A landslide movement simulation method based on a motion model

Abstract

The present invention relates to the technical field of landslide movement simulation, and particularly to a landslide movement simulation method based on a movement model. The method includes the following steps: obtaining target landslide area data, generating complete terrain feature data through geomorphic recognition and interpolation completion, performing terrain analysis on this data to identify landslide-prone areas, constructing a three-dimensional model and judging the critical state, calculating risk weights, generating a probability distribution set of landslide-prone areas, performing path analysis to obtain the initial movement trajectory and its probability prediction model, constructing a three-dimensional dynamic simulation scenario and performing iterative analysis, finally generating a landslide movement simulation result, and establishing a dynamic risk warning and emergency response model to improve the prevention and control ability of landslide disasters. The present invention realizes a more accurate and reliable landslide movement simulation method.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide movement simulation, and particularly to a landslide movement simulation method based on a motion model. Background Art

[0002] The occurrence of landslides is often closely related to various factors such as terrain, rainfall, geology, etc. However, there are still many challenges in the prediction and simulation of landslide movement. Current research mainly focuses on the impact assessment after landslides occur, while the dynamic simulation research on the evolution process of landslides is relatively weak. This has led to a lack of scientific basis in the construction of landslide risk management and early warning systems, and it is unable to effectively guide actual emergency responses. Traditional landslide risk assessment methods often rely on static models and lack real-time monitoring of landslide movement trajectories and dynamic changes. Summary of the Invention

[0003] Based on this, it is necessary to provide a landslide movement simulation method based on a motion model to solve at least one of the above technical problems.

[0004] To achieve the above object, a landslide movement simulation method based on a motion model includes the following steps:

[0005] Step S1: Obtain target landslide area data; perform geomorphic recognition on the target landslide area data to obtain landslide area parameters; perform difference filling processing on the landslide area parameters to generate complete terrain feature data;

[0006] Step S2: Perform terrain analysis on the complete terrain feature data to obtain terrain structure data; perform landslide risk analysis on the terrain structure data to obtain landslide-prone areas;

[0007] Step S3: Construct a three-dimensional model for the landslide-prone areas to generate a three-dimensional landslide-prone area model; perform critical state judgment on the three-dimensional landslide-prone area model to obtain the critical state of the landslide area;

[0008] Step S4: Calculate risk weights based on the landslide critical state to generate a probability distribution set for the landslide-prone areas; perform numerical simulation of critical conditions on the probability distribution set for the landslide-prone areas to generate a starting condition data set;

[0009] Step S5: Perform path analysis based on the starting condition data set to obtain an initial motion trajectory set; perform trajectory probability prediction modeling on the initial motion trajectory set to generate a trajectory prediction probability model;

[0010] Step S6: Construct a three-dimensional dynamic simulation scenario based on the trajectory prediction probability model to obtain a visualization model; perform iterative analysis on the visualization model according to the target landslide area data to obtain a landslide movement simulation result; establish a dynamic risk early warning and emergency response model based on the landslide movement simulation result.

[0011] Through high-precision data acquisition and geomorphological recognition of the target landslide area, the present invention can comprehensively obtain the geographical information and topographic parameters of the landslide area. This process ensures the integrity and accuracy of the data, avoiding the risk assessment errors caused by data loss or inaccuracy in traditional methods. In the data acquisition process, efficient means such as remote sensing technology, geographic information system (GIS), and unmanned aerial vehicle survey are combined, which can accurately capture the geomorphological features of the landslide area, providing a solid data foundation for subsequent analysis and ensuring the accuracy and meticulousness of geomorphological recognition. By performing difference filling processing on the landslide area parameters, more complete topographic feature data can be obtained, eliminating the impact of data blank areas on subsequent analysis. In the process of topographic analysis, through in-depth analysis of the complete topographic feature data, detailed topographic structure data can be generated, which further provides a basis for landslide risk analysis. By conducting in-depth analysis of the landslide-prone areas based on the topographic structure data, high-risk areas can be accurately identified. Using modern analysis tools to identify landslide-prone areas further determines the probability of potential landslides, ensuring the accuracy of the determination of landslide-prone areas and thus avoiding the negative impact of misjudged areas on disaster prevention and emergency response. The combination of topographic analysis and landslide risk analysis improves the accuracy of landslide disaster prevention. By constructing a three-dimensional model of the landslide-prone area, the complex terrain and geological conditions of the landslide area can be truly reflected. Using the three-dimensional visualization model, the spatial distribution and potential dangerous areas of the landslide-prone area can be intuitively displayed. Combining critical state judgment to evaluate the safety of the landslide area can effectively identify the critical state of the landslide, predict the specific location and time of landslide occurrence, and provide key decision-making basis for post-disaster rescue and resource allocation. Through this process, the dynamic changes of the landslide area can be understood more scientifically and intuitively. By calculating the risk weight based on the landslide critical state, the risk degree of the landslide-prone area can be quantitatively analyzed, and the calculation results can provide a reliable numerical basis for decision-makers. Further simulating the probability distribution of the landslide-prone area provides scientific data support for subsequent numerical simulation of critical conditions. The numerical simulation of critical conditions can accurately predict the probability and scale of landslides under different conditions, generating a set of initiation conditions, ensuring that the prediction of landslide risk is more in line with the actual environment. The critical condition simulation provides higher reliability and accuracy for landslide risk assessment. Based on the set of initiation conditions, path analysis can obtain the preliminary landslide movement trajectory. Further performing trajectory probability prediction modeling on the initial movement trajectory set can predict the development path of the landslide and the probability of its occurrence. The trajectory prediction probability model provides important decision-making support for disaster response. Based on multi-dimensional factors such as historical data and environmental changes, the model can accurately simulate the path changes of the landslide and further predict the impact range and intensity of the landslide on the surrounding area. By analyzing the trajectory prediction results, the improper disaster prevention and mitigation measures caused by inaccurate path prediction can be effectively avoided. Based on the trajectory prediction probability model, a three-dimensional dynamic simulation scenario is constructed.It can intuitively display the dynamic process of landslide movement and its impact on the surrounding environment. The generated visualization model provides a clear reference basis for the emergency response after a landslide. By iteratively analyzing the visualization model in combination with the data of the target landslide area, the accuracy of the simulation model can be continuously optimized. Through simulation, detailed simulation results of landslide movement can be obtained, so as to provide real-time and accurate early warning information for the dynamic risk early warning system. The dynamic risk early warning and emergency response model established based on the landslide movement simulation results can not only predict the occurrence time and location of landslides, but also monitor the landslide movement in real time, providing scientific and real-time decision-making support for disaster emergency management, ensuring that various disaster prevention and mitigation measures can respond quickly and minimizing the losses caused by landslide disasters.

[0012] Preferably, step S1 includes the following steps:

[0013] Step S11: Obtain the data of the target landslide area;

[0014] Step S12: Identify the topography and geomorphology of the data of the target landslide area to obtain landslide area parameters;

[0015] Step S13: Perform coordinate transformation on the landslide area parameters to generate landslide area coordinate data;

[0016] Step S14: Detect missing points in the landslide area coordinate data to obtain coordinate missing data;

[0017] Step S15: Complement the landslide area coordinate data based on the coordinate missing data to generate complete terrain feature data.

[0018] The present invention ensures comprehensive and accurate data collection of the landslide area by obtaining the data of the target landslide area, acquires basic geographical data, provides necessary original information for subsequent topographic and geomorphic analysis, helps to accurately identify the spatial scope and characteristics of the landslide area, conducts topographic and geomorphic identification on the data of the target landslide area to obtain landslide area parameters, can identify the geological structure, topographic features and other relevant parameters of the landslide area, provides basic data for further analyzing the probability and risk of landslide occurrence, the topographic and geomorphic parameters provide a scientific basis for subsequent landslide risk assessment and prediction, conducts coordinate transformation on the landslide area parameters to generate landslide area coordinate data, ensures the coordination and consistency between different data sources, through coordinate transformation, can unify the spatial information of various data, eliminates the deviation caused by inconsistent data formats, ensures the operability of the landslide area data in different models, conducts missing point detection on the landslide area coordinate data to obtain coordinate missing data, can effectively identify the vacant or incomplete parts in the landslide area data, discovers potential problems in the data, provides a clear direction for subsequent data supplementation and optimization, through missing point detection, can improve the quality of the landslide area data, conducts difference filling on the landslide area coordinate data based on the coordinate missing data to generate complete topographic feature data, solves the analysis problems caused by missing data, the difference filling process effectively fills the blank areas in the data, ensures the integrity of the topographic feature data, improves the accuracy and reliability of the landslide area model, makes the landslide movement simulation more real and reliable, and ensures the accuracy of subsequent analysis results.

[0019] Preferably, step S2 includes the following steps:

[0020] Step S21: Conduct topographic slope analysis on the complete topographic feature data to obtain topographic slope data;

[0021] Step S22: Conduct topographic aspect analysis on the complete topographic feature data to obtain topographic aspect data;

[0022] Step S23: Conduct topographic integration on the topographic slope data and the topographic aspect data to generate topographic structure data;

[0023] Step S24: Conduct regional overlay on the topographic structure data to generate a topographic overlay area;

[0024] Step S25: Conduct landslide risk analysis on the topographic overlay area to obtain landslide-prone areas.

[0025] By analyzing the terrain slope of the complete terrain feature data, the present invention obtains terrain slope data, which can effectively reveal the slope characteristics of the landslide area, identify the steep and gentle slope areas in the landslide area, and provide a scientific basis for the stability analysis of the landslide. The slope data provides important parameters for the subsequent landslide risk assessment, helps to comprehensively understand the terrain undulation of the landslide area, analyzes the terrain aspect of the complete terrain feature data to obtain terrain aspect data, and by analyzing the aspect data, the main aspect direction and its changes in the landslide area can be identified, providing spatial information on the influence of different aspects on the landslide. The aspect analysis provides a key basis for further evaluating the potential direction and influence range of landslide occurrence. Integrating the terrain slope data and terrain aspect data to generate terrain structure data, combining the slope and aspect data, comprehensively reflects the terrain structure characteristics of the landslide area, forms a more detailed and comprehensive terrain structure data, provides more accurate basic data for the subsequent landslide risk analysis, helps to reveal the occurrence mechanism and potential influence area of the landslide, performing regional overlay on the terrain structure data to generate a terrain overlay area, which can overlay different terrain features together to form a multi-level and multi-dimensional terrain data set. By overlaying different terrain information, the geographical structure of the landslide area can be understood more clearly. The overlay area provides a comprehensive perspective, which helps to more accurately divide the prone areas and their ranges during landslide analysis. Conducting landslide risk analysis on the terrain overlay area to obtain landslide-prone areas, combining terrain features and landslide risk factors to identify high-risk areas of landslide occurrence, accurately demarcating landslide-prone areas, providing spatial distribution information of high-risk areas, and providing data support for landslide prevention and mitigation work. The identification of landslide-prone areas is of great significance for subsequent landslide movement simulation and emergency response plan formulation.

[0026] Preferably, step S3 includes the following steps:

[0027] Step S31: Analyze the stability of the landslide-prone area to obtain stability data;

[0028] Step S32: Based on the stability data, construct a three-dimensional model of the landslide-prone area to generate a three-dimensional landslide-prone area model;

[0029] Step S33: Conduct static mechanical equilibrium analysis on the three-dimensional landslide-prone area model to obtain static equilibrium data;

[0030] Step S34: Judge the critical state of the static equilibrium data to obtain the critical state of the landslide area.

[0031] Through the stability analysis of landslide-prone areas, stability data are obtained. By analyzing the stability of landslide-prone areas, the landslide potential of each area can be accurately evaluated, and the key areas prone to landslides can be identified. The stability analysis provides data on the landslide resistance ability of the area, laying a solid foundation for subsequent model construction and landslide risk assessment. Based on the stability data, a three-dimensional model of the landslide-prone area is constructed to generate a three-dimensional landslide-prone area model. Using the stability data for three-dimensional modeling can truly reflect the three-dimensional spatial characteristics of the landslide area, accurately display the terrain changes and the specific shape of the landslide area. Through the three-dimensional model, the terrain deformation process during landslide occurrence can be simulated, and the generated three-dimensional landslide model provides a specific spatial structure for subsequent dynamic simulation. The static mechanical equilibrium analysis of the three-dimensional landslide-prone area model is carried out to obtain static equilibrium data. The static mechanical equilibrium analysis can consider various mechanical factors in the landslide area in the model and perform equilibrium calculations of physical conditions, so as to evaluate the stability of the area under different mechanical actions. The static equilibrium data helps to determine the safety factor of the landslide area, providing a basis for critical state judgment. The critical state judgment of the static equilibrium data is carried out to obtain the critical state of the landslide area. Through the analysis and judgment of the static equilibrium data, it is possible to accurately determine whether the landslide area is in a critical state, clarify the critical conditions and timing of landslide occurrence. The critical state judgment provides a theoretical basis for subsequent risk assessment and emergency response measures, clarifies the specific area and state of landslide occurrence, and improves the effectiveness of landslide disaster warning.

[0032] Preferably, step S4 includes the following steps:

[0033] Step S41: Calculate the risk weight based on the landslide critical state to obtain risk probability density data;

[0034] Step S42: Conduct cluster analysis on the risk probability density data to generate a probability distribution set of landslide-prone areas;

[0035] Step S43: Conduct stress field analysis on the probability distribution set of landslide-prone areas to obtain the stress gradient tensor;

[0036] Step S44: Perform numerical simulation of critical conditions according to the stress gradient tensor to generate a dataset of initiation conditions.

[0037] The present invention calculates risk weight based on the critical state of landslides to obtain risk probability density data. Through a detailed analysis of the critical state of the landslide area, the landslide risks in each area are quantitatively evaluated. The calculated risk probability density data reflects the occurrence probability of landslides in different areas. The accurate calculation of the data provides a reliable basis for the subsequent division and dynamic simulation of landslide-prone areas. Cluster analysis is performed on the risk probability density data to generate a probability distribution set of landslide-prone areas. Cluster analysis helps to identify similarities within different areas. By classifying the risk probability density data, the landslide-prone areas can be accurately divided. The generated probability distribution set reflects the risk levels and occurrence probabilities of each area, ensuring a more scientific delineation of landslide-prone areas. Stress field analysis is performed on the probability distribution set of landslide-prone areas to obtain the stress gradient tensor. Stress field analysis can calculate the stress distribution within the area based on the geological and physical characteristics of the landslide-prone area. Through the stress field analysis of the probability distribution set, the stress gradient tensor is obtained. This data is crucial for judging the timing and intensity of landslide occurrence. Numerical simulation of critical conditions is carried out according to the stress gradient tensor to generate a dataset of initiation conditions. The stress gradient tensor is used to numerically simulate the critical conditions of the landslide area. The simulation results can reveal the specific conditions for landslide occurrence. The generated dataset of initiation conditions provides a scientific basis for the initiation timing and triggering mechanism of landslide movement, ensuring high timeliness and accuracy in landslide simulation and prediction.

[0038] Preferably, step S5 includes the following steps:

[0039] Step S51: Construct a kinematic model for the dataset of initiation conditions to generate a kinematic model;

[0040] Step S52: Conduct path analysis on the kinematic model to obtain an initial set of movement trajectories;

[0041] Step S53: Perform terrain constraint processing based on the initial set of movement trajectories to obtain a corrected set of movement trajectories;

[0042] Step S54: Build a trajectory probability prediction model for the corrected set of movement trajectories to generate a trajectory prediction probability model.

[0043] The present invention constructs a kinematic model by constructing a kinematic model for the starting condition data set, generates a kinematic model, constructs an accurate kinematic model based on the starting condition data of the landslide area through kinematic principles, simulates the movement behavior of the landslide body, and the obtained kinematic model can reflect the dynamic characteristics of the landslide movement. Further, it provides a mathematical basis for landslide path prediction and trajectory analysis. By performing path analysis on the kinematic model, an initial movement trajectory set is obtained. Based on the constructed kinematic model, the initial movement trajectory of the landslide body is predicted through path analysis. The result of the path analysis can reveal the path direction and movement range of the landslide movement. The generated initial movement trajectory set provides key preliminary data for landslide prediction. Based on the initial movement trajectory set, terrain constraint processing is performed to obtain a corrected movement trajectory set. Combining the terrain characteristics of the landslide area, the initial movement trajectory is constrained. The terrain characteristics play a key role in the trajectory correction process. Through the constraint processing, the real movement path of the landslide body under specific terrain conditions can be more accurately reflected, and the obtained corrected movement trajectory set is more in line with the actual situation. By performing trajectory probability prediction modeling on the corrected movement trajectory set, a trajectory prediction probability model is generated. By performing probability prediction modeling on the corrected movement trajectory set, the probability of the landslide occurrence and the trajectory trend can be evaluated. The trajectory prediction probability model can provide multiple landslide paths and their occurrence probabilities, providing key data support for subsequent risk assessment and early warning systems, and ensuring that the landslide simulation has a high prediction accuracy.

[0044] Preferably, step S6 includes the following steps:

[0045] Step S61: Construct a three-dimensional dynamic simulation scene based on the trajectory prediction probability model to obtain a visualization model;

[0046] Step S62: Perform a comparative analysis on the visualization model and the target landslide area data to obtain comparative difference data;

[0047] Step S63: Perform iterative analysis on the visualization model according to the comparative difference data to obtain a landslide movement simulation result;

[0048] Step S64: Establish a dynamic risk warning and emergency response model based on the landslide movement simulation result.

[0049] The present invention constructs a three-dimensional dynamic simulation scenario based on a trajectory prediction probability model to obtain a visualization model. Using the trajectory prediction probability model, a three-dimensional dynamic simulation scenario matching the terrain and landform of the landslide area is constructed. Through three-dimensional modeling, the movement path and process of the landslide can be intuitively displayed. The generated visualization model not only provides a clear dynamic display of the landslide, but also supports subsequent analysis and prediction. By comparing and analyzing the visualization model and the data of the target landslide area, comparative difference data is obtained. Combining the terrain data of the actual landslide area with the three-dimensional dynamic simulation scenario for comparative analysis, the differences between the model and the actual area data are identified, and comparative difference data is obtained. These difference data can reveal the adaptability of the model to the actual landslide area in terms of terrain, climate, rock and soil properties, etc., providing a clear direction for model optimization. Through iterative analysis of the visualization model based on the comparative difference data, the landslide movement simulation results are obtained. Based on the comparative difference data, the three-dimensional visualization model is iteratively optimized. After multiple rounds of analysis and adjustment, accurate landslide movement simulation results are finally obtained. Through iterative analysis, the real-time changes in the landslide area can be reflected, ensuring that the simulation results can fit the actual landslide occurrence process. Based on the landslide movement simulation results, a dynamic risk warning and emergency response model is established. Using the landslide movement simulation results, a dynamic risk warning system and an emergency response model are constructed. By simulating the landslide movement trajectory and process, potential risks can be evaluated in real time, and scientific basis can be provided for emergency response decision-making. The generated dynamic risk warning and emergency response model ensures the timely discovery and effective response to landslide disasters, and improves the prevention and emergency response capabilities of landslide disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the step flow of a landslide movement simulation method based on a motion model;

[0051] Figure 2 is Figure 1 a detailed implementation step flow diagram of step S2 in

[0052] Figure 3 is Figure 1 a detailed implementation step flow diagram of step S3 in

[0053] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0055] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0056] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0057] To achieve the above object, please refer to Figures 1 to 3 , a landslide movement simulation method based on a motion model, comprising the following steps:

[0058] Step S1: Obtain target landslide area data; perform geomorphic identification on the target landslide area data to obtain landslide area parameters; perform difference filling processing on the landslide area parameters to generate complete terrain feature data;

[0059] Step S2: Perform terrain analysis on the complete terrain feature data to obtain terrain structure data; perform landslide risk analysis on the terrain structure data to obtain landslide-prone areas;

[0060] Step S3: Construct a three-dimensional model for the landslide-prone areas to generate a three-dimensional landslide-prone area model; perform a critical state judgment on the three-dimensional landslide-prone area model to obtain the critical state of the landslide area;

[0061] Step S4: Calculate risk weights based on the landslide critical state to generate a probability distribution set for the landslide-prone areas; perform numerical simulation of critical conditions on the probability distribution set for the landslide-prone areas to generate a set of starting condition data;

[0062] Step S5: Perform path analysis based on the set of starting condition data to obtain a set of initial movement trajectories; perform trajectory probability prediction modeling on the set of initial movement trajectories to generate a trajectory prediction probability model;

[0063] Step S6: Construct a three-dimensional dynamic simulation scenario based on the trajectory prediction probability model to obtain a visualization model; perform iterative analysis on the visualization model according to the target landslide area data to obtain the landslide movement simulation results; establish a dynamic risk warning and emergency response model based on the landslide movement simulation results.

[0064] Through high-precision data acquisition and geomorphological recognition of the target landslide area, the present invention can comprehensively obtain the geographical information and topographic parameters of the landslide area. This process ensures the integrity and accuracy of the data, avoiding the risk assessment errors caused by data missing or inaccurate in traditional methods. In the data acquisition process, efficient means such as remote sensing technology, geographic information system (GIS), and unmanned aerial vehicle measurement are combined, which can accurately capture the geomorphological features of the landslide area, providing a solid data foundation for subsequent analysis and ensuring the accuracy and meticulousness of geomorphological recognition. By performing difference filling processing on the landslide area parameters, more complete topographic feature data can be obtained, eliminating the impact of data blank areas on subsequent analysis. In the process of topographic analysis, through in-depth analysis of the complete topographic feature data, detailed topographic structure data can be generated, thus providing a basis for landslide risk analysis. By conducting in-depth analysis of the landslide-prone areas based on the topographic structure data, high-risk areas can be accurately identified. Using modern analysis tools to identify the landslide-prone areas can further determine the probability of potential landslides occurring, ensuring the accuracy of the determination of landslide-prone areas, thereby avoiding the negative impacts of misjudged areas on disaster prevention and emergency response. The combination of topographic analysis and landslide risk analysis improves the accuracy of landslide disaster prevention. By constructing a three-dimensional model of the landslide-prone area, the complex topography and geological conditions of the landslide area can be truly reflected. Using the three-dimensional visualization model, the spatial distribution and potential dangerous areas of the landslide-prone area can be intuitively displayed. Combining critical state judgment to evaluate the safety of the landslide area can effectively identify the critical state of the landslide, predict the specific location and time of the landslide occurrence, and provide key decision-making basis for post-disaster rescue and resource allocation. Through this process, the dynamic changes of the landslide area can be understood more scientifically and intuitively. By calculating the risk weights based on the landslide critical state, the risk degree of the landslide-prone area can be quantitatively analyzed, and the calculation results can provide reliable numerical basis for decision-makers. Further simulating the probability distribution of the landslide-prone area can provide scientific data support for subsequent numerical simulation of critical conditions. The numerical simulation of critical conditions can accurately predict the probability and scale of landslides occurring under different conditions, generating a set of starting conditions, ensuring that the prediction of landslide risk is more in line with the actual environment. The critical condition simulation provides higher reliability and accuracy for landslide risk assessment. Based on the set of starting conditions, path analysis can obtain the preliminary landslide movement trajectory. Further performing trajectory probability prediction modeling on the initial movement trajectory set can predict the development path of the landslide and the probability of its occurrence. The trajectory prediction probability model provides important decision-making support for disaster response. Based on multi-dimensional factors such as historical data and environmental changes, the model can accurately simulate the path changes of the landslide and further predict the influence range and intensity of the landslide on the surrounding areas. By analyzing the trajectory prediction results, the improper disaster prevention and mitigation measures caused by inaccurate path prediction can be effectively avoided. Based on the trajectory prediction probability model, a three-dimensional dynamic simulation scenario is constructed.It can intuitively display the dynamic process of landslide movement and its impact on the surrounding environment. The generated visualization model provides a clear reference for emergency response after the landslide occurs. Iterative analysis of the visualization model combined with the target landslide area data can continuously optimize the accuracy of the simulation model. Through simulation, detailed simulation results of landslide movement are obtained, thereby providing real-time and accurate warning information for the dynamic risk warning system. The dynamic risk warning and emergency response model established based on the landslide movement simulation results can not only predict the time and location of landslides, but also monitor the landslide movement in real time, providing scientific and real-time decision support for disaster emergency management, ensuring that various disaster prevention and mitigation measures can respond quickly and minimize the losses caused by landslide disasters.

[0065] In the embodiment of the present invention, refer to Figure 1 , is a schematic diagram of the steps of a landslide motion simulation method based on a motion model of the present invention. In this example, the landslide motion simulation method based on a motion model includes the following steps:

[0066] Step S1: acquiring target landslide area data; performing landform recognition on the target landslide area data to obtain landslide area parameters; performing difference completion processing on the landslide area parameters to generate complete terrain feature data;

[0067] In this embodiment, the target landslide area data is obtained; the target landslide area data is subjected to geomorphic identification to obtain the landslide area parameters; the landslide area parameters are subjected to difference completion processing to generate complete terrain feature data. First, the terrain data of the target landslide area is obtained through remote sensing technology or ground measurement instruments (such as drones, laser scanners), including surface elevation, slope, slope direction and other information, and then the data is preliminarily processed using a geographic information system (GIS), and the geomorphic features of the area are identified to obtain the key parameters of each landslide area, such as slope value, soil type, geological structure, etc., and the missing or incomplete regional data are supplemented by difference through interpolation algorithms (such as Kriging interpolation or nearest neighbor interpolation), thereby generating a complete set of landslide area terrain feature data, which covers the overall picture of landslide-prone areas and provides a basis for subsequent analysis.

[0068] Step S2: Perform terrain analysis on the complete terrain feature data to obtain terrain structure data; perform landslide risk analysis on the terrain structure data to obtain landslide prone areas;

[0069] In this embodiment, terrain analysis is performed on the complete terrain feature data to obtain terrain structure data; landslide risk analysis is performed on the terrain structure data to obtain landslide-prone areas. By analyzing the acquired complete terrain feature data and using terrain analysis tools such as ArcGIS or QGIS, first slope analysis is carried out to evaluate the slope values of each area, and potential landslide areas are screened according to the slope values. Then aspect analysis is carried out to identify the water flow paths and precipitation convergence points in the landslide areas. By comprehensively analyzing factors such as slope, aspect, soil type, and groundwater level, through landslide risk assessment models such as factor analysis method and logistic regression model, the risks in the landslide areas are calculated, so as to generate a landslide-prone area map, marking the areas with higher risks, which is convenient for subsequent in-depth analysis and prediction.

[0070] Step S3: Construct a 3D model for the landslide-prone areas to generate a 3D landslide-prone area model; judge the critical state of the 3D landslide-prone area model to obtain the critical state of the landslide area;

[0071] In this embodiment, a 3D model is constructed for the landslide-prone areas to generate a 3D landslide-prone area model; judge the critical state of the 3D landslide-prone area model to obtain the critical state of the landslide area. Use 3D modeling software (such as AutoCAD, Civil 3D) to process the data of the landslide-prone areas, convert the 2D data into a 3D digital model, and generate a complete 3D landslide-prone area model through model rendering technology. This model can truly reflect the 3D spatial characteristics and local structures of the landslide areas. Static mechanical equilibrium analysis is carried out through the mechanical parameters (such as soil strength, rock strength, etc.) in the 3D model, and the model is analyzed using the limit equilibrium method or the finite element method. According to the analysis results, judge whether the landslide area is in a critical state. The basis for critical state judgment is the critical conditions for landslide occurrence, such as slope, soil friction coefficient, etc., to determine whether there is a potential risk of landslide.

[0072] Step S4: Calculate the risk weights based on the landslide critical state to generate a probability distribution set of the landslide-prone areas; perform numerical simulation of the critical conditions on the probability distribution set of the landslide-prone areas to generate a dataset of initiation conditions;

[0073] In this embodiment, based on the critical state of the landslide, the risk weights are calculated to generate a probability distribution set of landslide-prone areas; a numerical simulation of the critical conditions is performed on the probability distribution set of landslide-prone areas to generate a dataset of initiation conditions. According to the foregoing judgment results of the critical state, using a risk weight calculation method (such as the multi-factor comprehensive scoring method), different weights are assigned to each parameter (slope, soil type, geological structure, etc.) in the landslide-prone area, and the probability of landslide occurrence in each area is calculated, thereby generating a complete probability distribution set of landslide-prone areas. Then, based on this distribution set, a critical condition simulation is carried out through a numerical simulation method (such as the finite element method or the discrete element method), and the triggering conditions of each type of landslide are simulated to obtain a dataset of initiation conditions, which contains the initiation risk information of each landslide area under specific conditions.

[0074] Step S5: Perform path analysis based on the dataset of initiation conditions to obtain a set of initial movement trajectories; perform trajectory probability prediction modeling on the set of initial movement trajectories to generate a trajectory prediction probability model.

[0075] In this embodiment, based on the dataset of initiation conditions, path analysis is performed to obtain a set of initial movement trajectories; trajectory probability prediction modeling is performed on the set of initial movement trajectories to generate a trajectory prediction probability model. According to the dataset of initiation conditions, the kinematic principle (such as Newton's Laws of Motion) is used to analyze the movement path of the landslide, and a path analysis tool (such as the path analysis module of ArcGIS) is used to simulate the movement process of the landslide from the starting point to the ending point to obtain a preliminary dataset of movement trajectories. Through a large number of historical landslide case data and existing landslide prediction models, based on methods such as Bayesian networks and Monte Carlo simulations, probability prediction is performed on the set of initial movement trajectories, a trajectory prediction probability model is constructed, the occurrence probability of various trajectories is generated, and the occurrence probability and risk level are evaluated.

[0076] Step S6: Construct a three-dimensional dynamic simulation scenario based on the trajectory prediction probability model to obtain a visualization model; perform iterative analysis on the visualization model according to the data of the target landslide area to obtain the landslide movement simulation result; establish a dynamic risk early warning and emergency response model based on the landslide movement simulation result.

[0077] In this embodiment, a three-dimensional dynamic simulation scenario is constructed based on a trajectory prediction probability model to obtain a visualization model; the visualization model is iteratively analyzed according to the data of the target landslide area to obtain a landslide movement simulation result; a dynamic risk warning and emergency response model is established based on the landslide movement simulation result. Using the trajectory prediction probability model, a dynamic simulation scenario is constructed in combination with 3D modeling software (such as Blender, Unity3D), and the movement trajectory and potential path of the landslide are displayed through a dynamic model to obtain a visualization model. Further, the model is iteratively optimized by combining the actual data of the target landslide area, and the landslide movement simulation result is analyzed, including information such as the movement path, time distribution, and landslide speed. According to the landslide movement simulation result, an emergency response model (such as a warning model based on GIS) is used to establish a dynamic risk warning system, which can monitor the movement state of the landslide area in real time and issue an alarm in the early stage of the landslide, providing real-time response measures for disaster prevention and mitigation.

[0078] Preferably, step S1 includes the following steps:

[0079] Step S11: Obtain the data of the target landslide area;

[0080] Step S12: Identify the topography and geomorphology of the data of the target landslide area to obtain landslide area parameters;

[0081] Step S13: Perform coordinate transformation on the landslide area parameters to generate landslide area coordinate data;

[0082] Step S14: Detect missing points in the landslide area coordinate data to obtain coordinate missing data;

[0083] Step S15: Perform difference filling on the landslide area coordinate data based on the coordinate missing data to generate complete terrain feature data.

[0084] In this embodiment, data of the target landslide area is obtained. First, surface data of the target landslide area is obtained through remote sensing technology or a lidar (LiDAR) system carried by an unmanned aerial vehicle. Remote sensing data such as multispectral images and radar images are used to comprehensively scan the landslide area to obtain information such as elevation, slope, aspect, and vegetation cover of the area. In cooperation with on-site measured data, GPS (Global Positioning System) devices are used for precise positioning on-site. Through remote sensing image registration technology (such as image registration methods based on cross-correlation or feature matching), image data from different times and different sources are uniformly processed to ensure that the accuracy of the obtained area data meets the requirements, so as to provide reliable basic data for subsequent topographic and geomorphic analysis. The data of the target landslide area is subjected to topographic and geomorphic recognition to obtain landslide area parameters. The obtained data of the target landslide area is analyzed and processed through a geographic information system (GIS). The slope analysis tool (such as the slope tool in ArcGIS) is used to analyze the slope of the terrain to identify areas with large ground inclination, which are often potential locations for landslides. Then, a geomorphic classification algorithm (such as the K-means clustering algorithm) is used to classify the topographic and geomorphic types to identify different geological units. Combining information such as soil type and rock layer structure in the area, area parameters prone to landslides, such as elevation difference, slope change, and soil stability, are extracted through spatial analysis tools (such as a spatial analysis toolset). These parameters will provide important bases for subsequent landslide risk assessment and movement simulation. The coordinates of the landslide area parameters are converted to generate landslide area coordinate data. According to the obtained landslide area parameters, the coordinate transformation algorithm is used to convert the obtained geographic coordinate system data (such as the WGS84 coordinate system) into the required projected coordinate system (such as the UTM projected coordinate system). An accurate transformation formula or the built-in coordinate transformation tool in the geographic information system (GIS) (such as the "Projection and Transformation" tool in ArcGIS) is adopted, and the coordinate transformation and storage of the data are realized through a geospatial database (such as PostGIS) to ensure that the converted coordinate data meets the requirements of subsequent 3D modeling and analysis. During the coordinate transformation process, the errors occurring during the transformation also need to be corrected to ensure the accuracy of the results. The obtained landslide area coordinate data is subjected to missing point detection to obtain coordinate missing data.By performing quality control on the converted landslide area coordinate data, comprehensively scanning the coordinate data using a missing data detection algorithm (such as an interpolation-based missing point detection method), identifying areas with missing values or data anomalies, evaluating the accuracy and integrity of the coordinate data using statistical analysis methods (such as standard deviation analysis or extreme value detection), marking points with missing or inaccurate data. Specifically, when operating, spatial analysis tools (such as spatial autocorrelation analysis) in a geographic information system (GIS) can be used to perform spatial distribution analysis on the geographic data within the area, locate the spatial positions of the missing data points, ensure the accuracy of the detected missing point data, and provide a basis for subsequent interpolation and completion. Based on the coordinate missing data, interpolate and complete the landslide area coordinate data to generate complete terrain feature data. Use spatial interpolation methods (such as Kriging interpolation method, inverse distance weighted method) to complete the missing coordinate data. First, select an appropriate interpolation algorithm, calculate the coordinate values of the missing points according to the spatial distribution of the known data points, and through interpolation processing, fill the missing data points into the complete coordinate system to ensure that the completed data can truly reflect the terrain features of the area. Further, use error evaluation methods (such as cross-validation or residual analysis) to perform quality detection on the completed data, confirm the accuracy of the completion result, and finally generate complete terrain feature data, and store and manage it for subsequent landslide risk analysis, model construction, and simulation.

[0085] Preferably, step S2 includes the following steps:

[0086] Step S21: Perform terrain slope analysis on the complete terrain feature data to obtain terrain slope data;

[0087] Step S22: Perform terrain aspect analysis on the complete terrain feature data to obtain terrain aspect data;

[0088] Step S23: Integrate the terrain slope data and the terrain aspect data to generate terrain structure data;

[0089] Step S24: Perform regional overlay on the terrain structure data to generate a terrain overlay area;

[0090] Step S25: Perform landslide risk analysis on the terrain overlay area to obtain landslide-prone areas.

[0091] In this embodiment, terrain slope analysis is performed on the complete terrain feature data to obtain terrain slope data. Using GIS (Geographic Information System) software, such as ArcGIS or QGIS, the complete terrain feature data is imported into the system. The terrain slope calculation tool is used to perform slope analysis on the area, and the slope value of each geographical unit is calculated. A suitable terrain data source, such as DEM (Digital Elevation Model), is selected, and the calculation window size (such as 3×3, 5×5 window) is set. The rasterization algorithm is applied to calculate the slope value of each raster cell. Using common slope calculation formulas, such as the derivative method based on trigonometric functions or the elevation difference method, the slope value of each pixel is obtained. The result is a new slope raster data set, representing the terrain slope of each point in the area. The entire analysis process needs to ensure the consistency of spatial resolution and smooth the output results to eliminate errors caused by data noise or outliers. Terrain aspect analysis is performed on the complete terrain feature data to obtain terrain aspect data. Through a process similar to slope analysis, the GIS tool is used to perform aspect analysis on the terrain in the area. First, the elevation data (such as DEM) is imported, the ground measurement points and areas are selected, and the aspect calculation tool (such as the "Aspect" tool in ArcGIS) is used to calculate the aspect angle of each raster cell according to the elevation value of the target area. The algorithm of selecting the maximum gradient direction from 8 neighboring pixels is adopted to obtain the aspect data of each pixel. The aspect calculation result is expressed in degrees, usually with north as 0°, and the clockwise direction is positive. The result is a new aspect data set, describing the slope orientation of each position in the area, and further checking and correcting the result through the spatial analysis function of GIS to ensure that the aspect data of each raster cell conforms to the terrain features. Special attention will be paid to the continuity and accuracy of the data during the processing to avoid the occurrence of faults or outliers. Terrain integration is performed on the terrain slope data and terrain aspect data to generate terrain structure data. Using the built-in spatial analysis tool in GIS software, the slope data and aspect data are superimposed and analyzed. A suitable integration algorithm (such as the raster-based weighted merging method) is selected, and weighted merging processing is performed by combining the values of slope and aspect. A comprehensive terrain structure data set is generated. In the analysis, the merging strategy can be refined by adjusting the weight coefficients (such as setting the relative importance of slope and aspect), and an integrated raster data is obtained, where each pixel contains two parameters of slope and aspect, forming a structure data set containing complete terrain features, ensuring that the integrated data can comprehensively reflect the terrain complexity of the area and providing a sufficient geographical information basis for subsequent landslide risk analysis. Through the spatial overlay analysis function in GIS, the terrain structure data is overlaid with other relevant geographical information (such as soil type, vegetation cover, hydrological conditions, etc.). A suitable area range is selected for analysis, and the raster overlay method is adopted to overlay multiple raster data sets pixel by pixel according to the geographical spatial position to obtain a new data set containing all necessary geographical information.During the overlay process, the weighted average method can be applied to calculate the weighted values of the attribute values of each pixel according to the influence weights of different factors, generating a comprehensive terrain overlay area data. Further, the overlay results are classified through cluster analysis (such as K-means clustering) to identify potential landslide-prone areas and generate corresponding risk area data, ensuring that the final terrain overlay area accurately reflects the interaction of different geographical factors and providing a reliable data basis for the identification and risk assessment of landslide-prone areas. Using the terrain overlay area data, a landslide risk assessment model (such as the analytic hierarchy process, logistic regression model or multi-factor weighted model) is adopted to analyze the landslide-prone areas. First, the key influencing factors of landslide occurrence (such as slope, aspect, soil moisture, vegetation cover, etc.) are selected, and spatial analysis of these factors is carried out using GIS to calculate the contribution degree of each factor in the terrain overlay area. Combining with the historical data of the known landslide occurrence areas, the risk level of the landslide-prone areas is generated through comparative analysis. Different weights are assigned to different factors using the spatial weighting method, and the influence of each factor is comprehensively analyzed. The landslide risk index is obtained through model calculation, and finally a risk level division map is generated to mark the landslide-prone areas. These areas provide basic data for subsequent landslide movement simulation, ensuring that the landslide risk assessment results have high accuracy and operability.

[0092] Preferably, step S3 includes the following steps:

[0093] Step S31: Conduct a stability analysis on the landslide-prone area to obtain stability data;

[0094] Step S32: Based on the stability data, construct a three-dimensional model of the landslide-prone area to generate a three-dimensional landslide-prone area model;

[0095] Step S33: Conduct a static mechanical equilibrium analysis on the three-dimensional landslide-prone area model to obtain static equilibrium data;

[0096] Step S34: Judge the critical state of the static equilibrium data to obtain the critical state of the landslide area.

[0097] In this embodiment, geographical data of landslide-prone areas (such as slope, soil type, vegetation cover, etc.) are imported into a GIS (Geographic Information System) platform, and various types of data are spatially superimposed through raster analysis tools to generate a comprehensive stability assessment data set. Common stability analysis models (such as the limit equilibrium method or the finite element method) are used to evaluate the stability of landslide-prone areas. Appropriate physical property parameters (such as soil friction angle, cohesion, gravitational acceleration, etc.) are selected, and numerical simulation is used to evaluate the stability of different points in the area, obtaining the stability index of each grid cell. Further, according to the different characteristics of the landslide-prone area, the stability results are classified, and areas with different stability levels are marked, finally generating stability data to ensure that the comprehensive influence of different factors is considered in the analysis process for accurate assessment of potential risks in the area. By importing the stability data, a three-dimensional model of the landslide-prone area is built using three-dimensional modeling software (such as AutoCAD, SolidWorks or ANSYS). First, the geographical boundaries of the area are defined, and detailed modeling of terrain features such as different levels, soil types and slopes within the area is carried out based on the stability data to construct a three-dimensional terrain model of the area. The stability state of each point is determined through numerical simulation, and the area is discretized using meshing technology, and the stability data of each discrete point are assigned to the corresponding grid cell to form a three-dimensional grid model containing stability information, combining the geographical location and elevation data of the area to accurately present the three-dimensional structure of the landslide-prone area and ensure the accuracy and spatial consistency of the model. A static mechanical analysis tool (such as ABAQUS, FLAC3D, etc.) is used to perform a mechanical equilibrium analysis on the three-dimensional landslide-prone area model. First, reasonable boundary conditions and load conditions are set according to factors such as the terrain characteristics, soil properties, and rock layer structure of the area, the three-dimensional model is input into the mechanical analysis software, an appropriate material model (such as elastic model, plastic model, etc.) is selected, external pressure and gravity are applied to each grid cell, and a static mechanical equilibrium analysis is carried out to calculate data such as stress, strain, and displacement of each point in the area, generating a static equilibrium data set to ensure that the analysis results fully reflect the mechanical characteristics under the actual terrain and physical environment. Further, the mechanical equilibrium state is checked to identify the inducements of landslides, ensuring that the calculation results are accurate and in line with the actual geological environment, and a critical state judgment is made on the static equilibrium data to obtain the critical state of the landslide area.By analyzing the static equilibrium data and using a critical state model (such as the Swiss mountain engineering stability analysis method or the Mohr-Coulomb critical condition method) to judge the critical state of landslides. First, set the critical instability criterion, calculate the safety factor of each grid cell based on the static equilibrium data of the region, select a suitable critical judgment method (such as the limit equilibrium method or the stability analysis method), classify the region according to the safety factor, identify the critical state of the landslide-prone area, generate a critical state map of the landslide area, combine the critical state judgment with the stability data to obtain the potential sliding hazard area of the landslide area, analyze whether the critical state of each area reaches the landslide trigger condition, and obtain the final critical state data through step-by-step analysis, providing detailed data support for subsequent landslide simulations to ensure that the judgment of the critical state is strictly based on actual data and scientific models.

[0098] Preferably, step S4 includes the following steps:

[0099] Step S41: Calculate the risk weight based on the critical state of the landslide to obtain the risk probability density data;

[0100] Step S42: Conduct a cluster analysis on the risk probability density data to generate a probability distribution set of landslide-prone areas;

[0101] Step S43: Conduct a stress field analysis on the probability distribution set of landslide-prone areas to obtain the stress gradient tensor;

[0102] Step S44: Conduct a numerical simulation of the critical conditions based on the stress gradient tensor to generate a dataset of activation conditions.

[0103] In this embodiment, risk weight calculation is performed based on the critical state of landslides to obtain risk probability density data. First, import the dataset of the critical state of landslides, combine multi-source data such as geology, meteorology, and historical landslide events, and use multi-factor risk assessment models (such as the weighted average method, the analytic hierarchy process, etc.) to calculate the risk weights of each landslide area. According to the stability data, critical state data, and other external factors (such as rainfall, earthquake, etc.) of the area, different risk weights are assigned to each area to ensure that the risk weight of each area reflects the probability of landslide occurrence. By statistically analyzing the historical landslide occurrence data and combining the landslide susceptibility assessment results of the area, the risk weights of each grid cell are obtained. Subsequently, probability density analysis is performed on these risk weights to generate the risk probability density data of each area. Cluster analysis is performed on the risk probability density data to generate the probability distribution set of landslide-prone areas. Apply clustering algorithms (such as the K-means clustering method, DBSCAN, etc.) to the risk probability density data, group the data through the clustering algorithm, and identify areas with different risk levels. First, standardize the risk probability density data, convert the risk values of each area into standard values and then input them into the clustering algorithm. According to the preset clustering criteria, areas with similar risk values are grouped into one category, so as to obtain the risk levels and probability distributions of multiple landslide-prone areas. Each clustered area represents a specific landslide risk area. After cluster analysis, according to the risk density distribution, further generate the probability distribution set of landslide-prone areas, generate a data visualization graph to clearly display the risk probability distribution of each area, perform stress field analysis on the probability distribution set of landslide-prone areas to obtain the stress gradient tensor. Use finite element analysis software (such as ANSYS, COMSOL Multiphysics, etc.) to perform stress field analysis on the probability distribution set of landslide-prone areas. First, convert the probability distribution set into a three-dimensional model, input it into the software for mesh generation, use the topographic features, rock and soil layer structure, and landslide-prone area probability information of the area as boundary conditions, perform a static analysis on the model, calculate the stress distribution of each grid cell, and combine material mechanics parameters (such as elastic modulus, Poisson's ratio, etc.) to calculate the stress gradient tensor. Through these stress gradient data, further analyze the stress changes in the landslide-prone area, generate a stress field distribution graph to show the stress differences at each point in the area, ensure the accurate quantification of the stress of each area, and provide detailed stress data for the numerical simulation of critical conditions. Perform numerical simulation of critical conditions according to the stress gradient tensor to generate the startup condition dataset.According to the stress gradient tensor, input it into numerical simulation software (such as FLAC3D, PLAXIS, etc.) to simulate the critical conditions, set the initial conditions and boundary conditions of the simulation, including the physical properties of the area (such as soil friction, pore pressure, etc.), dynamically track the stress changes in the area through numerical simulation, calculate the evolution process of the stress field in the area, and judge whether each point has reached the critical conditions for landslide triggering. During the simulation, according to the specific parameters of the area, gradually adjust the stress gradient to simulate its impact on the occurrence of landslides. Finally, obtain the starting condition data set for each area, and record in detail the starting conditions for each area, including the stress values, deformation amounts, external influencing factors, etc. required to reach the critical conditions, providing detailed data support for subsequent landslide movement simulation to ensure the accuracy of the starting condition simulation and fully reflect the potential risks within the area.

[0104] Preferably, step S5 includes the following steps:

[0105] Step S51: Construct a kinematic model for the starting condition data set to generate a kinematic model;

[0106] Step S52: Conduct path analysis on the kinematic model to obtain an initial motion trajectory set;

[0107] Step S53: Perform terrain constraint processing based on the initial motion trajectory set to obtain a corrected motion trajectory set;

[0108] Step S54: Build a trajectory probability prediction model for the corrected motion trajectory set to generate a trajectory prediction probability model.

[0109] In this embodiment, a kinematic model is constructed for the starting condition dataset to generate a kinematic model. First, the starting condition dataset is obtained. This dataset includes the stress field data, terrain parameters, rainfall, and other factors affecting landslide initiation in the landslide area. Based on these data, a classic kinematic equation (such as Newton's second law) is used to construct the kinematic model of the landslide area. First, the starting condition dataset is transformed into a format suitable for modeling, and a mathematical modeling software (such as MATLAB, Simulink, etc.) is used to solve the kinematic equation. By inputting the physical characteristics and initial conditions of the landslide area (such as slope, friction, soil hardness, etc.), the kinematic model of this area is obtained through numerical calculation, generating a mathematical model describing the motion state of the landslide area. The model will include the velocity, acceleration, force, and other dynamic variables of the object. The output model includes the motion state information of each point in the area, forming a complete kinematic model. Path analysis is performed on the kinematic model to obtain the initial motion trajectory set. Path analysis is performed on the constructed kinematic model. First, based on the obtained kinematic equation, numerical simulation methods (such as the Euler method, Runge-Kutta method, etc.) are used to calculate the path of the landslide area. Under given initial conditions (such as initial velocity, initial position, initial acceleration, etc.), the motion path of the landslide object at different time steps is calculated. A dynamic simulation software (such as the ODE solver in MATLAB) is used to perform multiple iterative calculations on the path. According to different initial states (such as different rainfall, slope changes, etc.), the motion trajectory of the landslide object is analyzed, and the displacement and velocity changes corresponding to each time step are recorded to obtain a preliminary initial motion trajectory set. This trajectory set will contain multi-dimensional information such as the motion path, velocity, and acceleration of the landslide object. Terrain constraint processing is performed on the initial motion trajectory set to obtain a corrected motion trajectory set. Terrain constraint processing is performed on the initial motion trajectory set. First, the complete terrain feature data is obtained. This data contains detailed terrain information of the landslide area, such as slope, aspect, obstacle distribution, etc. Using these terrain information and combining with the initial motion trajectory set, terrain constraint analysis is carried out. By considering the actual impact of the terrain on the landslide object (such as encountering terrain undulations, rock obstacles, streams, etc.), the initial motion trajectory is corrected. Numerical analysis methods, such as the finite element method (FEM) or the boundary element method (BEM), are used to perform spatial constraint processing on the motion trajectory, recalculating the path of the landslide object at each moment, and adjusting the direction and speed of the path according to the changes in terrain factors to ensure that the path conforms to the actual terrain characteristics, obtaining a corrected motion trajectory set. This trajectory set can more accurately reflect the motion of the landslide object in complex terrain. Trajectory probability prediction modeling is performed on the corrected motion trajectory set to generate a trajectory prediction probability model.Based on the corrected trajectory set, a trajectory probability prediction model is built using statistical methods. First, a statistical analysis is performed on each trajectory in the corrected trajectory set to calculate the probability of each trajectory's occurrence. Combining external factors such as the distribution of historical landslide events and rainfall data, a probability distribution model (such as Gaussian distribution, Poisson distribution, etc.) is used to model the trajectories. In this model, the motion state of the input trajectory (such as displacement, velocity, acceleration, etc.) and the external environmental conditions are input, and through Bayesian inference or Maximum Likelihood Estimation (MLE) methods, the probabilities of different trajectories occurring are obtained, and then a complete trajectory prediction probability model is generated. The model contains the probability density of each trajectory occurring, which can describe the occurrence probability of different trajectories in the landslide area. Finally, the probability distribution of landslide events in this area is output, providing data support for subsequent landslide motion simulation and risk assessment.

[0110] Preferably, step S6 includes the following steps:

[0111] Step S61: Construct a three-dimensional dynamic simulation scenario based on the trajectory prediction probability model to obtain a visualization model;

[0112] Step S62: Conduct a comparative analysis of the visualization model and the target landslide area data to obtain comparative difference data;

[0113] Step S63: Conduct an iterative analysis of the visualization model based on the comparative difference data to obtain the landslide motion simulation results;

[0114] Step S64: Establish a dynamic risk warning and emergency response model based on the landslide motion simulation results.

[0115] In this embodiment, a three-dimensional dynamic simulation scenario is constructed based on the trajectory prediction probability model to obtain a visualization model. First, three-dimensional scene modeling is carried out using the trajectory data and external environmental data (such as precipitation, slope change, etc.) in the trajectory prediction probability model. A three-dimensional terrain model is constructed using three-dimensional modeling software (such as AutoCAD, 3DS Max, Blender, etc.) according to the geographical information of the landslide area (such as DEM data). Then, the trajectory prediction data is combined with the terrain model, and each trajectory data is mapped into three-dimensional space. The positions of each trajectory at different time nodes are calculated to generate a dynamic three-dimensional trajectory path. Animation software (such as Blender, Maya, etc.) is used to perform dynamic simulation on the path. The evolution of the trajectory is controlled by a time progress bar. The trajectory points obtained from the kinematic model are converted into a dynamic simulation process in chronological order. Finally, a visualized three-dimensional model of the landslide movement is generated, which includes the dynamic changes of the landslide movement path and the interaction with the terrain. The output of the model is a three-dimensional animation sequence of the landslide area, reflecting the dynamic changes during the landslide process. The visualization model and the data of the target landslide area are compared and analyzed to obtain comparison difference data. The comparison and analysis are carried out from two dimensions. First, historical data of the landslide area (such as past landslide event records, geological and meteorological data of the landslide area, etc.) is extracted. Then, these historical data are compared with the generated three-dimensional dynamic simulation scenario, and the differences between the actual trajectories and the model-predicted trajectories in historical landslide events are compared. Error analysis methods (such as root mean square error, mean error, etc.) are used to quantitatively analyze the deviation of the trajectories. By calculating the spatial differences between the model trajectories and the historical trajectories at each moment, comparison difference data is obtained. The deviation between the actual situation of the landslide area and the simulation results is further analyzed, and the deviation data is recorded and mapped into the simulation model to adjust the parts of the model that do not conform to the actual data in order to better optimize the simulation scenario. Finally, a set of comparison difference data is obtained, including information in multiple dimensions such as spatial error, time error, and speed error. The visualization model is iteratively analyzed according to the comparison difference data to obtain the landslide movement simulation results.Based on the obtained comparison difference data, the three-dimensional dynamic simulation model is iteratively corrected through optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.). First, according to the comparison differences obtained from historical data, the regions with large model deviations are identified, and then the iterative optimization method is used to adjust the parameters in the simulation model. The specific operations include modifying the terrain features in the model, modifying the motion parameters of the landslide objects, adjusting the external environmental conditions, etc. Through multiple rounds of iterative optimization, the simulation results are made to fit the historical data more closely. In each round of optimization, based on the new comparison difference data, the simulation calculation is re-performed, and the updated simulation results are compared with the actual historical landslide events until the difference between the simulation results and the historical data reaches the minimum. Finally, the optimized landslide motion simulation results are generated. The output simulation results include the motion state information such as the final position, velocity, and acceleration of the landslide objects, and have a high prediction accuracy. A dynamic risk early warning and emergency response model is established based on the landslide motion simulation results. According to the optimized landslide motion simulation results, first, an index system for landslide risk assessment is defined, such as the probability of landslide occurrence, the scale of the landslide, the destructive power of the landslide, etc. Then, based on the parameters such as the motion trajectory, velocity, and acceleration in the simulation results, combined with the geographical environment (such as nearby residential areas, infrastructure, etc.), a dynamic risk early warning model is constructed using numerical simulation and data fusion methods. By setting thresholds (such as landslide rate, change rate of the landslide area, etc.), the risk level of the landslide is evaluated in real time. At the same time, machine learning methods (such as support vector machines, decision trees, etc.) are used to analyze the potential laws in historical data to predict the probability of future landslide occurrence, and an emergency response model is constructed. According to the probability and time of landslide occurrence, corresponding emergency response plans are automatically generated, including personnel evacuation, resource scheduling, etc. Finally, a dynamic risk early warning and emergency response system is generated. This system can monitor the landslide risk in real time based on the landslide motion simulation results and automatically initiate emergency response measures according to the risk assessment results.

[0116] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0117] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A landslide motion simulation method based on a motion model, characterized in that: The following steps are involved: Step S1: Acquire target landslide area data; Perform geomorphic recognition on the target landslide area data to obtain the landslide area parameters; perform difference completion processing on the landslide area parameters to generate complete terrain feature data; Step S2: Perform terrain analysis on the complete terrain feature data to obtain terrain structure data; perform landslide risk analysis on the terrain structure data to obtain landslide prone areas; Step S3: constructing a three-dimensional model of the landslide-prone area to generate a three-dimensional landslide-prone area model; judging the critical state of the three-dimensional landslide-prone area model to obtain the critical state of the landslide area, wherein step S3 includes the following steps: Step S31: Perform stability analysis on the landslide-prone area to obtain stability data; Step S32: constructing a three-dimensional model of the landslide-prone area based on the stability data to generate a three-dimensional landslide-prone area model; Step S33: performing static mechanical equilibrium analysis on the three-dimensional landslide prone area model to obtain static equilibrium data; Step S34: performing critical state judgment on the static equilibrium data to obtain the critical state of the landslide area; Step S4: Calculate the risk weight based on the landslide critical state to generate a probability distribution set of landslide-prone areas; perform critical condition numerical simulation on the probability distribution set of landslide-prone areas to generate a start condition data set, wherein step S4 includes the following steps: Step S41: Calculate the risk weight based on the landslide critical state to obtain risk probability density data; Step S42: performing cluster analysis on the risk probability density data to generate a probability distribution set of landslide-prone areas; Step S43: performing stress field analysis on the probability distribution set of landslide-prone areas to obtain a stress gradient tensor; Step S44: performing a critical condition numerical simulation according to the stress gradient tensor to generate a start-up condition data set; Step S5: performing path analysis based on the start condition data set to obtain an initial motion trajectory set; performing trajectory probability prediction modeling on the initial motion trajectory set to generate a trajectory prediction probability model, wherein step S5 includes the following steps: Step S51: constructing a kinematic model for the start condition data set to generate a kinematic model; Step S52: performing path analysis on the kinematic model to obtain an initial motion trajectory set; Step S53: performing terrain constraint processing based on the initial motion trajectory set to obtain a modified motion trajectory set; Step S54: performing trajectory probability prediction modeling on the corrected motion trajectory set to generate a trajectory prediction probability model; Step S6: construct a three-dimensional dynamic simulation scene based on the trajectory prediction probability model to obtain a visualization model; iteratively analyze the visualization model according to the target landslide area data to obtain the landslide movement simulation results; and establish a dynamic risk warning and emergency response model based on the landslide movement simulation results.

2. The method for simulating landslide motion based on a motion model according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire target landslide area data; Step S12: performing topographic recognition on the target landslide area data to obtain landslide area parameters; Step S13: performing coordinate conversion on the landslide area parameters to generate landslide area coordinate data; Step S14: performing missing point detection on the coordinate data of the landslide area to obtain coordinate missing data; Step S15: performing difference completion on the landslide area coordinate data based on the missing coordinate data to generate complete terrain feature data.

3. The method for simulating landslide motion based on a motion model according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing terrain slope analysis on the complete terrain feature data to obtain terrain slope data; Step S22: performing terrain aspect analysis on the complete terrain feature data to obtain terrain aspect data; Step S23: terrain integration is performed on the terrain slope data and the terrain aspect data to generate terrain structure data; Step S24: performing regional superposition on the terrain structure data to generate a terrain superposition area; Step S25: Perform landslide risk analysis on the terrain superposition area to obtain landslide-prone areas.

4. The method for simulating landslide motion based on a motion model according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: constructing a three-dimensional dynamic simulation scene based on the trajectory prediction probability model to obtain a visualization model; Step S62: performing comparative analysis on the visualization model and the target landslide area data to obtain comparative difference data; Step S63: iteratively analyzing the visualization model according to the comparison difference data to obtain the landslide movement simulation result; Step S64: Establishing a dynamic risk warning and emergency response model based on the landslide movement simulation results.

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