Landslide motion simulation method based on motion model

Abstract

The invention relates to the technical field of landslide motion simulation, in particular to a landslide motion simulation method based on a motion model. The method comprises the following steps: acquiring data of a target landslide area; generating complete topographic feature data through landform identification and difference complementation; performing topographic analysis on the data; identifying a landslide-prone area, constructing a three-dimensional model, judging a critical state, performing risk weight calculation, and generating a probability distribution set of the landslide-prone area; performing path analysis to obtain an initial motion track and a probability prediction model thereof, constructing a three-dimensional dynamic simulation scene, performing iterative analysis, finally generating a landslide motion simulation result, and establishing a dynamic risk early warning and emergency response model to improve the prevention and control capability of landslide disasters. According to the invention, a more accurate and more reliable landslide motion simulation method is realized.

Description

Technical Field

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

[0002] The occurrence of landslides is often closely related to multiple factors such as topography, rainfall, and geology, but there are still many challenges in the prediction and simulation of landslide movement. Current research mostly focuses on the impact assessment after the landslide occurs, while the dynamic simulation research on the landslide evolution process is relatively weak. This leads to a lack of scientific basis for the construction of landslide risk management and early warning systems, and 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 motion simulation method based on a motion model to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a landslide motion simulation method based on a motion model comprises the following steps:

[0005] 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;

[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: 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;

[0008] 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;

[0009] 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;

[0010] 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.

[0011] The present invention can comprehensively acquire the geographic information and terrain parameters of the landslide area by performing high-precision data acquisition and landform recognition on the target landslide area. This process ensures the integrity and accuracy of the data and avoids the risk assessment errors caused by missing or inaccurate data in traditional methods. The data acquisition process combines remote sensing technology, geographic information system (GIS) and drone measurement and other efficient means to accurately capture the geomorphic features of the landslide area, provide a solid data foundation for subsequent analysis, and ensure the accuracy and meticulousness of landform recognition. By performing difference completion processing on the landslide area parameters, more complete terrain feature data can be obtained, eliminating the impact of data blank areas on subsequent analysis. In the process of terrain analysis, by performing differential completion processing on the complete terrain features, In-depth analysis of the data can generate detailed terrain structure data, which in turn provides a basis for landslide risk analysis. Through in-depth analysis of landslide-prone areas based on terrain structure data, high-risk areas can be accurately identified. Modern analysis tools are used to identify landslide-prone areas, and the probability of potential landslides is further determined, ensuring the accuracy of the determination of landslide-prone areas, thereby avoiding the negative impact of misjudgment of areas on disaster prevention and emergency response. The combination of terrain analysis and landslide risk analysis improves the accuracy of landslide disaster prevention. By constructing a three-dimensional model of landslide-prone areas, the complex terrain and geological conditions of landslide areas can be truly reflected. The three-dimensional visualization model can be used to intuitively display the spatial distribution and potential dangers of landslide-prone areas. The safety of the landslide area can be evaluated by combining the critical state judgment, which can effectively identify the critical state of the landslide, predict the specific location and time of the landslide, 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 critical state of the landslide, the risk degree of the landslide-prone area can be quantitatively analyzed. The calculation results can provide a reliable numerical basis for decision makers, and further simulate the probability distribution of the landslide-prone area to provide scientific data support for the subsequent critical condition numerical simulation. The critical condition numerical simulation can accurately predict the probability and scale of landslides under different conditions, generate a start condition data set, and ensure the landslide risk. The prediction is more in line with the actual environment. The critical condition simulation provides higher reliability and accuracy for landslide risk assessment. Based on the path analysis of the starting condition data set, the preliminary landslide movement trajectory can be obtained. Further trajectory probability prediction modeling is performed on the initial movement trajectory set to 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 historical data, environmental changes and other multi-dimensional factors, the model can accurately simulate the path changes of the landslide, and further predict the scope and intensity of the impact of the landslide on the surrounding area. Through the analysis of the trajectory prediction results, inappropriate 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 scene 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.

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

[0013] Step S11: Acquire target landslide area data;

[0014] Step S12: performing topographic recognition on the target landslide area data to obtain landslide area parameters;

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

[0016] Step S14: performing missing point detection on the coordinate data of the landslide area to obtain coordinate missing data;

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

[0018] The present invention ensures comprehensive and accurate data collection of the landslide area by acquiring the target landslide area data, obtains basic geographic data, provides necessary original information for subsequent topographic and geomorphic analysis, helps to accurately identify the spatial range and characteristics of the landslide area, performs topographic and geomorphic identification on the target landslide area data, obtains landslide area parameters, can identify the geological structure, topographic characteristics and other related parameters of the landslide area, provides basic data for further analysis of the probability of landslide occurrence and its risk, and topographic and geomorphic parameters provide a scientific basis for subsequent landslide risk assessment and prediction, performs coordinate transformation on the landslide area parameters, generates landslide area coordinate data, ensures coordination and consistency between different data sources, and can unify the spatial information of various types of data through coordinate transformation, eliminating factors. The deviation caused by inconsistent data formats is guaranteed to ensure the operability of landslide area data in different models. Missing point detection is performed on the landslide area coordinate data to obtain coordinate missing data, which can effectively identify the gaps or incomplete parts in the landslide area data and discover potential problems in the data, providing a clear direction for subsequent data supplementation and optimization. The quality of landslide area data can be improved through missing point detection. The landslide area coordinate data is supplemented by difference based on the missing coordinate data to generate complete terrain feature data, solving the analysis problems caused by missing data. The difference supplementation process effectively fills the blank areas in the data, ensures the integrity of the terrain feature data, improves the accuracy and reliability of the landslide area model, makes the landslide movement simulation more realistic and reliable, and ensures the accuracy of subsequent analysis results.

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

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

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

[0022] Step S23: terrain integration is performed on the terrain slope data and the terrain aspect data to generate terrain structure data;

[0023] Step S24: performing regional superposition on the terrain structure data to generate a terrain superposition area;

[0024] Step S25: Perform landslide risk analysis on the terrain superposition area to obtain landslide-prone areas.

[0025] The present invention obtains terrain slope data by performing terrain slope analysis on complete terrain feature data, which can effectively reveal the slope characteristics of the landslide area, identify the steep areas and gentle slope areas in the landslide area, and provide a scientific basis for the stability analysis of the landslide. The slope data provides an important parameter for the subsequent landslide risk assessment, which is helpful to fully understand the terrain undulations in the landslide area. The terrain aspect analysis is performed on the complete terrain feature data to obtain terrain aspect data. By analyzing the aspect data, the main slope direction and its changes in the landslide area can be identified, and spatial information on the impact of different slope aspects on the landslide is provided. The aspect analysis provides a key basis for further evaluating the potential direction of the landslide and its impact range. The terrain slope data and the terrain aspect data are terrain integrated to generate terrain structure data. The slope and aspect data are combined to fully reflect the terrain structure characteristics of the landslide area, forming a more detailed and comprehensive terrain structure. The data provides more accurate basic data for subsequent landslide risk analysis, helps to reveal the occurrence mechanism and potential impact area of ​​landslides, and performs regional superposition of terrain structure data to generate terrain superposition areas, which can superimpose different terrain features together to form a multi-level and multi-dimensional terrain data set. By superimposing different terrain information, the geographical structure of the landslide area can be more clearly understood. The superposition area provides a comprehensive perspective, which helps to more accurately divide the prone areas and their ranges during landslide analysis. Landslide risk analysis is performed on the terrain superposition area to obtain landslide-prone areas. Combining terrain characteristics with landslide risk factors, high-risk areas for landslides are identified, and landslide-prone areas are accurately delineated. The spatial distribution information of high-risk areas is provided, which provides data support for landslide disaster 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 comprises the following steps:

[0027] Step S31: Perform stability analysis on the landslide-prone area to obtain stability data;

[0028] 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;

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

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

[0031] The present invention obtains stability data by performing stability analysis on landslide-prone areas. By analyzing the stability of landslide-prone areas, the landslide potential of each area can be accurately evaluated, and key areas prone to landslides can be identified. The stability analysis provides data on the anti-landslide ability of the area, which provides 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. The three-dimensional modeling using the stability data can truly reflect the three-dimensional spatial characteristics of the landslide area, accurately display the terrain changes and the specific form of the landslide area. The three-dimensional model can simulate the terrain deformation process when a landslide occurs, and the generated three-dimensional landslide model provides a specific spatial structure for subsequent dynamic simulation. Static mechanical equilibrium analysis is performed on the three-dimensional landslide-prone area model to obtain static equilibrium data. Static mechanical equilibrium analysis can take into account various mechanical factors in the landslide area in the model and perform equilibrium calculations of physical conditions, thereby evaluating the stability of the area under different mechanical effects. Static equilibrium data helps determine the safety factor of the landslide area and provides a basis for critical state judgment. Critical state judgment is performed on the static equilibrium data to obtain the critical state of the landslide area. Through analysis and judgment of the static equilibrium data, it is possible to accurately determine whether the landslide area is in a critical state and clarify the critical conditions and timing of landslide occurrence. 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 comprises 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: performing cluster analysis on the risk probability density data to generate a probability distribution set of landslide-prone areas;

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

[0036] Step S44: Perform a critical condition numerical simulation based on the stress gradient tensor to generate a start-up condition data set.

[0037] The present invention obtains risk probability density data by calculating risk weights based on the critical state of landslides. By analyzing the critical state of landslide areas in detail, the landslide risks of various areas are quantitatively evaluated. The calculated risk probability density data reflects the 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 in 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 level and occurrence probability of each area, ensuring that the delineation of landslide-prone areas is more accurate. Scientifically, stress field analysis is performed on the probability distribution set of landslide-prone areas to obtain stress gradient tensors. Stress field analysis can calculate the stress distribution in the area according to the geological and physical characteristics of the landslide-prone areas. By analyzing the stress field of the probability distribution set, the stress gradient tensor is obtained. This data is crucial for judging the timing and intensity of landslides. Critical conditions are numerically simulated based on the stress gradient tensor to generate a starting condition data set. The critical conditions of the landslide area are numerically simulated using the stress gradient tensor. The simulation results can reveal the specific conditions for the occurrence of landslides. The generated starting condition data set provides a scientific basis for the starting timing and triggering mechanism of landslide movement, ensuring that landslide simulation and prediction have high timeliness and accuracy.

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

[0039] Step S51: constructing a kinematic model for the start condition data set to generate a kinematic model;

[0040] Step S52: performing path analysis on the kinematic model to obtain an initial motion trajectory set;

[0041] Step S53: performing terrain constraint processing based on the initial motion trajectory set to obtain a modified motion trajectory set;

[0042] Step S54: performing trajectory probability prediction modeling on the corrected motion trajectory set to generate a trajectory prediction probability model.

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

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

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

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

[0047] Step S63: iteratively analyzing the visualization model according to the comparison difference data to obtain the landslide movement simulation result;

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

[0049] The present invention constructs a three-dimensional dynamic simulation scene based on a trajectory prediction probability model to obtain a visualization model. The trajectory prediction probability model is used to construct a three-dimensional dynamic simulation scene that matches the terrain and landform of the landslide area. The movement path and process of the landslide can be intuitively displayed through three-dimensional modeling. The generated visualization model not only provides a clear dynamic display of the landslide, but also supports subsequent analysis and prediction. The visualization model and the target landslide area data are compared and analyzed to obtain comparative difference data. The terrain data of the actual landslide area are combined with the three-dimensional dynamic simulation scene for comparative analysis to identify the difference between the model and the actual area data to obtain comparative difference data. 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., and provide a clear direction for model optimization. The visualization model is iteratively analyzed according to the contrast difference data to obtain the landslide movement simulation results. The three-dimensional visualization model is iteratively optimized according to the contrast difference data. After multiple rounds of analysis and adjustment, accurate landslide movement simulation results are finally obtained. The iterative analysis can reflect the real-time changes of the landslide area, ensuring that the simulation results can fit the actual landslide process. A dynamic risk warning and emergency response model is established based on the landslide movement simulation results. The dynamic risk warning system and emergency response model are constructed using the landslide movement simulation results. Through the simulation of the landslide movement trajectory and process, potential risks can be evaluated in real time, and a scientific basis can be provided for emergency response decisions. 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 A schematic diagram of the steps of a landslide motion simulation method based on a motion model;

[0051] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0052] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0053] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within 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 figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0056] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. 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 this, please refer to Figures 1 to 3 , a landslide motion simulation method based on a motion model, comprising the following steps:

[0058] 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;

[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: 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;

[0061] 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;

[0062] 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;

[0063] 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.

[0064] The present invention can comprehensively acquire the geographic information and terrain parameters of the landslide area by performing high-precision data acquisition and landform recognition on the target landslide area. This process ensures the integrity and accuracy of the data and avoids the risk assessment errors caused by missing or inaccurate data in traditional methods. The data acquisition process combines remote sensing technology, geographic information system (GIS) and drone measurement and other efficient means to accurately capture the geomorphic features of the landslide area, provide a solid data foundation for subsequent analysis, and ensure the accuracy and meticulousness of landform recognition. By performing difference completion processing on the landslide area parameters, more complete terrain feature data can be obtained, eliminating the impact of data blank areas on subsequent analysis. In the process of terrain analysis, by performing differential completion processing on the complete terrain features, In-depth analysis of the data can generate detailed terrain structure data, which in turn provides a basis for landslide risk analysis. Through in-depth analysis of landslide-prone areas based on terrain structure data, high-risk areas can be accurately identified. Modern analysis tools are used to identify landslide-prone areas, and the probability of potential landslides is further determined, ensuring the accuracy of the determination of landslide-prone areas, thereby avoiding the negative impact of misjudgment of areas on disaster prevention and emergency response. The combination of terrain analysis and landslide risk analysis improves the accuracy of landslide disaster prevention. By constructing a three-dimensional model of landslide-prone areas, the complex terrain and geological conditions of landslide areas can be truly reflected. The three-dimensional visualization model can be used to intuitively display the spatial distribution and potential dangers of landslide-prone areas. The safety of the landslide area can be evaluated by combining the critical state judgment, which can effectively identify the critical state of the landslide, predict the specific location and time of the landslide, 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 critical state of the landslide, the risk degree of the landslide-prone area can be quantitatively analyzed. The calculation results can provide a reliable numerical basis for decision makers, and further simulate the probability distribution of the landslide-prone area to provide scientific data support for the subsequent critical condition numerical simulation. The critical condition numerical simulation can accurately predict the probability and scale of landslides under different conditions, generate a start condition data set, and ensure the landslide risk. The prediction is more in line with the actual environment. The critical condition simulation provides higher reliability and accuracy for landslide risk assessment. Based on the path analysis of the starting condition data set, the preliminary landslide movement trajectory can be obtained. Further trajectory probability prediction modeling is performed on the initial movement trajectory set to 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 historical data, environmental changes and other multi-dimensional factors, the model can accurately simulate the path changes of the landslide, and further predict the scope and intensity of the impact of the landslide on the surrounding area. Through the analysis of the trajectory prediction results, inappropriate 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 scene 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; and landslide risk analysis is performed on the terrain structure data to obtain landslide-prone areas. By analyzing the acquired complete terrain feature data, terrain analysis tools such as ArcGIS or QGIS are used to first perform slope analysis to evaluate the slope values ​​of each area, and screen the potential landslide areas according to the slope values. Then, a slope analysis is performed to identify the water flow path and precipitation convergence point in the landslide area, and the slope, slope direction, soil type, groundwater level and other factors are comprehensively analyzed. Through a landslide risk assessment model, such as factor analysis, logistic regression model, etc., the risk of the landslide area is calculated, thereby generating a landslide-prone area map, marking the areas with higher risks, and facilitating subsequent in-depth analysis and prediction.

[0070] 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;

[0071] In this embodiment, a three-dimensional model is constructed for a landslide-prone area to generate a three-dimensional landslide-prone area model; a critical state judgment is performed on the three-dimensional landslide-prone area model to obtain the critical state of the landslide area. The data of the landslide-prone area is processed using three-dimensional modeling software (such as AutoCAD, Civil 3D), and the two-dimensional data is converted into a three-dimensional digital model. A complete three-dimensional landslide-prone area model is generated through model rendering technology. The model can truly reflect the three-dimensional spatial characteristics and local structure of the landslide area. Static mechanical equilibrium analysis is performed through mechanical parameters (such as soil strength, rock strength, etc.) in the three-dimensional model. The model is analyzed using the limit equilibrium method or the finite element method. It is judged whether the landslide area is in a critical state based on the analysis results. The critical state judgment is based on the critical conditions for the occurrence of landslides, such as slope, soil friction coefficient, etc., to determine whether there is a potential risk of landslides.

[0072] 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;

[0073] In this embodiment, risk weight calculation is performed based on the critical state of landslide to generate a probability distribution set of landslide-prone areas; critical condition numerical simulation is performed on the probability distribution set of landslide-prone areas to generate a start condition data set. According to the above critical state judgment result, a risk weight calculation method (such as a multi-factor comprehensive scoring method) is used to assign different weights to each parameter (slope, soil type, geological structure, etc.) of the landslide-prone area, calculate the probability of landslide occurrence in each area, and then generate a complete probability distribution set of landslide-prone areas. Then, based on the distribution set, a critical condition simulation is performed by a numerical simulation method (such as a finite element method or a discrete element method), and the triggering conditions of each type of landslide are simulated to obtain a start condition data set, which contains the start risk information of each landslide area under specific conditions.

[0074] 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;

[0075] In this embodiment, path analysis is performed based on the start condition data set to obtain an initial motion trajectory set; trajectory probability prediction modeling is performed on the initial motion trajectory set to generate a trajectory prediction probability model. According to the start condition data set, the motion path of the landslide is analyzed using kinematic principles (such as Newton's Laws of Motion), and a path analysis tool (such as the path analysis module of ArcGIS) is used to simulate the motion process of the landslide from the starting point to the end point to obtain a preliminary motion trajectory data set. Through a large amount of historical landslide case data and existing landslide prediction models, the initial motion trajectory set is probability predicted based on methods such as Bayesian networks and Monte Carlo simulations, and a trajectory prediction probability model is constructed to generate the probability of occurrence of various trajectories, and to evaluate the probability of occurrence and risk level.

[0076] 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.

[0077] In this embodiment, a three-dimensional dynamic simulation scene is constructed based on the trajectory prediction probability model to obtain a visualization model; the visualization model is iteratively analyzed according to the target landslide area data to obtain the landslide movement simulation results; and a dynamic risk warning and emergency response model is established based on the landslide movement simulation results. The trajectory prediction probability model is used in combination with three-dimensional modeling software (such as Blender, Unity3D) to construct a dynamic simulation scene, and the movement trajectory and potential path of the landslide are displayed through the dynamic model to obtain a visualization model. The model is further iteratively optimized in combination with the actual data of the target landslide area, and the landslide movement simulation results are analyzed to obtain information such as the movement path, time distribution, and landslide speed. According to the landslide movement simulation results, an emergency response model (such as a GIS-based warning model) 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 comprises the following steps:

[0079] Step S11: Acquire target landslide area data;

[0080] Step S12: performing topographic recognition on the target landslide area data to obtain landslide area parameters;

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

[0082] Step S14: performing missing point detection on the coordinate data of the landslide area to obtain coordinate missing data;

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

[0084] In this embodiment, the target landslide area data is obtained. First, the surface data of the target landslide area is obtained by remote sensing technology or a laser radar (LiDAR) system carried by an unmanned aerial vehicle, and the landslide area is comprehensively scanned using remote sensing data such as multispectral images and radar images to obtain information such as the elevation, slope, slope direction, and vegetation coverage of the area. In conjunction with ground measured data, GPS (Global Positioning System) equipment is used to accurately locate the area on site, and image data from different times and sources are uniformly processed by remote sensing image registration technology (such as an image registration method based on cross-correlation or feature matching) to ensure that the accuracy of the acquired regional data meets the requirements, so as to provide reliable basic data for subsequent topographic and geomorphic analysis, and to perform topographic and geomorphic recognition on the target landslide area data to obtain landslide area parameters. The acquired data of the target landslide area are analyzed and processed through the 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. These areas are often potential locations for landslides. Then, the landform classification algorithm (such as the K-means clustering algorithm) is used to divide the terrain and landform types, identify different geological units, and combine the soil type, rock structure and other information in the area. The spatial analysis tools (such as the spatial analysis tool set) are used to extract the parameters of the area prone to landslides, such as elevation difference, slope change, soil stability, etc. These parameters will provide an important basis for subsequent landslide risk assessment and motion simulation. The coordinate conversion of the landslide area parameters is performed to generate the landslide area coordinate data. According to the acquired landslide area parameters, the acquired geographic coordinate system data (such as WGS84 coordinate system) is converted into the required projection coordinate system (such as UTM projection coordinate system) using coordinate conversion algorithm. The accurate conversion formula or the built-in coordinate conversion tool in the geographic information system (GIS) (such as the "Projection and Transformation" tool in ArcGIS) is used to realize the coordinate conversion and storage of the data through the geospatial database (such as PostGIS) to ensure that the converted coordinate data meets the requirements of subsequent three-dimensional modeling and analysis. In the process of coordinate conversion, the errors occurring in the conversion process need to be corrected to ensure the accuracy of the results. The missing points of the coordinate data of the landslide area are detected to obtain the coordinate missing data.Through quality control of the converted coordinate data of the landslide area, a missing data detection algorithm (such as a missing point detection method based on interpolation) is used to comprehensively scan the coordinate data to identify areas with missing values ​​or data anomalies. Statistical analysis methods (such as standard deviation analysis or extreme value detection) are used to evaluate the accuracy and completeness of the coordinate data, and points with missing or inaccurate data are marked. In specific operations, spatial analysis tools (such as spatial autocorrelation analysis) in the geographic information system (GIS) can be used to perform spatial distribution analysis on the geographic data in the area, locate the spatial position of the missing data points, ensure that the detected missing point data is accurate, and provide a basis for subsequent difference completion. Based on the missing coordinate data, the coordinate data of the landslide area is difference-completed to generate complete terrain feature data. Spatial interpolation methods (such as Kriging interpolation and inverse distance weighted method) are used to complete the missing coordinate data. First, an appropriate interpolation algorithm is selected to calculate the coordinate values ​​of the missing points based on the spatial distribution of known data points. The missing data points are filled in the complete coordinate system through interpolation processing to ensure that the completed data can truly reflect the regional terrain characteristics. Further, error assessment methods (such as cross-validation or residual analysis) are used to perform quality inspection on the completed data to confirm the accuracy of the completed results. Finally, complete terrain feature data is generated and stored and managed to facilitate subsequent landslide risk analysis, model construction and simulation.

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

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

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

[0088] Step S23: terrain integration is performed on the terrain slope data and the terrain aspect data to generate terrain structure data;

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

[0090] Step S25: Perform landslide risk analysis on the terrain superposition 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. GIS (Geographic Information System) software, such as ArcGIS or QGIS, is used to import the complete terrain feature data into the system. A terrain slope calculation tool is used to perform slope analysis on the area, and the slope value of each geographic unit is calculated. An appropriate terrain data source, such as DEM (Digital Elevation Model), is selected, and a calculation window size (such as a 3×3 or 5×5 window) is set. A rasterization algorithm is applied to calculate the slope value of each grid unit. A common slope calculation formula, such as a derivative method based on trigonometric functions or an elevation difference method, is used to obtain the slope value of each pixel point. The result is a new slope raster data set, which represents 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. The terrain aspect analysis is performed on the complete terrain feature data to obtain the terrain aspect data. Through a process similar to the 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 grid cell according to the elevation value of the target area. The algorithm of selecting the maximum gradient direction from the 8 neighboring pixels is used to obtain the aspect data of each pixel. The aspect calculation result is expressed as an angle, usually with north as 0° and clockwise as 0°. The direction is positive, and the result is a new aspect dataset, which describes the slope direction of each location in the area. The results are further checked and corrected through the spatial analysis function of GIS to ensure that the aspect data of each grid unit is consistent with the terrain characteristics. During the processing, special attention will be paid to the continuity and accuracy of the data to avoid faults or outliers. The terrain slope data and terrain aspect data are topographically integrated to generate terrain structural data. The spatial analysis tools provided in the GIS software are used to overlay the slope data and the aspect data. A suitable integration algorithm (such as the grid-based weighted merging method) is selected to combine the slope and aspect values ​​for weighted merging processing to generate a comprehensive terrain structural dataset. The weight coefficient can be adjusted during the analysis. (such as setting the relative importance of slope and aspect) to refine the merging strategy and obtain an integrated raster data, in which each pixel contains two parameters, slope and aspect, to form a structural data set containing complete terrain features, to ensure that the integrated data can fully reflect the terrain complexity of the region and provide sufficient geographic information basis for subsequent landslide risk analysis. Through the spatial overlay analysis function in GIS, the terrain structural data and other relevant geographic information (such as soil type, vegetation cover, hydrological conditions, etc.) are overlaid, and a suitable regional range is selected for analysis. The raster overlay method is used to overlay multiple raster data sets pixel by pixel according to the geographic spatial position to obtain a new data set containing all necessary geographic information.The weighted average method can be applied in the superposition process. According to the influence weights of different factors, the attribute values ​​of each pixel are weighted and calculated to generate a comprehensive terrain superposition area data. The superposition results are further classified through cluster analysis (such as K-means clustering) to identify potential landslide-prone areas and generate corresponding risk area data to ensure that the final terrain superposition area accurately reflects the interaction of different geographical factors, providing a reliable data basis for the identification and risk assessment of landslide-prone areas. Using terrain superposition area data, landslide risk assessment models (such as hierarchical analysis method, logistic regression model or multi-factor weighted model) are used to analyze landslide-prone areas. First, , select key factors affecting landslides (such as slope, slope aspect, soil moisture, vegetation coverage, etc.), use GIS to perform spatial analysis on these factors, calculate the contribution of each factor in the terrain superposition area, combine historical data of known landslide areas, generate risk levels for landslide-prone areas through comparative analysis, use spatial weighting methods to assign different weights to different factors, comprehensively analyze the impact of various factors, and calculate the landslide risk index through model calculation. Finally, generate a risk level division map and 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 comprises the following steps:

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

[0094] 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;

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

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

[0097] In this embodiment, the geographic data (such as slope, soil type, vegetation coverage, etc.) of the landslide-prone area is imported into the GIS (geographic information system) platform, and the 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 limit equilibrium method or finite element method) are used to evaluate the stability of the landslide-prone area. Appropriate physical property parameters (such as soil friction angle, cohesion, gravity acceleration, etc.) are selected, and the stability of different points in the area is evaluated by numerical simulation to obtain the stability index of each grid unit. The stability results are further classified according to the different characteristics of the landslide-prone area, and areas with different stability levels are marked. Finally, stability data is generated to ensure that the analysis process takes into account the comprehensive impact of different factors so as to accurately assess the potential risks in the area. By importing the stability data, the landslide-prone area is modeled using three-dimensional modeling software (such as AutoCAD, SolidWorks or ANSYS). First, the geographic boundaries of the area are defined, and the terrain features such as different levels, soil types and slopes in the area are modeled in detail based on the stability data to construct a three-dimensional terrain model in the area. , the stability state of each point is determined by numerical simulation, the area is discretized using gridding technology, and the stability data of each discrete point is assigned to the corresponding grid unit to form a three-dimensional grid model containing stability information. Combined with the geographical location and elevation data of the region, the three-dimensional structure of the landslide-prone area is accurately presented to ensure the accuracy and spatial consistency of the model. Static mechanical analysis tools (such as ABAQUS, FLAC3D, etc.) are used to perform mechanical equilibrium analysis on the three-dimensional landslide-prone area model. First, according to the terrain characteristics, soil properties, rock structure and other factors of the region, reasonable boundary conditions and load conditions are set. The three-dimensional model is input into the mechanical analysis software, and appropriate material models (such as elastic models, plastic models, etc.) are selected. External pressure and gravity are applied to each grid unit to perform static mechanical equilibrium analysis. The stress, strain, displacement and other data of each point in the region are calculated to generate a static equilibrium data set to ensure that the analysis results fully reflect the mechanical properties under the actual terrain and physical environment. The mechanical equilibrium state is further checked to identify the landslide inducement, to ensure that the calculation results are accurate and consistent with the actual geological environment, and the critical state of the static equilibrium data is judged to obtain the critical state of the landslide area.By analyzing the static equilibrium data, the critical state model (such as the Swiss mountain engineering stability analysis method or the Mohr-Coulomb critical condition method) is used to judge the critical state of the landslide. First, the critical instability standard is set, and the safety factor of each grid unit is calculated according to the static equilibrium data of the region. The appropriate critical judgment method (such as the limit equilibrium method or the stability analysis method) is selected, and the region is classified according to the safety factor. The critical state of the landslide-prone area is identified, and the critical state diagram of the landslide area is generated. The critical state judgment is combined with the stability data to obtain the potential sliding hazard area of ​​the landslide area. It is analyzed whether the critical state of each area meets the landslide triggering conditions. The final critical state data is obtained through step-by-step analysis, which provides detailed data support for subsequent landslide simulation and ensures that the judgment of the critical state is strictly based on actual data and scientific models.

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

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

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

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

[0102] Step S44: Perform a critical condition numerical simulation based on the stress gradient tensor to generate a start-up condition data set.

[0103] In this embodiment, risk weight calculation is performed based on the critical state of landslide to obtain risk probability density data. First, a landslide critical state data set is imported, and multi-source data such as geology, meteorology, and historical landslide events are combined to calculate the risk weight of each landslide area using a multi-factor risk assessment model (such as weighted average method, analytic hierarchy process, etc.). Different risk weights are assigned to each area based on the stability data, critical state data, and other external factors (such as rainfall, earthquake, etc.) of the area to ensure that the risk weight of each area reflects the probability of landslide occurrence. The risk weight of each grid unit is obtained by statistics of historical landslide occurrence data and combined with the landslide susceptibility assessment results of the area. Subsequently, probability density analysis is performed on these risk weights to generate risk probability density data for each area. Cluster analysis is performed on the risk probability density data to generate a probability distribution set of landslide-prone areas. Clustering algorithms (such as K-means clustering, DBSCAN, etc.) are applied to the risk probability density data. The data are grouped through the clustering algorithm to identify areas with different risk levels. First, the risk probability density data is standardized. The risk value of each area is converted into a standard value and then input into the clustering algorithm. According to the preset clustering standard, areas with similar risk values ​​are classified into one category, thereby obtaining the risk levels and probability distributions of multiple landslide-prone areas. Each clustered area represents a specific landslide risk area. After clustering analysis, the probability distribution set of landslide-prone areas is further generated based on the risk density distribution. A data visualization diagram is generated to clearly display the risk probability distribution of each area. The stress field analysis is performed on the probability distribution set of landslide-prone areas to obtain the stress gradient tensor. Finite element analysis software (such as ANSYS, COMSOL Multiphysics, etc.) is used to perform stress field analysis on the probability distribution set of landslide-prone areas. First, the probability distribution set is converted into a three-dimensional model and input into the software for grid division. The terrain characteristics, rock and soil layer structure, and probability information of landslide-prone areas in the region are used as boundary conditions. The model is statically analyzed and the stress distribution of each grid unit is calculated. The stress gradient tensor is calculated in combination with the material mechanical parameters (such as elastic modulus, Poisson's ratio, etc.). Through these stress gradient data, the stress changes in landslide-prone areas are further analyzed, and a stress field distribution diagram is generated to display the stress differences at each point in the region, ensuring that the stress in each region is accurately quantified to provide detailed stress data for critical condition numerical simulation. Critical condition numerical simulation is performed based on the stress gradient tensor to generate a startup condition data set.According to the stress gradient tensor, the numerical simulation software (such as FLAC3D, PLAXIS, etc.) is input to perform critical condition simulation, and the initial conditions and boundary conditions of the simulation are set, including the physical properties of the region (such as soil friction, pore pressure, etc.). The stress changes in the region are dynamically tracked through numerical simulation, and the evolution process of the stress field in the region is calculated to determine whether each point has reached the critical condition for triggering the landslide. During the simulation, the stress gradient is gradually adjusted according to the specific parameters of the region to simulate its impact on the occurrence of the landslide. Finally, the starting condition data set for each region is obtained, and the starting conditions of each region are recorded in detail, including the stress value, deformation, external influencing factors, etc. required to reach the critical conditions, so as to provide detailed data support for the subsequent landslide movement simulation, ensure that the starting condition simulation is accurate, and fully reflect the potential risks in the region.

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

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

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

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

[0108] Step S54: performing trajectory probability prediction modeling on the corrected motion trajectory set to generate a trajectory prediction probability model.

[0109] In this embodiment, a kinematic model is constructed for a start-up condition data set to generate a kinematic model. First, a start-up condition data set is obtained, which includes stress field data, terrain parameters, rainfall and other factors that affect the start of the landslide in the landslide area. Based on these data, a classical kinematic equation (such as Newton's second law) is used to construct a kinematic model of the landslide area. First, the start-up condition data set is converted 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 (such as slope, friction, soil hardness, etc.) of the landslide area, a kinematic model of the area is obtained by numerical calculation, and a mathematical model describing the motion state of the landslide area is generated. The model will include the speed, 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, and the kinematic model is subjected to path analysis to obtain an initial motion trajectory set. The constructed kinematic model is subjected to path analysis. First, based on the obtained kinematic equation, numerical simulation methods (such as the Euler method, the 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. 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 amounts, 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, which will contain multi-dimensional information such as the motion path, velocity, acceleration, etc. of the landslide object. Based on the initial motion trajectory set, terrain constraints are processed to obtain a modified motion trajectory set. The initial motion trajectory set is processed with terrain constraints. First, complete terrain feature data is obtained, which contains detailed terrain information of the landslide area, such as slope, slope direction, obstacle distribution, etc. These terrain information are combined with the initial motion trajectory set to perform terrain constraint analysis. 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, and the path of the landslide object at each moment is recalculated. According to the changes in terrain factors, the direction and speed of the path are adjusted to ensure that the path conforms to the actual terrain characteristics. The corrected motion trajectory set is obtained, which can more accurately reflect the movement 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 modified motion trajectory set, the trajectory probability prediction modeling is carried out using statistical methods. First, each trajectory in the modified trajectory set is statistically analyzed to calculate the probability of each trajectory. Combined with the distribution of historical landslide events, rainfall data and other external factors, the trajectory is modeled using a probability distribution model (such as Gaussian distribution, Poisson distribution, etc.). In this model, the motion state of the trajectory (such as displacement, velocity, acceleration, etc.) and external environmental conditions are input. The probability of occurrence of different trajectories is obtained through Bayesian inference or maximum likelihood estimation (MLE) methods, and then a complete trajectory prediction probability model is generated. The model contains the probability density of each trajectory, which can describe the probability of occurrence of different trajectories in the landslide area. Finally, the probability distribution of landslide events in the area is output, providing data support for subsequent landslide motion simulation and risk assessment.

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

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

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

[0113] Step S63: iteratively analyzing the visualization model according to the comparison difference data to obtain the landslide movement simulation result;

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

[0115] In the present embodiment, a three-dimensional dynamic simulation scene is constructed based on a trajectory prediction probability model to obtain a visualization model. First, the trajectory data in the trajectory prediction probability model and external environment data (such as precipitation, slope change, etc.) are used to perform three-dimensional scene modeling, and a three-dimensional terrain model is constructed according to the geographic information (such as DEM data) of the landslide area using three-dimensional modeling software (such as AutoCAD, 3DS Max, Blender, etc.), and then the trajectory prediction data is combined with the terrain model, and each trajectory data is mapped into a three-dimensional space, and the position of each trajectory at different time nodes is calculated to generate a dynamic three-dimensional trajectory path, and animation software (such as Blender, Maya, etc.) is used to dynamically simulate the path, and the evolution of the trajectory is controlled by a time progress bar, and the trajectory points obtained in the kinematic model are converted into a dynamic simulation process in chronological order, and finally a visualization three-dimensional model of landslide motion is generated, which includes the dynamic changes of the landslide motion path and the interaction with the terrain, and the output of the model is a three-dimensional animation sequence of the landslide area, which reflects the dynamic changes in the landslide process, and the visualization model and the target landslide area data are compared and analyzed to obtain comparison difference data. The comparative analysis is carried out from two dimensions. First, the historical data of the landslide area (such as past landslide event records, geological and meteorological data of the landslide area, etc.) are extracted, and then these historical data are compared with the generated three-dimensional dynamic simulation scene. The difference between the actual trajectory of the historical landslide event and the trajectory predicted by the model is compared. The deviation of the trajectory is quantitatively analyzed using error analysis methods (such as root mean square error, average error, etc.). By calculating the spatial difference between the model trajectory and the historical trajectory at each moment, the comparative difference data is obtained. The deviation between the actual situation in the landslide area and the simulation results is further analyzed. The deviation data is recorded and mapped to the simulation model. The part of the model that does not conform to the actual data is adjusted to better optimize the simulation scene. Finally, a set of comparative difference data is obtained, including information in multiple dimensions such as spatial error, time error, and velocity error. The visualization model is iteratively analyzed based on the comparative difference data to obtain the landslide movement simulation results.Based on the obtained contrast 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 contrast differences obtained from the historical data, the areas with large model deviations are identified, and then the parameters in the simulation model are adjusted using the iterative optimization method. 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 more consistent with the historical data. In each round of optimization, the simulation calculation is re-performed based on the new contrast difference data, and the updated simulation results are compared with the actual historical landslide events until the difference between the simulation results and the historical data is minimized. Finally, the optimized landslide motion simulation results are generated. The output simulation results include the final position, velocity, acceleration and other motion state information of the landslide objects, and have high prediction accuracy. A dynamic risk warning and emergency response model is established based on the landslide motion simulation results. According to the optimized landslide movement simulation results, the indicator system of landslide risk assessment is first defined, such as the probability of landslide occurrence, the scale of landslide, the destructive power of landslide, etc. Then, according to the motion trajectory, speed, acceleration and other parameters in the simulation results, combined with the geographical environment (such as nearby residential areas, infrastructure, etc.), the dynamic risk warning model is constructed using numerical simulation and data fusion methods. By setting thresholds (such as landslide rate, change rate of landslide area, etc.), the risk level of landslide is evaluated in real time. At the same time, machine learning methods (such as support vector machine, decision tree, etc.) are used to analyze the potential rules in historical data, predict the probability of future landslides, and construct an emergency response model. According to the probability and time of landslide occurrence, the corresponding emergency response plan is automatically generated, including personnel evacuation, resource scheduling, etc., and finally a dynamic risk warning and emergency response system is generated. The system can monitor the landslide risk in real time according to the landslide movement simulation results, and automatically initiate emergency response measures according to the risk assessment results.

[0116] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0117] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented 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; 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; 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; 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 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: Perform critical state judgment on the static equilibrium data to obtain the critical state of the landslide area.

5. The method for simulating landslide motion based on a motion model according to claim 1, characterized in that: 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: Perform a critical condition numerical simulation based on the stress gradient tensor to generate a start-up condition data set.

6. The method for simulating landslide motion based on motion model according to claim 1, characterized in that: 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.

7. 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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