water flow field, finite element elements of soil stress field and structure-soil interface contact elements. The point cloud data from UAV oblique photography and ground laser scanning were registered using an iterative nearest point registration algorithm. The sampling points of the borehole profile were then gridded using a Kriging interpolation algorithm to obtain gridded borehole data. The registered point cloud data and the gridded borehole data were then fused to obtain a three-dimensional geometric solid model of the riverbank slope. The three-dimensional geometric solid model of the river slope was partitioned using a finite element-finite volume hybrid mesh generation algorithm. Specifically, tetrahedral finite element elements were used for the soil domain, hexahedral finite volume elements were used for the water domain, and contact elements were used for the soil-structure interface, resulting in a coupled digital twin initial model. The on-site monitoring data is processed by a distributed sensor network acquisition system to obtain a multi-source heterogeneous monitoring dataset. Based on the multi-source heterogeneous monitoring dataset, the coupled digital twin initial model is processed by inversion analysis and state estimation algorithms to obtain an optimized digital twin model. By processing multi-source heterogeneous monitoring datasets through edge computing nodes, edge preprocessed data is obtained. The edge preprocessed data and digital twin optimization model are then input into the cloud computing cluster and processed through a multi-physics field coupled analytical algorithm to obtain the slope stability assessment result dataset. The slope stability assessment result dataset is processed by a multi-index fusion algorithm to obtain a comprehensive risk score. The comprehensive risk score is then processed based on the graded early warning threshold to obtain multi-level risk early warning information and corresponding engineering treatment plans. The process involves processing the slope stability assessment result dataset using a multi-index fusion algorithm to obtain a comprehensive risk score. This comprehensive risk score is then processed based on tiered early warning thresholds to obtain multi-level risk warning information and corresponding engineering response plans, including: The slope stability assessment result dataset is processed by a multi-level fuzzy comprehensive evaluation algorithm. The displacement rate index, stress state index and hydrological condition index are normalized and weighted to obtain a multi-dimensional comprehensive risk score including immediate risk value, trend risk value and comprehensive risk value. The multi-dimensional comprehensive risk score is graded by a dynamic threshold adjustment algorithm. The warning threshold is dynamically adjusted according to historical risk data and current environmental conditions to generate four levels of risk warning information, including green safety, yellow caution, orange warning, and red danger. The corresponding engineering disposal plan is matched based on the expert knowledge base.

2. The method for real-time monitoring and early warning of riverbank slope stability based on digital twins as described in claim 1, characterized in that, The process involves acquiring field monitoring data through a distributed sensor network system to obtain a multi-source heterogeneous monitoring dataset. Based on this dataset, an optimized digital twin model is obtained by processing the coupled initial digital twin model using inversion analysis and state estimation algorithms. This includes: Real-time monitoring of riverbank slopes is achieved through a distributed multi-level sensor network, resulting in field monitoring data including GNSS displacement data, tilt deformation data, pore water pressure data, and soil-structure interface contact stress data. The field monitoring data is then processed based on a data quality control algorithm to obtain a standardized multi-source heterogeneous monitoring dataset. The multi-source heterogeneous monitoring dataset and the coupled digital twin initial model are processed by the Bayesian parameter inversion algorithm to obtain the posterior probability distribution of the model parameters. The model parameters are then updated based on the maximum a posteriori estimation criterion to obtain a digital twin optimized model that is synchronized with the actual slope condition.

3. The method for real-time monitoring and early warning of riverbank slope stability based on digital twins as described in claim 2, characterized in that, The process of acquiring field monitoring data through a distributed sensor network system to obtain a multi-source heterogeneous monitoring dataset, and then using inversion analysis and state estimation algorithms to process the coupled digital twin initial model to obtain an optimized digital twin model, further includes: The on-site monitoring data is denoised using wavelet denoising algorithm, and sensor fault data is identified using outlier detection algorithm. The missing data is interpolated using multi-sensor data fusion algorithm to obtain a complete and standardized multi-source heterogeneous monitoring dataset with time series. The Markov chain Monte Carlo sampling algorithm is used to extract samples from the posterior distribution of model parameters, and the convergence of the sampling is judged by a convergence diagnosis algorithm. Based on the sampling results, the expected values ​​of the model parameters are calculated as the updated values ​​of the model parameters.

4. The method for real-time monitoring and early warning of riverbank slope stability based on digital twins as described in claim 1, characterized in that, The process involves processing multi-source heterogeneous monitoring datasets through edge computing nodes to obtain edge preprocessed data. This edge preprocessed data and the digital twin optimization model are then input into a cloud computing cluster and processed using a multiphysics coupled analytical algorithm to obtain a slope stability assessment result dataset, including: The multi-source heterogeneous monitoring dataset is filtered, compressed, and feature extracted in real time using a data preprocessing algorithm deployed on edge computing nodes to obtain a set of key feature parameters after dimensionality reduction. A lightweight stability assessment model is then used to conduct a preliminary security assessment of the key feature parameter set at the edge, resulting in edge preprocessed data and a preliminary risk level. The edge preprocessed data and the digital twin optimization model are distributed to the cloud parallel computing cluster through a task scheduling algorithm. The multiphysics fully coupled finite element algorithm is used for numerical solution to obtain a dataset of slope stability assessment results including stress distribution, displacement field and safety factor.

5. A real-time monitoring and early warning system for riverbank slope stability based on digital twins, used to execute the real-time monitoring and early warning method for riverbank slope stability based on digital twins as described in any one of claims 1-4, characterized in that, The system includes: The initial model building module is used to process the geometric characteristic parameters, geological stratification data and support structure parameters of the river slope through the water-soil-structure multiphysics unified equation model to obtain a coupled digital twin initial model. The model inversion optimization module is used to process field monitoring data through a distributed sensor network acquisition system to obtain a multi-source heterogeneous monitoring dataset. Based on the multi-source heterogeneous monitoring dataset, the coupled digital twin initial model is processed through inversion analysis and state estimation algorithms to obtain an optimized digital twin model. The edge processing and parsing module is used to process the multi-source heterogeneous monitoring dataset through edge computing nodes to obtain edge preprocessed data. The edge preprocessed data and the digital twin optimization model are input into the cloud computing cluster and processed by the multi-physics field coupled analytical algorithm to obtain the slope stability assessment result dataset. The slope assessment and early warning module is used to process the slope stability assessment result dataset through a multi-index fusion algorithm to obtain a comprehensive risk score. Based on the graded early warning threshold, the comprehensive risk score is processed to obtain multi-level risk early warning information and corresponding engineering treatment plans.

6. An electronic device, characterized in that, include: At least one processor, at least one memory, a communication interface, and a bus; The processor, memory, and communication interface communicate with each other through the bus. The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to implement the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to perform the method as described in any one of claims 1 to 4.

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