An intelligent debris flow early warning method and system combining physical models and algorithms

Abstract

The present invention provides a debris flow intelligent early warning method and system combining physical models with algorithms, belonging to the technical field of geological disaster monitoring. The method includes: S1: Conduct indoor soil physical and mechanical tests, build a physical model, and calibrate the parameters of the physical model in combination with the stability criterion equation of the rainfall infiltration - matrix suction coupling effect; S2: Simulate different conditions, and the physical model generates and outputs the occurrence process data of debris flows under different working conditions; S3: Use the occurrence process data and real-time monitoring data as inputs to construct a dual-channel input network, train and correct the model to obtain the corrected response data; S4: Based on the SHAP interpretability framework, construct a Bayesian probability early warning model driven by multi-source heterogeneous data; S5: Trigger the real-time early warning of the early warning platform according to the real-time occurrence probability of debris flows at the monitoring points, and through the dynamic simulation of the mechanical behavior of the soil by the physical model, construct the mapping relationship between soil deformation and critical parameters.

Description

Technical Field

[0001] This specification relates to the technical field of geological disaster monitoring, and particularly relates to a debris flow intelligent early warning method and system combining physical models with algorithms. Background Art

[0002] Debris flow is a highly destructive geological disaster, and its early warning is of great significance for reducing disaster losses. The core pain points of current debris flow early warning technologies are as follows: 1. Single data dimension: Traditional methods rely on single-point monitoring data such as rain gauges and displacement sensors, making it difficult to capture the dynamic changes in soil mechanical properties and the coupling effects of multiple physical fields; 2. Insufficient model generalization ability: Empirical models are limited by regional geological characteristics, and it is costly to re-collect historical disaster data when migrating to a new monitoring area; 3. Difficulty and high cost in obtaining high-fidelity data: The complex terrain makes it difficult to deploy monitoring equipment, and the data quality and coverage are limited, resulting in difficulty and high cost in obtaining high-fidelity monitoring data.

[0003] The existing Chinese patent with the publication number CN117829031A and the name of "Dynamic simulation method considering the interaction between runoff and debris flow" includes constructing a rainfall spatial distribution model; constructing a vegetation interception model; constructing a soil infiltration coupling model; determining rainfall data, vegetation interception rainwater data, and soil infiltration rainwater data; determining the remaining rainwater data; obtaining the characteristic data of runoff and debris flow; constructing a two-layer depth-averaged model for the propagation of runoff and debris flow based on the water absorption rate parameter of debris flow for runoff; spatially discretizing the two-layer depth-averaged model to obtain a dynamic model; and simulating the behaviors and interaction processes of runoff and debris flow. The present invention considers the processes of rainfall, vegetation interception, soil infiltration, runoff generation, and debris flow propagation, proposes a two-layer depth-averaged model for describing the dynamics of runoff and debris flow, introduces the water absorption rate parameter, and uses the dynamic model to accurately and effectively simulate the behaviors and interaction processes of runoff and debris flow.

[0004] Although the above technology aims to improve the classification ability, it does not consider physical mechanism constraints, resulting in a deviation between the algorithm output and the actual situation. Therefore, there is an urgent need for a new early warning model that combines physical laws with data-driven algorithms.

[0005] Therefore, there is a need to provide a debris flow intelligent early warning method and system combining physical models with algorithms to achieve low-cost modeling driven by multi-source data. Summary of the Invention

[0006] The embodiments of this specification provide a debris flow intelligent early warning method and system combining physical models with algorithms. By dynamically simulating the mechanical behavior of soil through physical models, a mapping relationship between soil deformation and critical parameters is constructed, significantly reducing the dependence on high-precision monitoring data.

[0007] In some embodiments, a debris flow intelligent early warning method combining physical models and algorithms includes:

[0008] S1: Conduct indoor soil physical and mechanical tests, build a physical model, and calibrate the parameters of the physical model by combining the stability criterion equation of rainfall infiltration - matrix suction coupling effect.

[0009] S2: Simulate different conditions, and the physical model generates and outputs the occurrence process data of debris flows under different working conditions.

[0010] S3: Use the occurrence process data and real - time monitoring data as inputs, construct a dual - channel input network, train and correct the model to obtain the corrected response data.

[0011] S4: Based on the SHAP interpretability framework, quantify the contribution degree of each index to the occurrence probability of debris flows, and construct a Bayesian probability early warning model driven by multi - source heterogeneous data.

[0012] S5: Trigger the real - time early warning of the early warning platform according to the real - time occurrence probability of debris flows at the monitoring points.

[0013] Furthermore, in S1, establish a soil dynamic stability model based on the modified Mohr - Coulomb criterion, introduce the Bishop effective stress formula to characterize the influence of matrix suction on shear strength, combine the Richards equation to describe the rainfall infiltration process, calibrate the soil hydraulic parameters through triaxial tests, and construct a physical model.

[0014] Furthermore, in S1, the stability criterion equation of rainfall infiltration - matrix suction coupling effect includes:

[0015]

[0016] In the formula: is the effective cohesion of the soil, is the shear strength of the soil under effective stress, is the friction angle related to matrix suction, is the pore air pressure, is the pore water pressure, is the normal stress, is the soil unit weight, is the thickness of the sliding mass, is the inclination angle of the sliding surface.

[0017] Furthermore, in S2, different conditions include different rainfall intensities, rainfall durations, and initial water contents, and the occurrence process data includes soil displacement changes, pore water pressure changes, and soil stability changes.

[0018] Further, in S3, the dual-channel input network includes a GAN adversarial generation network and an improved ConvLSTM network. The original monitoring data is sent as input to the improved ConvLSTM network for distribution. The physical model generates simulated data through the GAN adversarial generation network. By using the simulated data and the original monitoring data as input, and taking whether the slope in the original monitoring data is unstable as the training target, the correction model is trained. The response data output by the physical model is corrected by the trained correction model.

[0019] Further, in S5, after embedding the physical model into the early warning platform, by taking the sensor data stream as the input of the Bayesian probability early warning model, the probability calculation result is output to trigger the corresponding level of early warning.

[0020] There is also a debris flow intelligent early warning system combining a physical model with an algorithm.

[0021] Further, it includes an early warning platform, a monitoring unit, and a physical model set on the platform.

[0022] The multi-source real-time monitoring data is obtained through the monitoring unit.

[0023] The physical model preprocesses the multi-source real-time monitoring data. By comparing the existing simulated data with the preprocessed data, the response data is output.

[0024] The early warning platform includes a multi-index coupling early warning model. By quantifying the decision contribution degree of the multi-index coupling early warning model, the real-time occurrence probability of debris flow of the response data is calculated.

[0025] Further, the monitoring unit includes a GNSS ground displacement monitoring station, a MEMS micro-displacement sensor, and a fiber Bragg grating pore water pressure gauge.

[0026] Further, the preprocessing includes using the wavelet transform-Kalman filter joint algorithm to perform time-frequency domain denoising on the multi-source real-time monitoring data, and extracting key indicators through feature engineering. The key indicators include displacement acceleration and hydraulic gradient mutation points.

[0027] Further, the quantification of the decision contribution degree includes critical rainfall intensity, displacement acceleration inflection point, and safety factor decay rate.

[0028] The beneficial effects of the present invention are:

[0029] 1. Realize low-cost modeling driven by multi-source data: Through the dynamic simulation of the mechanical behavior of the soil body by the physical model, the mapping relationship between soil body deformation and critical parameters is constructed, significantly reducing the dependence on high-precision monitoring data. Combining with transfer learning technology, the model can achieve cross-regional generalization applications based on limited samples, greatly reducing the data acquisition cost compared with traditional methods.

[0030] 2. Establish an intelligent early warning system for the fusion mechanism: Adopt a dual - engine architecture of physical model and deep learning. Among them, the physical engine is responsible for the numerical simulation of the soil stress - strain relationship, and the intelligent algorithm conducts non - linear modeling of the multi - parameter coupling relationship. Through the dynamic safety factor correction module and the displacement trend prediction unit, the early warning accuracy rate of this architecture is much higher than that of traditional early warning models;

[0031] 3. Form an adaptive risk prediction ability: The risk quantification module based on the improved Monte Carlo simulation can real - time analyze the joint probability distribution of parameters such as rainfall intensity, displacement acceleration, pore water pressure, etc., and early - warn potential instability risks through the failure mode recognition algorithm. The model supports multi - scale parameter migration and can still maintain a high prediction reliability in areas where monitoring data is missing. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] This specification will be further described by way of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0033] Figure 1 is a schematic diagram of the working principle shown in some embodiments of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the drawings represent the same structure or operation.

[0035] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0036] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "including" and "comprising" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0037] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the preceding or subsequent operations are not necessarily executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. Also, other operations can be added to these processes, or one or several steps can be removed from these processes.

[0038] Example:

[0039] Please refer to Figure 1 , for the target monitoring area, collect representative undisturbed soil samples and prepare them according to relevant specifications to ensure that the soil samples can truly reflect the physical and mechanical properties of the in-situ soil mass; conduct a series of indoor physical and mechanical tests, including but not limited to: basic physical property tests such as the density, water content, and particle size distribution of the soil mass, soil strength tests such as direct shear tests and triaxial compression tests, and permeability characteristic tests to analyze the influence of rainfall infiltration on soil mass stability.

[0040] Based on unsaturated soil mechanics theory, establish a stability criterion equation considering the coupling effect of rainfall infiltration - matrix suction,

[0041]

[0042] In the formula, is the effective cohesion of the soil mass, is the shear strength of the soil mass under effective stress, is the friction angle related to matrix suction, is the pore air pressure, is the pore water pressure, is the normal stress, is the unit weight of the soil mass, is the thickness of the sliding mass, is the dip angle of the sliding surface.

[0043] It should be noted that using the above physical model, simulate the occurrence process of debris flow under the influence of different conditions (different rainfall intensities, rainfall durations, initial water contents, etc.), obtain the safety factor, displacement field, stress field, etc. of the soil mass, and output response data including changes in soil mass displacement, pore water pressure, soil mass stability changes, etc.

[0044] Construct a dual-channel input network: The output data (displacement field, pore pressure field) of the physical model and the real-time monitoring data are input into the improved ConvLSTM network after standardization, and the feature weights are dynamically allocated through the attention mechanism; introduce the Generative Adversarial Network (GAN) to generate realistic soil instability sequence data to solve the problem of model overfitting in small-sample scenarios. Use the simulated data generated by the physical model and the actual monitoring data as inputs, and the result of whether the slope is unstable in the monitoring data as the training target to train and correct the model. Use the trained correction model to correct the response data generated by the physical model, so that the corrected data has the same accuracy and reliability as the measured data.

[0045] Based on the SHAP (Shapley Additive Explanations) interpretability framework, systematically quantify the contribution degrees of key indicators such as critical rainfall intensity (≥50mm / h), cumulative displacement (>10cm / 24h), and dynamic safety factor (threshold <1.05) to the probability of debris flow occurrence, and reveal the non-linear coupling mechanism of each indicator. Through the calculation of Shapley values, clarify the marginal effects of different features in model prediction. For example, the contribution weight of rainfall intensity to sudden debris flow is higher than that of cumulative displacement, and the dynamic adjustment of the safety factor can reflect the critical state of soil instability. Combine the historical disaster case database and the geological mechanics expert knowledge base to construct a Bayesian probability early warning model driven by multi-source heterogeneous data, and its output result is the probability value of debris flow occurrence in the range of 0-1.

[0046] After embedding the model into the real-time monitoring platform, dynamically analyze the sensor data stream through the edge computing module, update the index status and probability calculation results every 5 minutes, and adopt a three-level early warning mechanism: trigger a red early warning (evacuate immediately) when the probability value P≥0.7, initiate an orange early warning (control key areas) when 0.5≤P<0.7, and issue a yellow early warning (strengthen inspections) when 0.3≤P<0.5. The model is built with a feedback learning mechanism that can optimize the feature weight parameters using the actual occurrence of each early warning event to achieve adaptive iteration of the early warning threshold.

[0047] This design emphasizes the collaborative optimization of physical mechanisms and data-driven approaches. The SHAP interpretability module not only verifies the scientificity of the index system but also generates a visual attribution report, providing a traceable quantitative basis for emergency decision-making.

[0048] It should be noted that in the framework of unsaturated soil mechanics, a dynamic stability model of soil is established based on the modified Mohr-Coulomb criterion. The Bishop effective stress formula is introduced to characterize the influence of matrix suction on shear strength, and the Richards equation is combined to describe the rainfall infiltration process. The hydraulic parameters of the soil (such as soil-water characteristic curve, permeability coefficient function) are calibrated through triaxial tests, and a multi-physics field coupling dynamic analysis model of soil stability including pore water pressure gradient and displacement field evolution is constructed. By coupling the pore water pressure evolution equation with the stress-strain relationship of the soil, the instability thresholds of the soil under different rainfall intensities and infiltration paths are simulated, and a multi-dimensional dataset including the spatio-temporal distribution of rainfall, displacement field evolution, pore water pressure gradient and safety factor matrix is generated. At the same time, a transfer learning framework (adversarial neural network DANN) is introduced, and the data generated by the model and the on-site monitoring data are mapped across domains to improve the generalization ability of the model.

[0049] A multi-source sensing network (GNSS surface displacement monitoring station, MEMS micro-displacement sensor, fiber Bragg grating pore water pressure gauge) is used to achieve centimeter-level deformation monitoring and minute-level data transmission, and the InSAR satellite remote sensing is combined to invert the regional surface deformation field. The wavelet transform-Kalman filter joint algorithm is used to denoise the monitoring data in the time-frequency domain, and key indicators such as displacement acceleration and hydraulic gradient mutation points are extracted through feature engineering. A spatial data alignment model based on weighted regression (GWR) is established to unify the spatio-temporal benchmarks of physical simulation data and multi-source monitoring data.

[0050] A physics-guided neural network architecture (PINN) is constructed, and the soil constitutive equation is embedded into the LSTM network as a regularization term. The simulation data and the monitoring data stream are processed synchronously through a dual-channel input mechanism. The Bayesian deep learning framework is used to quantify the epistemic uncertainty in the data correction process, and the Monte Carlo Dropout strategy is used to dynamically evaluate the correction confidence. A hybrid loss function is designed, comprehensively considering the mean square error (MSE), dynamic time warping (DTW) and physical conservation constraints, to ensure that the correction results satisfy both data matching and mechanical laws.

[0051] A multi-index coupling early warning model is constructed based on the SHAP-XGBoost framework to quantify the decision-making contribution degrees of indicators such as critical rainfall intensity (≥50mm / h), displacement acceleration inflection point (second derivative > 0.05cm / h²), safety factor decay rate (ΔSF / Δt < -0.1h⁻¹), etc. The federated learning mechanism is used to realize the collaborative update of the parameters of the regional sub-model and the central model, and support the local inference of distributed edge nodes (such as on-site RTU controllers). A lightweight early warning engine is deployed, integrating an online learning module, which automatically triggers the incremental update of the model when the monitoring data deviates from the training distribution, ensuring the continuous reliability of the system in a non-stationary environment.

[0052] In summary, an intelligent early warning system integrating mechanisms is established: a dual-engine architecture of physical models and deep learning is adopted, where the physical engine is responsible for the numerical simulation of the soil stress-strain relationship, and the intelligent algorithm conducts non-linear modeling of the multi-parameter coupling relationship. Through the dynamic safety factor correction module and the displacement trend prediction unit, the accuracy of this architecture's early warning is much higher than that of traditional early warning models.

Claims

1. An intelligent early warning method for debris flow combining physical models and algorithms, characterized in that, including S1: Conduct indoor soil physical and mechanical tests, build a physical model, and calibrate the parameters of the physical model in combination with the stability criterion equation of the rainfall infiltration - matrix suction coupling effect; S2: Simulate different conditions, and the physical model generates and outputs the occurrence process data of debris flow under different working conditions; S3: Use the occurrence process data and real-time monitoring data as inputs to construct a dual-channel input network, train and correct the model to obtain the corrected response data. Among them, the dual-channel input network includes a GAN adversarial generation network and an improved ConvLSTM network. The original monitoring data is sent as an input to the improved ConvLSTM network for distribution. The physical model generates simulated data through the GAN adversarial generation network. By using the simulated data and the original monitoring data as inputs, and taking whether the slope is unstable in the original monitoring data as the training target, train and correct the model, and use the trained corrected model to correct the response data output by the physical model; S4: Based on the SHAP interpretability framework, quantify the contribution degree of each index to the occurrence probability of debris flow, and construct a Bayesian probability early warning model driven by multi-source heterogeneous data; S5: Trigger the real-time early warning of the early warning platform according to the real-time occurrence probability of debris flow at the monitoring point.

2. The debris flow intelligent early warning method combining a physical model with an algorithm according to claim 1, characterized in that, In S1, establish a soil dynamic stability model based on the modified Mohr-Coulomb criterion, introduce the Bishop effective stress formula to characterize the influence of matrix suction on shear strength, combine the Richards equation to describe the rainfall infiltration process, calibrate the soil hydraulic parameters through triaxial tests, and construct a physical model.

3. The debris flow intelligent early warning method combining a physical model with an algorithm according to claim 2, wherein, In S1, the stability criterion equation of the rainfall infiltration - matrix suction coupling effect includes Wherein, is the effective cohesive force of the soil mass, is the shear strength of the soil mass under effective stress, is the friction angle related to the matric suction, is the pore air pressure, is the pore water pressure, is the normal stress, is the unit weight of the soil mass, is the thickness of the sliding mass, is the dip angle of the slip surface.

4. The debris flow intelligent early warning method combining a physical model with an algorithm according to claim 3, characterized in that, In S2, different conditions include different rainfall intensities, rainfall durations, and initial water contents, and the occurrence process data includes soil displacement changes, pore water pressure changes, and soil stability changes.

5. The debris flow intelligent early warning method combining a physical model with an algorithm as described in claim 4, characterized in that, In S5, after embedding the physical model into the early warning platform, by using the sensor data stream as the input of the Bayesian probability early warning model, output the probability calculation result, and trigger the corresponding level of early warning.

6. A debris flow intelligent early warning system combining a physical model and an algorithm, characterized in that, It includes an early warning platform, a monitoring unit, and a physical model set on the platform Obtain multi-source real-time monitoring data through the monitoring unit; Preprocess the multi-source real-time monitoring data through the physical model, compare the existing simulated data with the preprocessed data, and output the response data. Among them, construct a dual-channel input network, which includes a GAN adversarial generation network and an improved ConvLSTM network. The original monitoring data is sent as an input to the improved ConvLSTM network for distribution. The physical model generates simulated data through the GAN adversarial generation network. By using the simulated data and the original monitoring data as inputs, and taking whether the slope is unstable in the original monitoring data as the training target, train and correct the model, and use the trained corrected model to correct the response data output by the physical model, train and correct the model to obtain the corrected response data; The early warning platform includes a multi-index coupling early warning model, and calculates the real-time occurrence probability of debris flow of the response data by quantifying the decision contribution degree of the multi-index coupling early warning model.

7. The debris flow intelligent early warning system combining a physical model and an algorithm according to claim 6, characterized in that, The monitoring unit includes a GNSS surface displacement monitoring station, a MEMS micro-displacement sensor, and a fiber Bragg grating pore water pressure gauge.

8. The debris flow intelligent early warning system combining a physical model and an algorithm according to claim 6, characterized in that The preprocessing includes using a wavelet transform-Kalman filter combined algorithm to denoise multi-source real-time monitoring data in the time-frequency domain, and extracting key indicators through feature engineering. The key indicators include displacement acceleration and hydraulic gradient mutation points.

9. The debris flow intelligent early warning system combining a physical model with an algorithm according to claim 6, wherein The quantification of decision-making contribution degree includes critical rainfall intensity, displacement acceleration inflection point, and safety factor decay rate.

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