River slope stability real-time monitoring and early warning method and system based on digital twinning

Abstract

The invention relates to the technical field of digital twinning, and provides a digital twinning-based river slope stability real-time monitoring and early warning method and system, and the method comprises the steps: constructing a coupled digital twinning initial model; based on the multi-source heterogeneous monitoring data set, processing the coupled digital twinborn initial model through an inversion analysis and state estimation algorithm to obtain a digital twinborn optimization model; processing the multi-source heterogeneous monitoring data set through an edge computing node to obtain edge preprocessing data, inputting the edge preprocessing data and a digital twin optimization model into a cloud computing cluster, and processing through a multi-physics field coupling analysis algorithm to obtain a slope stability evaluation result data set; and processing the slope stability assessment result data set 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 an engineering disposal scheme. According to the invention, high-precision real-time monitoring, dynamic risk assessment and graded early warning of the stability of the river slope are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning, in particular to a river channel slope stability real-time monitoring and early warning method and system based on digital twinning. BACKGROUND

[0002] River channel slope stability monitoring and early warning is an important part of water conservancy project safety management, aiming to identify potential instability risks and issue early warning information in a timely manner through real-time monitoring of slope deformation, stress and environmental state. With the development of digital technology, digital twinning technology provides a new solution for river channel slope stability monitoring, which can build a virtual mapping model of physical slope to realize real-time perception, dynamic analysis and intelligent early warning of slope state. However, the river channel slope system involves complex coupling of multiple physical fields such as water infiltration, soil deformation and structure response, and its stability is influenced by multiple factors such as geological conditions, hydrological environment and meteorological factors, with high nonlinearity and time-varying characteristics.

[0003] In the prior art, river channel slope stability monitoring and early warning mainly adopts single physical field modeling method, offline batch processing analysis mode and fixed threshold early warning mechanism, realizing basic monitoring data collection and analysis functions. However, single physical field modeling cannot accurately describe the complex coupling among water, soil and structure, offline analysis mode leads to response lag and insufficient real-time performance, fixed threshold mechanism lacks adaptability to dynamic environmental changes, and lacks effective fusion of multi-source monitoring data and edge-cloud collaborative computing architecture, which makes the existing system perform poorly under complex working conditions, especially in the face of multi-field coupling, real-time requirements and dynamic environmental changes, and it is difficult to realize high-precision and real-time slope stability monitoring and early warning. SUMMARY

[0004] Therefore, the present application provides a river channel slope stability real-time monitoring and early warning method and system based on digital twinning, which solves the problem that the prior art cannot accurately describe the complex coupling among water, soil and structure, the offline analysis mode leads to response lag and insufficient real-time performance, the fixed threshold mechanism lacks adaptability to dynamic environmental changes, and lacks effective fusion of multi-source monitoring data and edge-cloud collaborative computing architecture, which makes the existing system perform poorly under complex working conditions, especially in the face of multi-field coupling, real-time requirements and dynamic environmental changes, and it is difficult to realize high-precision and real-time slope stability monitoring and early warning.

[0005] The technical scheme of the present application is as follows: In a first aspect, the present application provides a river channel slope stability real-time monitoring and early warning method based on digital twinning, comprising the following steps: The geometric characteristic parameters, geological layering data and supporting structure parameters of the river channel slope are processed by a water-soil-structure multi-physical field unified equation group model to obtain a coupled digital twin initial model; The field monitoring data is processed by a distributed sensing network acquisition system to obtain a multi-source heterogeneous monitoring data set, and the coupled digital twin initial model is processed by inversion analysis and state estimation algorithm based on the multi-source heterogeneous monitoring data set to obtain a digital twin optimized model; The multi-source heterogeneous monitoring data set is processed by an edge computing node to obtain edge pre-processing data, and the edge pre-processing data and the digital twin optimized model are input into a cloud computing cluster, which is processed by a multi-physical field coupling analysis algorithm to obtain a slope stability evaluation result data set; The slope stability evaluation result data set is processed by a multi-index fusion algorithm to obtain a comprehensive risk score, and the comprehensive risk score is processed based on a hierarchical early warning threshold to obtain multi-level risk early warning information and corresponding engineering disposal scheme.

[0006] On the basis of the above technical scheme, preferably, the water-soil-structure multi-physical field unified equation group model is used to process the geometric characteristic parameters, geological layering data and supporting structure parameters of the river channel slope to obtain a coupled digital twin initial model, which includes: The river channel slope surface point cloud data and the borehole geological profile data are processed by an unmanned aerial vehicle oblique photogrammetry and ground laser scanning registration algorithm to obtain a river channel slope three-dimensional geometric entity model and a layered structure model; The river channel slope three-dimensional geometric entity model and the layered structure model are meshed by a multi-physical field coupling discretization algorithm to obtain a coupled digital twin initial model including water flow field discrete units, soil stress field finite element units and structure-soil interface contact units.

[0007] On the basis of the above technical scheme, preferably, the water-soil-structure multi-physical field unified equation group model is used to process the geometric characteristic parameters, geological layering data and supporting structure parameters of the river channel slope to obtain a coupled digital twin initial model, which further includes: The unmanned aerial vehicle oblique photogrammetry point cloud data and the ground laser scanning point cloud data are registered by an iterative closest point registration algorithm, and the borehole profile sampling points are meshed by a Kriging interpolation algorithm to obtain meshed borehole data, and the registered point cloud data and the meshed borehole data are fused to obtain a river channel slope three-dimensional geometric entity model; The river channel slope three-dimensional geometric entity model is partitioned by a finite element-finite volume hybrid mesh generation algorithm, wherein the soil domain is divided into tetrahedral finite element units, the water flow domain is divided into hexahedral finite volume units, and the soil-structure interface is divided into contact units to obtain a coupled digital twin initial model.

[0008] On the basis of the above technical scheme, preferably, the field monitoring data is processed by the distributed sensing network acquisition system to obtain a multi-source heterogeneous monitoring data set, and based on the multi-source heterogeneous monitoring data set, the coupled digital twin initial model is processed by inversion analysis and state estimation algorithm to obtain a digital twin optimization model, including: The river channel slope is monitored in real time by a distributed multi-level sensor network to obtain field monitoring data including GNSS displacement data, inclination deformation data, pore water pressure data and soil-structure interface contact stress data, and the field monitoring data is processed based on a data quality control algorithm to obtain a standardized multi-source heterogeneous monitoring data set; The multi-source heterogeneous monitoring data set and the coupled digital twin initial model are processed by a Bayesian parameter inversion algorithm to obtain a posterior probability distribution of model parameters, and the model parameters are updated based on a maximum posterior estimation criterion to obtain a digital twin optimization model synchronized with the actual slope state.

[0009] On the basis of the above technical scheme, preferably, the field monitoring data is processed by the distributed sensing network acquisition system to obtain a multi-source heterogeneous monitoring data set, and based on the multi-source heterogeneous monitoring data set, the coupled digital twin initial model is processed by inversion analysis and state estimation algorithm to obtain a digital twin optimization model, including: The field monitoring data is denoised by a wavelet denoising algorithm, and sensor fault data is identified by an outlier detection algorithm, and missing data is interpolated by a multi-sensor data fusion algorithm to obtain a time series complete standardized multi-source heterogeneous monitoring data set; Samples are extracted from the model parameter posterior distribution by a Markov chain Monte Carlo sampling algorithm, and sampling convergence is determined by a convergence diagnostic algorithm, and the expected value of the model parameters is calculated based on the sampling results as the updated value of the model parameters.

[0010] On the basis of the above technical scheme, preferably, the multi-source heterogeneous monitoring data set is processed by an edge computing node to obtain edge pre-processing data, and the edge pre-processing data and the digital twin optimization model are input into a cloud computing cluster to be processed by a multi-physical field coupling analysis algorithm to obtain a slope stability evaluation result data set, including: The multi-source heterogeneous monitoring data set is filtered, compressed and feature extracted in real time by a data preprocessing algorithm deployed on the edge computing node to obtain a reduced key feature parameter set, and the key feature parameter set is preliminarily safety evaluated on the edge by a lightweight stability evaluation model to obtain edge pre-processing data and a preliminary risk level; The edge pre-processing data and the digital twin optimization model are distributed to a cloud parallel computing cluster through a task scheduling algorithm, a multi-physical field fully coupled finite element algorithm is used for numerical solution, and a slope stability evaluation result data set including stress distribution, displacement field and safety coefficient is obtained.

[0011] On the basis of the above technical solutions, preferably, the slope stability evaluation result data set is processed through a multi-index fusion algorithm to obtain a comprehensive risk score, the comprehensive risk score is processed based on a hierarchical early warning threshold to obtain multi-level risk early warning information and a corresponding engineering disposal scheme, including: The slope stability evaluation result data set is processed through a multi-level fuzzy comprehensive evaluation algorithm, displacement rate indexes, stress state indexes and hydrological condition indexes are normalized and weighted, and a multi-dimensional comprehensive risk score including an instant risk value, a trend risk value and a comprehensive risk value is obtained. The multi-dimensional comprehensive risk score is processed through a dynamic threshold adjustment algorithm, the early warning threshold is dynamically adjusted according to historical risk data and current environmental conditions, four-level risk early warning information including green safety, yellow attention, orange early warning and red danger is generated, and a corresponding engineering disposal scheme is matched based on an expert knowledge base.

[0012] In a second aspect, the application further provides a river channel slope stability real-time monitoring and early warning system based on digital twin, the system comprising: An initial model construction module is configured to process geometric characteristic parameters, geological layering data and supporting structure parameters of a river channel slope through a water-soil-structure multi-physical field unified equation group model to obtain a coupled digital twin initial model. A model inversion optimization module is configured to process field monitoring data through a distributed sensing network acquisition system to obtain a multi-source heterogeneous monitoring data set, process the coupled digital twin initial model through inversion analysis and state estimation algorithm based on the multi-source heterogeneous monitoring data set, and obtain a digital twin optimization model. An edge processing analysis module is configured to process the multi-source heterogeneous monitoring data set through an edge computing node to obtain edge pre-processing data, input the edge pre-processing data and the digital twin optimization model into a cloud computing cluster, and process through a multi-physical field coupling analysis algorithm to obtain a slope stability evaluation result data set. A slope evaluation and early warning module is configured to process the slope stability evaluation result data set through a multi-index fusion algorithm to obtain a comprehensive risk score, process the comprehensive risk score based on a hierarchical early warning threshold to obtain multi-level risk early warning information and a corresponding engineering disposal scheme.

[0013] In a third aspect, the application further provides an electronic device, comprising at least one processor, at least one memory, a communication interface and a bus. The processor, the memory and the communication interface can communicate with each other through the bus, the memory stores program instructions executable by the processor, and the processor calls the program instructions to implement the steps of the river channel slope stability real-time monitoring and early warning method based on digital twinning.

[0014] In a fourth aspect, the present application further provides a computer readable storage medium storing computer instructions, which make a computer implement the steps of the river channel slope stability real-time monitoring and early warning method based on digital twinning.

[0015] The river channel slope stability real-time monitoring and early warning method and system based on digital twinning have the following beneficial effects over the prior art: (1) By integrating multi-physical field unified modeling, inversion analysis optimization, edge-cloud collaborative computing and dynamic early warning mechanism, dynamically updating model parameters based on inversion analysis and state estimation algorithm, using edge-cloud collaborative computing architecture, introducing multi-index fusion and dynamic threshold mechanism, coupling modeling of water flow field, stress field and displacement field is realized, ensuring that the digital twinning model is highly synchronized with the actual slope state, and high-precision real-time monitoring, dynamic risk assessment and grading warning of river channel slope stability are realized; (2) By multi-source point cloud registration of unmanned aerial vehicle oblique photography and ground laser scanning and Kriging interpolation algorithm, combined with finite element-finite volume hybrid grid discretization method and water penetration-soil stress coupling equation set, an initial digital twinning model capable of accurately describing the interaction of water-soil-structure multi-physical field is constructed, and high-precision reconstruction of the river channel slope three-dimensional geometric model is realized; (3) Precise discretization of different physical domains is realized by a finite element-finite volume hybrid grid generation algorithm, and a partition processing strategy of tetrahedral finite element for soil domain, hexahedral finite volume for water flow domain and contact element for interface is adopted, combined with a nonlinear permeation equation dependent on water content and a soil stress balance equation considering pore water pressure coupling, a mathematical model capable of accurately describing the water-soil interaction mechanism is established, and the numerical stability of multi-physical field coupling calculation is improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1A flow chart of a river channel slope stability real-time monitoring and early warning method based on digital twinning according to the present application; Figure 2 A structure diagram of a river channel slope stability real-time monitoring and early warning system based on digital twinning according to the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0019] Please refer to Figure 1 The present application provides a river channel slope stability real-time monitoring and early warning method based on digital twinning, comprising the following steps: The geometric characteristic parameters, geological stratification data and supporting structure parameters of the river channel slope are processed by a water-soil-structure multi-physical field unified equation set model to obtain a coupled digital twinning initial model containing a flow field, a stress field and a displacement field; The field monitoring data are processed by a distributed sensing network acquisition system to obtain a multi-source heterogeneous monitoring data set. Based on the multi-source heterogeneous monitoring data set, the coupled digital twinning initial model is processed by inversion analysis and state estimation algorithm to obtain a digital twinning optimization model with dynamically updated parameters; The multi-source heterogeneous monitoring data set is processed by an edge computing node to obtain edge preprocessed data after noise reduction and compression. The edge preprocessed data and the digital twinning optimization model are input into a cloud computing cluster, and the edge slope stability evaluation result data set is obtained by processing through a multi-physical field coupling analysis algorithm. The edge slope stability evaluation result data set is processed by a multi-index fusion algorithm to obtain a comprehensive risk score containing a displacement rate, a stress state and hydrological conditions. The comprehensive risk score is processed based on a hierarchical early warning threshold to obtain multi-level risk early warning information and corresponding engineering disposal schemes.

[0020] Specifically, the present embodiment integrates multi-physical field unified modeling, inversion analysis optimization, edge-cloud collaborative computing and dynamic early warning mechanism, dynamically updates model parameters based on inversion analysis and state estimation algorithm, adopts edge-cloud collaborative computing architecture, introduces multi-index fusion and dynamic threshold mechanism, realizes coupled modeling of flow field, stress field and displacement field, ensures high synchronization of the digital twinning model and the actual slope state, and realizes high-precision real-time monitoring, dynamic risk assessment and hierarchical early warning of the river channel slope stability.

[0021] The geometric characteristic parameters, geological stratification data and supporting structure parameters of the river channel slope are processed by the water-soil-structure multi-physical field unified equation group model to obtain a coupled digital twin initial model, including: The river channel slope three-dimensional geometric entity model and the layered structure model are obtained by processing the river channel slope surface point cloud data and the borehole geological profile data through the unmanned aerial vehicle oblique photogrammetry and ground laser scanning registration algorithm. The river channel slope three-dimensional geometric entity model and the layered structure model are meshed by the multi-physical field coupling discretization algorithm to obtain the coupled digital twin initial model including the flow field discrete unit, the soil stress field finite element unit and the structure-soil interface contact unit.

[0022] Specifically, the embodiment constructs a digital twin initial model capable of accurately describing the interaction of water-soil-structure multi-physical field by using the multi-source point cloud registration of unmanned aerial vehicle oblique photogrammetry and ground laser scanning and the Kriging interpolation algorithm, combining the finite element-finite volume mixed grid discretization method and the water flow penetration-soil stress coupling equation group, and realizes the high-precision reconstruction of the river channel slope three-dimensional geometric model.

[0023] The geometric characteristic parameters, geological stratification data and supporting structure parameters of the river channel slope are processed by the water-soil-structure multi-physical field unified equation group model to obtain a coupled digital twin initial model, including: The unmanned aerial vehicle oblique photogrammetry point cloud data and the ground laser scanning point cloud data are registered by the iterative closest point registration algorithm, and the borehole profile sampling points are meshed by the Kriging interpolation algorithm to obtain meshed borehole data. The registered point cloud data and the meshed borehole data are fused to obtain the river channel slope three-dimensional geometric entity model. The river channel slope three-dimensional geometric entity model is partitioned by the finite element-finite volume mixed grid generation algorithm, wherein the soil body domain adopts tetrahedral finite element unit, the water flow domain adopts hexahedral finite volume unit, and the soil-structure interface adopts contact unit to realize multi-physical field coupling discretization and obtain the coupled digital twin initial model.

[0024] In a specific embodiment, the coupled digital twin initial model includes a water flow penetration equation and a soil stress balance equation, wherein the water flow penetration equation is: ; ; wherein, is the soil water content, is the hydraulic head, is the water content dependent permeability coefficient, is the space-time related water source and sink term, is a spatial gradient operator, Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient; The land stress equilibrium equation is: Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient, Ks is a saturated permeability coefficient.

[0025] In a specific embodiment, for a large river channel slope engineering (slope height 50 meters, slope length 200 meters), the traditional modeling method has the problem of insufficient coupling precision of multiple physical fields due to single data source and fixed grid type. The embodiment provides a solution, and the steps are as follows: Multi-source data accurate registration fusion: high-precision point cloud data (accuracy ±2cm) of the slope surface is obtained by using unmanned aerial vehicle oblique photography, local fine point cloud data (accuracy ±5mm) is obtained by ground laser scanning, and millimeter-level accurate registration of the two kinds of point cloud data is realized by using iterative closest point registration algorithm. The registration error is controlled within ±3mm; at the same time, three-dimensional Kriging interpolation algorithm is used for spatial interpolation of 15 drilling sampling points arranged along the slope, and a continuous geological stratification model is constructed. The interpolation accuracy is improved by more than 40% compared with the traditional two-dimensional interpolation.

[0026] Hybrid grid intelligent generation: a finite element-finite volume hybrid grid generation algorithm is used, and different physical domain characteristics are processed by partitioning: tetrahedral finite element units (about 1.2 million units) are used for soil body domain which adapts to complex geometry, hexahedral finite volume units (about 800,000 units) are used for water flow domain which ensures mass conservation, and special contact units (about 150,000 units) are used for soil-structure interface, realizing seamless connection of different grid types, improving grid quality index by 25%, and improving calculation efficiency by 30%.

[0027] Accurate description of coupled equations: the established nonlinear permeability equation which can accurately describe the change of permeability characteristics of unsaturated soil, combined with the soil stress balance equation which considers the coupling effect of pore water pressure, realizes the mathematical description of the water-soil two-way coupling mechanism, and improves the calculation accuracy by 45% compared with the traditional decoupling method, effectively solving the precision loss problem caused by linearization in the traditional method.

[0028] ​The field monitoring data is processed by the distributed sensing network acquisition system to obtain a multi-source heterogeneous monitoring data set, and based on the multi-source heterogeneous monitoring data set, a coupled digital twin initial model is processed by inversion analysis and state estimation algorithm to obtain a digital twin optimized model, including: The river channel slope is monitored in real time by a distributed multi-level sensor network to obtain field monitoring data including GNSS displacement data, inclination deformation data, pore water pressure data and soil-structure interface contact stress data, and the field monitoring data is processed based on a data quality control algorithm to obtain a standardized multi-source heterogeneous monitoring data set; The multi-source heterogeneous monitoring data set and the coupled digital twin initial model are processed by a Bayesian parameter inversion algorithm to obtain a posterior probability distribution of model parameters, and model parameters are updated based on a maximum posterior estimation criterion to obtain a digital twin optimized model synchronized with the actual slope state.

[0029] Specifically, the present embodiment realizes accurate discretization of different physical domains by a finite element-finite volume hybrid mesh generation algorithm, adopts a partition processing strategy of tetrahedral finite elements for soil domains, hexahedral finite volumes for water flow domains, and contact elements for interfaces, establishes a mathematical model capable of accurately describing the water-soil interaction mechanism by combining a water content-dependent nonlinear permeation equation and a soil stress balance equation considering pore water pressure coupling, and improves the numerical stability of multi-physical field coupling calculation.

[0030] The field monitoring data is processed by the distributed sensing network acquisition system to obtain a multi-source heterogeneous monitoring data set, and based on the multi-source heterogeneous monitoring data set, a coupled digital twin initial model is processed by inversion analysis and state estimation algorithm to obtain a digital twin optimized model, further including: The field monitoring data is denoised by a wavelet denoising algorithm, and sensor fault data is identified by an outlier detection algorithm, and missing data is interpolated by a multi-sensor data fusion algorithm to obtain a time series complete standardized multi-source heterogeneous monitoring data set; Samples are extracted from the model parameter posterior distribution by a Markov chain Monte Carlo sampling algorithm, and sampling convergence is judged by a convergence diagnostic algorithm, and the expected value of the model parameters is calculated based on the sampling results as the updated value of the model parameters to realize dynamic calibration of the digital twin model.

[0031] In a specific embodiment, the calculation formula of the Bayesian parameter inversion algorithm is: ; Wherein, is the parameter posterior distribution, is the likelihood function, is the parameter prior distribution, is the 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] Algorithmic synergy optimization: Through a three-level data quality control process of wavelet denoising- anomaly detection- data fusion, combined with the closed-loop feedback mechanism of adaptive MCMC parameter inversion, a complete technical chain of "data quality control- parameter dynamic updating- model precision improvement" is constructed, realizing the dual protection of monitoring data quality and model reliability, and providing a high-quality digital foundation for accurate assessment of slope stability.

[0035] The multi-source heterogeneous monitoring data set is processed by the edge computing node to obtain edge pre-processing data, the edge pre-processing data and the digital twin optimization model are input into the cloud computing cluster, and the multi-physical field coupling analysis algorithm is used for processing to obtain the slope stability evaluation result data set, including: The data preprocessing algorithm deployed by the edge computing node performs real-time filtering, compression and feature extraction on the multi-source heterogeneous monitoring data set to obtain a set of key feature parameters after dimension reduction, and performs preliminary safety evaluation on the key feature parameter set through a lightweight stability evaluation model to obtain edge pre-processing data and a preliminary risk level. The edge pre-processing data and the digital twin optimization model are distributed to the cloud parallel computing cluster through the task scheduling algorithm, and the multi-physical field fully coupled finite element algorithm is used for numerical solution to obtain the slope stability evaluation result data set including stress distribution, displacement field and safety factor.

[0036] Specifically, the present embodiment realizes real-time acquisition of multi-dimensional monitoring data such as GNSS displacement, inclination deformation, pore water pressure and interface contact stress through a distributed multi-level sensor network, guarantees standardized processing of multi-source heterogeneous data through a data quality control algorithm, and realizes dynamic updating and optimization of digital twin model parameters through a Bayesian parameter inversion algorithm, improving the synchronization accuracy and dynamic adaptability of the digital twin model and the actual slope state.

[0037] The slope stability evaluation result data set is processed by a multi-index fusion algorithm to obtain a comprehensive risk score, and the comprehensive risk score is processed based on a hierarchical early warning threshold to obtain multi-level risk warning information and corresponding engineering disposal schemes, including: The slope stability evaluation result data set 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, and a multi-dimensional comprehensive risk score including immediate risk value, trend risk value and comprehensive risk value is obtained. The multi-dimensional comprehensive risk score is processed by a dynamic threshold adjustment algorithm, the warning threshold is dynamically adjusted according to historical risk data and current environmental conditions, four-level risk warning information including green safety, yellow attention, orange warning and red danger is generated, and corresponding engineering disposal schemes are matched based on an expert knowledge base.

[0038] Specifically, the embodiment realizes the fusion processing of multiple indexes such as displacement rate, stress state and hydrological condition by a multi-level fuzzy comprehensive evaluation algorithm, constructs a multi-dimensional scoring system including instant risk, trend risk and comprehensive risk, realizes the adaptive update of the early warning threshold by a dynamic threshold adjustment algorithm combined with historical risk data and environmental conditions, establishes a four-level early warning mechanism of green safety, yellow attention, orange early warning and red danger, and improves the accuracy of the slope stability risk assessment.

[0039] The slope stability assessment result data set is processed by the multi-index fusion algorithm to obtain a comprehensive risk score, the comprehensive risk score is processed based on a graded early warning threshold to obtain multi-level risk early warning information and a corresponding engineering disposal scheme, and the method further includes the following steps. The weight coefficients of the displacement rate index, the stress state index and the hydrological condition index are calculated by an analytic hierarchy process, each index is fuzzified by using a trapezoidal fuzzy membership function, and multi-index fusion calculation is performed by using a weighted fuzzy operator to obtain a comprehensive risk score reflecting the comprehensive state of the slope stability. The trend of the historical risk data is predicted by a time series analysis algorithm, the early warning threshold is dynamically adjusted by an adaptive threshold update algorithm combined with the current meteorological conditions and the slope state, and a matched disposal scheme is retrieved from an engineering disposal knowledge base to obtain a targeted engineering disposal scheme.

[0040] Specifically, the embodiment realizes the accurate allocation of the weight coefficients of the displacement rate, stress state and hydrological condition indexes by an analytic hierarchy process, realizes the accurate fusion calculation of multi-indexes by using a trapezoidal fuzzy membership function and a weighted fuzzy operator, predicts the trend of the historical risk data by a time series analysis algorithm, constructs an adaptive threshold update mechanism and an engineering disposal knowledge base matching system, and improves the scientificity of the comprehensive risk score and the intelligent level of the early warning threshold adjustment.

[0041] For reference Figure 2 The application also provides a river channel slope stability real-time monitoring and early warning system based on digital twinning, which comprises: An initial model construction module is configured to process the geometric characteristic parameters, geological layering data and supporting structure parameters of the river channel slope by a water-soil-structure multi-physical field unified equation group model to obtain a coupled digital twinning initial model. A model inversion optimization module is configured to process the field monitoring data by a distributed sensing network acquisition system to obtain a multi-source heterogeneous monitoring data set, process the coupled digital twinning initial model by inversion analysis and state estimation algorithm based on the multi-source heterogeneous monitoring data set, and obtain a digital twinning optimized model. The edge processing analysis module is configured to process the multi-source heterogeneous monitoring data set through an edge computing node, obtain edge pre-processing data, input the edge pre-processing data and the digital twin optimization model into a cloud computing cluster, and obtain a slope stability evaluation result data set through multi-physical field coupling analysis algorithm processing. The slope evaluation and early warning module is configured to process the slope stability evaluation result data set through a multi-index fusion algorithm, obtain a comprehensive risk score, process the comprehensive risk score based on a hierarchical early warning threshold, and obtain multi-level risk early warning information and corresponding engineering disposal schemes.

[0042] Specifically, the river slope stability real-time monitoring and early warning system based on digital twin of the embodiment realizes full-process automatic processing from digital twin model construction, multi-source data inversion update, edge-cloud collaborative calculation to comprehensive risk early warning through the construction of the initial model construction module, the model inversion optimization module, the edge processing analysis module and the slope evaluation and early warning module, and forms a complete river slope stability real-time monitoring and early warning system.

[0043] The application further discloses an electronic device, which comprises at least one processor, at least one memory, a communication interface and a bus: wherein the processor, the memory and the communication interface complete communication with each other through the bus; the memory stores program instructions executable by the processor, and the processor calls the program instructions to realize the river slope stability real-time monitoring and early warning method based on digital twin.

[0044] The application further discloses a computer readable storage medium, which stores computer instructions, and the computer instructions enable the computer to realize all or part of steps of the river slope stability real-time monitoring and early warning method based on digital twin. The storage medium comprises various storage medium capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0045] The above only describes preferred embodiments of the application and is not intended to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

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 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.

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 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.

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 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 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.

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 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.

5. The method for real-time monitoring and early warning of riverbank slope stability based on digital twins as described in claim 4, 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.

6. The method for real-time monitoring and early warning of riverbank slope stability based on digital twin 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.

7. The method for real-time monitoring and early warning of riverbank slope stability based on digital twin as described in claim 1, characterized in that, 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.

8. 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 1-7, 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.

9. 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 7.

10. 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 7.

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