An intelligent landslide rainfall simulation system and method based on AI historical rainfall data driving

Abstract

The application provides an AI history rainfall data driven intelligent landslide rainfall simulation system and method, and belongs to the technical field of geological disaster monitoring and early warning. Through automatic fusion of multi-source monitoring data and space-time feature extraction, combined with deep learning and physical mechanism modeling, dynamic coupling analysis and high-precision simulation of rainfall process and slope stability are realized. The system can realize real-time sensing of rainfall, soil moisture content, slope deformation and other data, intelligently identify landslide induced risks, output graded early warning information, and support digital twinning and adaptive risk optimization decision-making. The application significantly improves the landslide disaster risk assessment and early warning accuracy and response timeliness, has good intelligence, expansibility and practical application value, and is widely applicable to the fields of geological disaster prevention and engineering safety monitoring.

Description

Technical Field

[0001] This invention belongs to the field of geological disaster monitoring and early warning technology, and more specifically relates to an intelligent landslide rainfall simulation system and method driven by AI historical rainfall data. Background Technology

[0002] With the increasing frequency and severity of global climate variability and extreme rainfall events, landslides are becoming more frequent and destructive. Traditional landslide monitoring and early warning methods rely primarily on limited empirical rainfall thresholds, on-site manual observations, and simplified physical models. These methods often fail to accurately and timely reflect landslide induction mechanisms and evolution processes when faced with complex geological structures, variable meteorological conditions, and heterogeneous and spatiotemporally dynamic data. Furthermore, traditional physical modeling methods are highly dependent on the completeness of geological survey data and cannot perform high-precision response simulations of spatially heterogeneous slopes with dynamically changing parameters.

[0003] Currently, with the rapid development of high-resolution remote sensing technology, IoT sensing, and artificial intelligence, multi-source data fusion, big data mining of historical disaster cases, and AI-driven spatiotemporal feature extraction and causal relationship modeling have become important trends in improving the simulation and early warning capabilities of landslide disasters. However, existing systems often lack intelligent understanding of the deep coupling mechanism between rainfall and slope stability, making it difficult to achieve multi-scale, highly dynamic, and causally interpretable landslide risk assessment and early warning. Therefore, there is an urgent need for an intelligent landslide rainfall simulation system that integrates AI technology, multi-source heterogeneous sensing, digital twin reconstruction, and adaptive decision optimization to improve the accuracy and reliability of landslide disaster prediction and better serve geological disaster prevention and engineering safety management. Summary of the Invention

[0004] To address the shortcomings of existing landslide monitoring and early warning systems, such as insufficient understanding of rainfall-induced mechanisms, limited multi-source data fusion capabilities, low accuracy of physical simulation, and low levels of intelligence in risk assessment and early warning, this invention proposes an intelligent landslide rainfall simulation system driven by AI-based historical rainfall data. This system aims to achieve intelligent analysis and high-precision dynamic simulation of the coupling mechanism between rainfall and landslides through multimodal perception and dynamic data integration, AI spatiotemporal feature extraction, rainfall-slope stability causal modeling, digital twin simulation, and adaptive early warning optimization. This significantly improves the accuracy of landslide risk assessment and the timeliness of early warning, effectively solving the technical challenges of inaccurate predictions and untimely responses under complex geological and meteorological conditions.

[0005] To achieve the above objectives, the present invention employs the following technical solution: the method comprises: Multidimensional perception and dynamic data integration are constructed by deploying a multidimensional sensor network to collect rainfall intensity, soil moisture content, slope displacement, environmental and geological parameters in real time. The real-time data is dynamically integrated with historical landslide cases, geological survey data, and high-dimensional data from remote sensing images to form a multimodal landslide risk sample library. Adaptive extraction of joint features of topography and rainfall time series: Based on the spatiotemporal interactive mapping network TSIM-Net, a dual-stream model is designed to extract features of topographic and geomorphic spatial features and rainfall time series dynamic response respectively. At the same time, an interactive fusion channel is constructed to capture the coupling effect of slope and rainfall. An adaptive multi-head attention mechanism is introduced into the model structure to realize the dynamic adjustment of the weights of different geological parameters and rainfall processes, highlighting the impact of key disaster-causing factors. To model the causal relationship between rainfall and slope stability, this paper proposes a prior knowledge transfer-causal relationship reinforcement coupling algorithm, addressing the characteristics of small sample size, heterogeneity, and time-varying nature. This algorithm embeds historically known landslide triggers into the neural network representation layer, achieving deep causal relationship modeling between rainfall and slope stability. Combined with generative adversarial training, this ensures the model's generalization performance and causal interpretability. Digital twin slope construction and high-precision landslide dynamic simulation: Based on three-dimensional terrain point cloud and real-time monitoring data, a physical-data dual-drive fine three-dimensional reconstruction algorithm is used to build a digital twin slope. AI prediction output is used to dynamically adjust the boundary conditions of finite element physical simulation to achieve high-precision slope response simulation during rainfall. Intelligent adaptive landslide early warning and suggestion output: Design an adaptive early warning decision module for landslide risk. Based on TSIM-Net and twin simulation results, integrate Bayesian confidence correction optimization to achieve real-time adaptive adjustment of the early warning threshold.

[0006] In one solution, the multi-dimensional perception and dynamic data integration construction includes a multi-dimensional intelligent sensor network of various types of hardware devices such as meteorological rain gauges, soil moisture sensors, slope deformation monitoring instruments, and surface current meters, to achieve real-time and continuous perception of key parameters such as rainfall intensity, soil moisture content, slope displacement, aquifer changes, and groundwater level; the data is preprocessed by an edge computing gateway to perform noise filtering, outlier detection, and data feature extraction before being transmitted back to the central database. Furthermore, by standardizing and formatting the real-time monitoring data and integrating it with multi-source data such as historical landslide cases, geological survey data, remote sensing images, and disaster assessment information, a multi-modal fusion engine is used to dynamically integrate the above data to establish a dynamically updated, structured multi-modal sample library of landslide risks.

[0007] In one approach, the adaptive extraction of the joint topographic and rainfall time-series features includes: using a temporal-spatial interactive mapping network (TSIM-Net) to extract joint features of topographic spatial features and rainfall time-series dynamic responses from a multimodal landslide risk sample database; Among them, TSIM-Net adopts a dual-stream deep neural network structure, with one spatial stream encoding the spatial characteristics of slope, aspect, elevation undulation, and soil structure based on a three-dimensional convolutional neural network; Another timeline is based on time-series modeling methods to process the temporal characteristics of rainfall intensity, cumulative precipitation, and rainfall pattern distribution. Meanwhile, an interactive fusion channel is set up to achieve the fusion of coupled features of slope and rainfall response; and an adaptive multi-head attention mechanism is introduced into the model structure to dynamically adjust the weights of different geological parameters and rainfall processes in feature representation.

[0008] In one scheme, the causal relationship modeling of rainfall-slope stability includes: using a prior knowledge transfer and causal relationship reinforcement coupling algorithm to transform known inducing factors and classic geological instability criteria in historical landslide cases into structured knowledge vectors, and deeply integrating them with multimodal spatiotemporal features through a knowledge injection mechanism in the neural network representation layer to achieve interpretable modeling of the causal relationship between rainfall and slope stability; Meanwhile, by combining the geological constraint adversarial enhancement network, and by introducing geophysical laws and classical stability criteria as generation constraints during the generation training process, the model not only improves its adaptability to boundary extreme samples and time-varying working conditions during the discrimination and generation process, but also ensures that the output slope stability risk assessment results conform to geophysical laws.

[0009] In one approach, the digital twin slope construction and high-precision landslide dynamic simulation acquire high-density three-dimensional terrain point clouds and real-time multi-dimensional monitoring data, combine historical geological profiles and slope structural properties, and employ a physical-data dual-drive fine three-dimensional reconstruction algorithm to achieve millimeter-level three-dimensional modeling of the slope structure and physical state. The digital twin slope model uses dynamic interpolation, inverse modeling, and physical constraint optimization to couple the terrain point cloud with the monitoring status to generate a high-precision three-dimensional mesh, covering soil and rock types, layered boundaries, fracture parameters, density, cohesion, internal friction angle, water content, and displacement properties. It is linked with the dynamic risk factors or sliding zone determination output by the AI ​​prediction model to automatically adjust the boundary and initial conditions in the finite element physical simulation, so as to realize the hourly dynamic simulation of the slope response and stability during the rainfall process. Meanwhile, it integrates a three-dimensional visualization spatiotemporal evolution module to interactively simulate and render the digital twin slope and its physical state changes, dynamically displaying the evolution of key parameters and catastrophic trends during the landslide process.

[0010] In one scheme, the intelligent adaptive landslide early warning and suggestion output is based on the spatiotemporal coupling characteristics of landslide disasters extracted by TSIM-Net and the output results of digital twin slope simulation. It integrates multi-source risk evidence and introduces the Bayesian confidence correction optimization method to dynamically adjust the early warning decision threshold based on real-time monitoring and historical landslide case data, so as to realize intelligent quantification and real-time response to landslide probability, graded risk, spatial impact range and uncertainty. It automatically outputs comprehensive early warning information, including landslide probability, potential impact area, risk classification, and customized emergency response suggestions. The content includes evacuation guidelines, engineering protection measures, and monitoring encryption suggestions, and it has a linkage interface function with physical rainfall simulation devices.

[0011] Furthermore, an intelligent landslide rainfall simulation system driven by AI historical rainfall data is provided, the system being applicable to the method described above, the system comprising: 1) Multi-dimensional sensing and dynamic data integration module, used to deploy various types of sensing hardware such as meteorological rain gauges, soil moisture sensors, slope deformation monitoring instruments, and surface current meters, to realize real-time acquisition of rainfall intensity, soil moisture content, slope displacement, environmental and geological parameters, and dynamically integrate the acquired data with historical landslide cases, geological exploration data, and remote sensing imagery high-dimensional data to construct a multi-modal landslide risk sample library; 2) The topographic and geomorphological-rainfall time series joint feature adaptive extraction module adopts the TSIM-Net spatiotemporal interactive mapping network to design a dual-stream model, extracts the spatial features of topographic and geomorphological features and the dynamic response features of rainfall time series, constructs an interactive fusion channel to capture the coupling effect of slope and rainfall, and uses an adaptive multi-head attention mechanism to dynamically adjust the feature weights of different geological parameters and rainfall processes. 3) Rainfall-slope stability causal relationship modeling module: Based on prior knowledge transfer and causal relationship reinforcement coupling algorithm, historical landslide induction knowledge is embedded into the neural network representation layer to realize deep causal modeling between rainfall and slope stability. Generative adversarial training is combined to enhance the generalization and causal interpretability of the model. 4) Digital twin slope construction and high-precision landslide dynamic simulation module: Based on three-dimensional terrain point cloud and real-time monitoring data, a physical-data dual-driven three-dimensional reconstruction algorithm is used to build a digital twin slope, and the boundary conditions of finite element physical simulation are dynamically adjusted according to AI prediction results to achieve high-precision simulation of slope response during rainfall. 5) Intelligent adaptive landslide early warning and suggestion output module: Based on TSIM-Net and twin simulation results, it integrates Bayesian confidence correction optimization to realize real-time adaptive adjustment of early warning threshold and outputs comprehensive early warning decision including evacuation guidance, engineering protection measures and monitoring encryption suggestions. It also has a linkage interface function with physical rainfall simulation device.

[0012] Beneficial effects of this invention: This invention introduces AI-driven intelligent landslide rainfall simulation technology, effectively enhancing the understanding and analysis of landslide disaster mechanisms. It significantly improves the efficiency of multi-source heterogeneous data fusion and spatiotemporal feature extraction, achieving high-precision dynamic simulation with deep coupling between rainfall and slope stability. Compared to traditional landslide monitoring and early warning methods, this invention can more accurately and in real-time assess landslide risk, greatly improving the timeliness and reliability of early warnings, reducing false alarms and missed alarms, and lowering disaster losses and emergency management costs. Furthermore, through digital twin and adaptive decision optimization capabilities, this invention possesses excellent system scalability and intelligence, and can be widely applied in geological disaster prevention and control, engineering safety monitoring, and urban emergency management, helping relevant departments improve their disaster risk prevention and control and refined management capabilities. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0014] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0015] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0016] like Figure 1 As shown, an intelligent landslide rainfall simulation method based on AI historical rainfall data is implemented through the following steps: Step 1: Construction of Multidimensional Perception and Dynamic Data Integration A multi-dimensional sensor network is deployed to collect real-time data on rainfall intensity, soil moisture content, slope displacement, and environmental and geological parameters. This real-time data is then dynamically integrated with historical landslide cases, geological survey data, and high-dimensional remote sensing imagery to form a multi-modal landslide risk sample library.

[0017] First, a multi-dimensional intelligent sensor network needs to be scientifically planned and deployed based on the topography, geomorphology, and actual monitoring needs of the landslide-prone area. This sensor network includes various types of hardware devices such as high-precision rain gauges, soil moisture sensors, slope deformation monitoring instruments (GNSS, fiber optic sensors, crack gauges), and surface current meters, enabling real-time and continuous sensing of core parameters such as rainfall intensity, soil moisture content, slope displacement, aquifer changes, and groundwater level. The collected multi-source raw data is preprocessed through an edge computing gateway, automatically performing noise filtering, outlier detection, and preliminary data feature extraction, and then efficiently transmitted back to the central database system via wireless / wired networks.

[0018] Building upon real-time monitoring data, the system further integrates historical landslide case data, including meteorological data at the time of landslides, geological disaster investigation reports, and post-disaster assessment documents. This, combined with geological exploration results and remote sensing imagery, provides multi-dimensional supplementary information on topography, geomorphology, structure, and evolution over a large spatiotemporal scope. All historical and current data are formatted and standardized using unified data specifications. Through intelligent tagging and metadata management, the system supports the spatiotemporal location, attribute description, and tracing of disaster-causing factors for each data point.

[0019] Ultimately, the system uses a multimodal fusion engine at its core to dynamically integrate real-time multidimensional sensing data with multidimensional static historical data, establishing a highly scalable and structured data sample library. This sample library supports subsequent AI modeling and inference, can dynamically update newly collected data, continuously enriching the samples related to landslide disaster mechanisms, and significantly improving the completeness of landslide risk analysis and the generalization ability of model training.

[0020] Step 2: Adaptive extraction of joint features of topography and rainfall time series Based on the Temporal-Spatial Interaction Mapping Network (TSIM-Net), a dual-flow model is designed to extract features from the spatial characteristics of topography and the dynamic temporal response of rainfall, respectively. Simultaneously, an interactive fusion channel is constructed to capture the coupling effect between slope and rainfall. An adaptive multi-head attention mechanism is introduced into the model structure to dynamically adjust the weights of different geological parameters and rainfall processes, highlighting the impact of key disaster-causing factors.

[0021] Based on the multimodal landslide risk sample library constructed in step 1, the system uses the temporal-spatial interactive mapping network (TSIM-Net) to explore the intrinsic connections between geological disaster-causing factors.

[0022] Specifically, TSIM-Net employs a dual-stream deep neural network structure. One spatial stream encodes three-dimensional features of static topographic features (such as slope, aspect, elevation undulation, and soil structure parameters), while the other temporal stream models the sequence of rainfall time-series data (including rainfall intensity variations, cumulative precipitation, and rainfall pattern distribution) collected by a multidimensional sensing network. The spatial stream typically uses a spatial convolutional network (3D CNN) to process the input topographic point cloud or DEM data. Feature extraction is performed to obtain the feature matrix of the slope's spatial structure. ; Meanwhile, the time stream employs a time series modeling method (LSTM) to analyze the rainfall time series signal. Expand the processing and extract the feature matrix of rainfall dynamic evolution. To fully describe the coupling effect between spatial heterogeneity and temporal response, TSIM-Net designs a cross-stream interactive fusion channel, generating joint feature representations through tensor product or attention-guided fusion methods. Fusion can represent higher-order cross-correlation, bilinear interaction, or multi-head attention fusion operations.

[0023] An adaptive multi-head attention mechanism is introduced into the TSIM-Net fusion module to achieve dynamic weight control of different geological parameters and spatiotemporal segments in feature representation. Specifically, multi-head attention layers are set for both spatial and temporal flows, and an attention mapping matrix is ​​defined. Where h is the h-th attention head. and For a learnable parameter matrix, The dimension of the attention vector; this mechanism assigns different weights to various geological indicators and the impact of rainfall at different times, through weighted summation. The coupling effect of key disaster-causing factors is automatically highlighted. Ultimately, throughout the end-to-end training process of TSIM-Net, a multimodal loss function is used to regulate spatial-temporal feature extraction and interactive learning, improving the model's ability to characterize complex rainfall-landslide response mechanisms and achieving greater accuracy and interpretability in landslide risk representation. In the above formula, and These represent the input topographic data and rainfall time series data, respectively. and Let N be the feature dimension, N be the number of spatial sampling points, and T be the temporal length. The attention matrix is ​​calculated for each attention head.

[0024] Step 3: Modeling the causal relationship between rainfall and slope stability To address the challenges of small sample sizes, heterogeneity, and time-varying characteristics, an innovative "prior knowledge transfer-causal relationship reinforcement coupling algorithm" is proposed. This algorithm embeds historically known landslide causal factors into the neural network representation layer, enabling deep causal relationship modeling between rainfall and slope stability. Combined with generative adversarial training (but not a traditional GAN, employing a geologically constrained adversarial reinforcement network that introduces geophysical laws as generative constraints), the algorithm ensures the model's generalization performance and causal interpretability.

[0025] The system needs to overcome the bottleneck of simple correlation learning, addressing challenges such as small sample sizes, heterogeneous data, and time-varying dynamic complexity, to achieve interpretable and transferable causal reasoning for disasters. Firstly, based on the multimodal high-dimensional spatiotemporal joint feature representation obtained in step 2... A prior knowledge transfer mechanism is introduced to transform known triggering factors (such as critical rainfall thresholds, empirical rules of safety factors for different soil and rock types, and classic geological instability criteria) from a large number of historical landslide cases into structured knowledge representation vectors. In the neural network representation layer (deep feature fusion module), a knowledge injection mechanism is used to... The feature distribution currently learned by the network To merge, mathematically expressed as ,in This is a fusion function (concatenation) that achieves a deep connection between historical physical knowledge and real-time AI representation. This strategy not only enhances the model's ability to perceive the causal chain of physical reality, but also effectively improves its generalization performance in new regions and small sample scenarios.

[0026] To further capture the multi-domain causal relationships between the physical, structural, and environmental domains in the evolution of rainfall and slope stability, the system is trained using a developed "geologically constrained adversarial enhancement network." Unlike the unconstrained adversarial generation of traditional GANs, this framework specifically incorporates geophysical laws and classical stability criteria as generation constraints, building upon the generator (G) and discriminator (D). For example, the generator, based on joint features... Output slope stability risk score , where z represents generated noise or sampling disturbance; and the discriminator not only distinguishes the consistency between the result and the actual monitored stability label y, but also introduces a geological constraint loss term. , in formula The measurement of the difference between the model predictions and the physical rules, among which These are geophysical stability assessment functions, such as limit equilibrium analysis and yield envelope theory.

[0027] The total loss in adversarial training can be formally written as: ,in For routine assessment of combat losses, Learning enhances the ability to adapt to extreme boundary samples and time-varying working conditions, while geological and physical constraints ensure that the output slope stability assessment has reasonable interpretability and engineering usability.

[0028] Using the above methods, the system achieves a deep coupling model of rainfall-slope stability with causal explanation, transferability, and high adaptability, providing a solid data intelligence foundation for highly reliable landslide risk early warning.

[0029] Step 4: Digital Twin Slope Construction and High-Precision Landslide Dynamic Simulation Based on 3D terrain point cloud and real-time monitoring data, a digital twin slope is established using a "physical-data dual-drive fine 3D reconstruction algorithm," achieving millimeter-level modeling of the slope structure and physical state. AI-predicted outputs are used to dynamically adjust the boundary conditions of the finite element physical simulation, enabling high-precision simulation of the slope response during rainfall.

[0030] Combined with a 3D visualization spatiotemporal evolution module, it dynamically displays landslide pre-simulation and disaster processes.

[0031] In the "Digital Twin Slope Construction and High-Precision Landslide Dynamic Simulation" phase, the first step is to acquire high-density three-dimensional terrain point cloud data (LiDAR point cloud). ) and real-time multidimensional monitoring data (such as soil displacement) Moisture content Rainfall intensity Historical geological profiles and structural attributes are integrated as the basis for modeling. A physical-data dual-driven fine 3D reconstruction algorithm is employed to synchronously couple the measured point cloud and monitoring status through dynamic interpolation, inverse modeling, and physical constraint optimization, generating a high-precision digital twin 3D mesh model. .

[0032] Specifically, point cloud data is obtained through spatial interpolation functions. Reconstructing the surface of the landform: Soil structural properties (soil type, layered boundaries, fracture parameters) are mapped into grid voxels under physical constraints, and each voxel attribute set is represented as follows: ,in For density, For cohesion, It is the internal friction angle. , These are time-varying moisture content and displacement, respectively.

[0033] Subsequently, the digital twin slope model not only reflects the static structure but also couples dynamic state inputs with the AI ​​prediction model. The AI ​​model outputs rainfall-slope stability risk factors or landslide zone determinations (see step 3, the output is represented as...). This is used to dynamically control the boundaries and initial conditions of finite element method (FEM) simulations. In finite element simulations, slope meshes are utilized. With physical properties Hourly dynamic simulations were performed on the changes in pore pressure, stress-strain field, and overall stability caused by rainfall. The physical governing equations typically include soil-hydraulic coupled seepage equations (such as the Richard equations): And the strength-failure criterion (Mohr-Coulomb theory): Ultimate shear strength, The effective normal stress; the evolution of the displacement field over time is described by the FEM discrete dynamic equations: ,in Let M be the displacement vector, and C, K be the mass, damping, and stiffness matrices, respectively. For external loads and AI-predicted disturbance terms.

[0034] To achieve intuitive representation and analysis of landslide processes, the system integrates a 3D visualization "spatiotemporal evolution" module for digital twin slope analysis. The module simulates and renders the time-varying states of the landslide. It maps key parameters obtained from the simulation (water level evolution, plastic zone expansion, and slip zone development trend) to an interactive 3D animation, supporting user retrospection, prediction, and multi-scenario simulation. Ultimately, these technologies support each other, achieving millimeter-level digital twin slope structure and real-time dynamic simulation, providing highly reliable, interpretable, and visualized engineering support for landslide disaster prediction, risk assessment, and emergency response.

[0035] Step 5: Intelligent Adaptive Landslide Early Warning and Recommendation Output An adaptive early warning decision-making module for landslide risk was designed. Based on TSIM-Net and twin simulation results, Bayesian confidence correction optimization was integrated to achieve real-time adaptive adjustment of the early warning threshold. The system automatically outputs comprehensive early warning information including landslide probability, impact range, risk level, and emergency response suggestions. It supports dynamic control of a physical rainfall simulation device to reproduce real rainfall patterns for experimental verification and decision support.

[0036] In the intelligent adaptive landslide early warning and suggestion output stage, the system innovatively constructs a landslide risk adaptive early warning decision module to achieve dynamic, accurate, and intelligent assessment of landslide risk. This module relies on the spatiotemporally coupled landslide disaster characteristics extracted by TSIM-Net and the high-precision dynamic simulation output of the digital twin slope, forming a fusion basis for multi-source heterogeneous risk evidence. To overcome the problems of rigid early warning thresholds and susceptibility to missed or false alarms in traditional systems, the system introduces Bayesian confidence correction optimization. Specifically, in the landslide probability output by TSIM-Net... Risk score estimated by twin simulation Based on this, and by integrating historical precedent landslide case data... The risk posterior is continuously updated through Bayesian inference. Under real-time monitoring of input, the early warning decision threshold is determined. Instead of setting a fixed threshold, it adaptively optimizes based on the current evidence data, mathematically expressed as: ,in Input for the current feature, This represents the real-time posterior probability of landslide risk. Based on this, the system updates the warning level classification threshold in real time according to the risk distribution, achieving a highly sensitive response and uncertainty quantification under extreme working conditions and abnormal sudden situations.

[0037] In the comprehensive risk decision-making output stage, the module automatically generates multi-dimensional early warning information: on the one hand, by analyzing the landslide probability distribution ( , To integrate weights) and spatial influence zone prediction (the slope sliding influence zone can be output by twin simulation) The system automatically classifies risks (general, moderate, severe) and assigns warning colors. Furthermore, based on causal diagnosis results, it outputs customized emergency response suggestions, including evacuation alerts, engineering protection plans, and monitoring encryption recommendations. The system also provides a control interface for physical rainfall simulation devices: when a potentially high-risk rainfall pattern is detected during monitoring or simulation, it automatically pushes a "reproduce real rainfall pattern" command with parameterized parameters, enabling the laboratory reproduction of the disaster-inducing process. This can be used for physical experimental verification of the AI ​​early warning system and provides data-physical dual support for frontline emergency decision-making and multi-round simulations. Overall, this module highly integrates AI spatiotemporal perception, physical simulation, Bayesian reliability optimization, and engineering suggestion output, supporting the foresight, dynamism, and decision-making usability of landslide disaster early warning, significantly improving the scientific rigor and effectiveness of disaster emergency response.

[0038] like Figure 2 As shown, an intelligent landslide rainfall simulation system driven by AI historical rainfall data specifically includes: The multi-dimensional sensing and dynamic data integration module is used to deploy various types of sensing hardware such as meteorological rain gauges, soil moisture sensors, slope deformation monitoring instruments, and surface current meters to realize real-time acquisition of rainfall intensity, soil moisture content, slope displacement, environmental and geological parameters. The acquired data is dynamically integrated with historical landslide cases, geological survey data, and high-dimensional data from remote sensing images to construct a multi-modal landslide risk sample library. The adaptive extraction module for the joint features of topography and rainfall time series uses a two-stream model designed with the TSIM-Net spatiotemporal interactive mapping network to extract the spatial features of topography and the dynamic response features of rainfall time series, respectively. An interactive fusion channel is constructed to capture the coupling effect between slope and rainfall, and an adaptive multi-head attention mechanism is used to dynamically adjust the feature weights of different geological parameters and rainfall processes. The rainfall-slope stability causal correlation module, based on prior knowledge transfer and causal relationship reinforcement coupling algorithm, embeds historical landslide induction knowledge into the neural network representation layer to achieve deep causal modeling between rainfall and slope stability, and combines generative adversarial training to enhance the model's generalization and causal interpretability. The digital twin slope construction and high-precision landslide dynamic simulation module, based on three-dimensional terrain point cloud and real-time monitoring data, uses a physical-data dual-driven three-dimensional reconstruction algorithm to build a digital twin slope, and dynamically adjusts the finite element physical simulation boundary conditions according to AI prediction results to achieve high-precision simulation of slope response during rainfall. The intelligent adaptive landslide early warning and suggestion output module, based on TSIM-Net and twin simulation results, integrates Bayesian confidence correction optimization to achieve real-time adaptive adjustment of the early warning threshold, and outputs a comprehensive early warning decision including evacuation guidance, engineering protection measures and monitoring encryption suggestions. It also has a linkage interface function with physical rainfall simulation device.

[0039] Example: The following example uses a high-risk landslide area in a mountainous region. Based on the AI-driven intelligent landslide rainfall simulation system of this invention, combined with multi-source sensor monitoring data (including rainfall, soil moisture content, slope displacement, and surface cracks), multi-factor data fusion, analysis, and dynamic simulation are performed to achieve landslide risk assessment and intelligent early warning.

[0040] 1. Data Collection and Sample Summary 2. Model Analysis and Risk Warning Output The system automatically analyzes the above multi-time-series monitoring data and combines it with historical data on similar events to achieve risk classification and early warning, as detailed below: 3. Beneficial effects of this embodiment Through the above data examples and AI dynamic simulation, the system of this invention can provide early warning of high landslide risk based on real-time and historical data, and achieve precise, graded and automated geological disaster risk prevention and control, effectively protecting the safety of people and property.

[0041] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM).

[0042] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or substitute some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart landslide rainfall simulation method driven by AI historical rainfall data, characterized in that: The method includes: Multidimensional perception and dynamic data integration are constructed by deploying a multidimensional sensor network to collect rainfall intensity, soil moisture content, slope displacement, environmental and geological parameters in real time. The real-time data is dynamically integrated with historical landslide cases, geological survey data, and high-dimensional data from remote sensing images to form a multimodal landslide risk sample library. Adaptive extraction of joint features of topography and rainfall time series: Based on the spatiotemporal interactive mapping network TSIM-Net, a dual-stream model is designed to extract features of topographic and geomorphic spatial features and rainfall time series dynamic response respectively. At the same time, an interactive fusion channel is constructed to capture the coupling effect of slope and rainfall. An adaptive multi-head attention mechanism is introduced into the model structure to realize the dynamic adjustment of the weights of different geological parameters and rainfall processes, highlighting the impact of key disaster-causing factors. To model the causal relationship between rainfall and slope stability, this paper proposes a prior knowledge transfer-causal relationship reinforcement coupling algorithm to address the characteristics of small sample size, heterogeneity, and time-varying nature. This algorithm embeds historically known landslide triggers into the neural network representation layer to achieve deep causal relationship modeling between rainfall and slope stability. Combined with generative adversarial training, this algorithm ensures the model's generalization performance and causal interpretability. Digital twin slope construction and high-precision landslide dynamic simulation: Based on three-dimensional terrain point cloud and real-time monitoring data, a physical-data dual-drive fine three-dimensional reconstruction algorithm is used to build a digital twin slope. AI prediction output is used to dynamically adjust the boundary conditions of finite element physical simulation to achieve high-precision slope response simulation during rainfall. Intelligent adaptive landslide early warning and suggestion output: Design an adaptive early warning decision module for landslide risk. Based on TSIM-Net and twin simulation results, integrate Bayesian confidence correction optimization to achieve real-time adaptive adjustment of the early warning threshold.

2. The intelligent landslide rainfall simulation method based on AI historical rainfall data as described in claim 1, characterized in that: The aforementioned multi-dimensional perception and dynamic data integration construction includes a multi-dimensional intelligent sensor network of various types of hardware devices such as meteorological rain gauges, soil moisture sensors, slope deformation monitors, and surface current meters, enabling real-time and continuous perception of key parameters such as rainfall intensity, soil moisture content, slope displacement, aquifer changes, and groundwater level. The data is preprocessed by an edge computing gateway to perform noise filtering, outlier detection, and data feature extraction before being transmitted back to the central database. Furthermore, by standardizing and formatting the real-time monitoring data and integrating it with multi-source data such as historical landslide cases, geological survey data, remote sensing images, and disaster assessment information, a multi-modal fusion engine is used to dynamically integrate the above data to establish a dynamically updated, structured multi-modal sample library of landslide risks.

3. The intelligent landslide rainfall simulation method based on AI historical rainfall data as described in claim 1, characterized in that: The adaptive extraction of joint features of topography and rainfall time series includes: using the spatiotemporal interaction mapping network TSIM-Net to extract joint features of topographic spatial features and rainfall time series dynamic response from the multimodal landslide risk sample library; Among them, TSIM-Net adopts a dual-stream deep neural network structure, with one spatial stream encoding the spatial characteristics of slope, aspect, elevation undulation, and soil structure based on a three-dimensional convolutional neural network; Another timeline is based on time-series modeling methods to process the temporal characteristics of rainfall intensity, cumulative precipitation, and rainfall pattern distribution. Meanwhile, an interactive fusion channel is set up to achieve the fusion of coupled features of slope and rainfall response; and an adaptive multi-head attention mechanism is introduced into the model structure to dynamically adjust the weights of different geological parameters and rainfall processes in feature representation.

4. The intelligent landslide rainfall simulation method based on AI historical rainfall data as described in claim 1, characterized in that: The aforementioned modeling of the causal relationship between rainfall and slope stability includes: using a prior knowledge transfer and causal relationship reinforcement coupling algorithm to transform known inducing factors and classic geological instability criteria from historical landslide cases into structured knowledge vectors, and then deeply integrating them with multimodal spatiotemporal features through a knowledge injection mechanism in the neural network representation layer to achieve interpretable modeling of the causal relationship between rainfall and slope stability; Meanwhile, by combining the geological constraint adversarial enhancement network, and by introducing geophysical laws and classical stability criteria as generation constraints during the generation training process, the model not only improves its adaptability to boundary extreme samples and time-varying working conditions during the discrimination and generation process, but also ensures that the output slope stability risk assessment results conform to geophysical laws.

5. The intelligent landslide rainfall simulation method based on AI historical rainfall data as described in claim 1, characterized in that: The aforementioned digital twin slope construction and high-precision landslide dynamic simulation acquire high-density three-dimensional terrain point clouds and real-time multi-dimensional monitoring data, combine historical geological profiles and slope structural attributes, and adopt a physical-data dual-drive fine three-dimensional reconstruction algorithm to achieve millimeter-level three-dimensional modeling of slope structure and physical state. The digital twin slope model uses dynamic interpolation, inverse modeling, and physical constraint optimization to couple the terrain point cloud with the monitoring status to generate a high-precision three-dimensional mesh, covering soil and rock types, layered boundaries, fracture parameters, density, cohesion, internal friction angle, water content, and displacement properties. It is linked with the dynamic risk factors or sliding zone determination output by the AI ​​prediction model to automatically adjust the boundary and initial conditions in the finite element physical simulation, so as to realize the hourly dynamic simulation of the slope response and stability during the rainfall process. Meanwhile, it integrates a three-dimensional visualization spatiotemporal evolution module to interactively simulate and render the digital twin slope and its physical state changes, dynamically displaying the evolution of key parameters and catastrophic trends during the landslide process.

6. The intelligent landslide rainfall simulation method based on AI historical rainfall data as described in claim 1, characterized in that: The intelligent adaptive landslide early warning and suggestion output is based on the spatiotemporal coupling characteristics of landslide disasters extracted by TSIM-Net and the output results of digital twin slope simulation. It integrates multi-source risk evidence and introduces the Bayesian confidence correction optimization method. It dynamically adjusts the early warning decision threshold based on real-time monitoring and historical landslide case data to achieve intelligent quantification and real-time response to landslide probability, graded risk, spatial impact range and uncertainty. It automatically outputs comprehensive early warning information, including landslide probability, potential impact area, risk classification, and customized emergency response suggestions. The content includes evacuation guidelines, engineering protection measures, and monitoring encryption suggestions, and it has a linkage interface function with physical rainfall simulation devices.

7. An intelligent landslide rainfall simulation system driven by AI historical rainfall data, wherein the system is applicable to the method as described in any one of claims 1-6, characterized in that: The system includes: 1) Multi-dimensional sensing and dynamic data integration module, used to deploy various types of sensing hardware such as meteorological rain gauges, soil moisture sensors, slope deformation monitoring instruments, and surface current meters, to realize real-time acquisition of rainfall intensity, soil moisture content, slope displacement, environmental and geological parameters, and dynamically integrate the acquired data with historical landslide cases, geological exploration data, and remote sensing imagery high-dimensional data to construct a multi-modal landslide risk sample library; 2) The topographic and geomorphological-rainfall time series joint feature adaptive extraction module adopts the TSIM-Net spatiotemporal interactive mapping network to design a dual-stream model, extracts the spatial features of topographic and geomorphological features and the dynamic response features of rainfall time series, constructs an interactive fusion channel to capture the coupling effect of slope and rainfall, and uses an adaptive multi-head attention mechanism to dynamically adjust the feature weights of different geological parameters and rainfall processes. 3) Rainfall-slope stability causal relationship module: Based on prior knowledge transfer and causal relationship reinforcement coupling algorithm, historical landslide induction knowledge is embedded into the neural network representation layer to realize deep causal modeling between rainfall and slope stability. Generative adversarial training is combined to enhance the generalization and causal interpretability of the model. 4) Digital twin slope construction and high-precision landslide dynamic simulation module: Based on three-dimensional terrain point cloud and real-time monitoring data, a physical-data dual-driven three-dimensional reconstruction algorithm is used to build a digital twin slope, and the boundary conditions of finite element physical simulation are dynamically adjusted according to AI prediction results to achieve high-precision simulation of slope response during rainfall. 5) Intelligent adaptive landslide early warning and suggestion output module: Based on TSIM-Net and twin simulation results, it integrates Bayesian confidence correction optimization to realize real-time adaptive adjustment of early warning threshold and outputs comprehensive early warning decision including evacuation guidance, engineering protection measures and monitoring encryption suggestions. It also has a linkage interface function with physical rainfall simulation device.

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