Dynamic simulation method and system for freeze-thaw mudflow chain disaster

Abstract

The present invention relates to the technical field of freeze-thaw mudflow simulation, and in particular to a method and system for dynamic simulation of freeze-thaw mudflow chain disasters. The method comprises the following steps: acquiring regional topographic survey data and performing point cloud conversion and three-dimensional modeling, generating a digital terrain model, collecting regional meteorological data and extracting features, performing multi-source fusion with the digital terrain model, forming a data fusion model, based on the model, identifying freeze-thaw critical conditions and locating freeze-thaw prone areas, performing geological structure calculation and stability analysis on prone areas, generating mudflow risk data, using these risk data, performing dynamic change simulation, obtaining simulated mudflow nodes and generating disaster trajectories, performing disaster risk assessment on the disaster trajectories, and implementing emergency warning processing based on the assessment results to generate corresponding warning data. The present invention realizes a more accurate dynamic simulation method for freeze-thaw mudflow chain disasters.

Description

Technical Field

[0001] The invention relates to the technical field of freeze-thaw mudflow simulation, and in particular to a freeze-thaw mudflow chain disaster dynamic simulation method and system. Background Art

[0002] Freeze-thaw mudslides usually occur during temperature fluctuations in spring and autumn, especially under rainy or snowmelt conditions, when soil saturation increases, forming unstable soil layers and leading to mudslides. Although existing studies have explored the causes and impacts of mudslides, there is still a lack of systematic analysis of the dynamic characteristics of freeze-thaw mudslides and their disaster mechanisms, and there is a lack of effective prediction and response strategies. Existing freeze-thaw mudslide research often focuses on local case analysis and lacks overall consideration of regional mudslide risks, resulting in insufficient applicability and accuracy of mudslide prediction models. Traditional analysis methods mainly rely on experience and historical data, and it is difficult to effectively integrate multiple factors such as topography, meteorology and geology, and cannot fully reflect the complex characteristics of freeze-thaw mudslides. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for simulating the dynamics of freeze-thaw mudflow chain disasters to solve at least one of the above technical problems.

[0004] To achieve the above purpose, the dynamic simulation method of freeze-thaw mudflow chain disaster includes the following steps:

[0005] Step S1: acquiring regional topographic survey data; performing point cloud conversion on the regional topographic survey data to obtain topographic point cloud data; performing three-dimensional modeling on the topographic point cloud data to generate a digital terrain model;

[0006] Step S2: collecting regional meteorological data; extracting features from the regional meteorological data to obtain meteorological feature data; performing multi-source fusion processing on the digital terrain model based on the meteorological feature data to generate a data fusion model;

[0007] Step S3: identifying freeze-thaw critical conditions based on the data fusion model to obtain a freeze-thaw critical condition set; locating the freeze-thaw prone area based on the freeze-thaw critical condition set using the data fusion model to generate a freeze-thaw prone area;

[0008] Step S4: Calculate the geological structure of the freeze-thaw prone area to obtain geological structure data; perform stability analysis on the freeze-thaw prone area based on the geological structure data to generate mudflow risk data;

[0009] Step S5: Based on the mudflow risk data, dynamic changes of the freeze-thaw prone area are simulated to obtain simulated mudflow nodes; the simulated mudflow nodes are chained to generate simulated disaster trajectories;

[0010] Step S6: Conduct disaster risk assessment on the simulated disaster trajectory to generate disaster risk data; perform emergency warning processing based on the disaster risk data to generate disaster warning data.

[0011] The present invention ensures the accuracy and comprehensiveness of data by acquiring regional topographic survey data. By performing point cloud conversion on regional topographic survey data, the terrain point cloud data obtained provides a high-resolution basis for three-dimensional modeling. The terrain point cloud data is subsequently subjected to three-dimensional modeling, and the generated digital terrain model can truly reflect the terrain characteristics of the region, thereby improving the reliability of subsequent analysis and laying a solid foundation for mudflow risk assessment. The implementation of collecting regional meteorological data ensures the real-time nature of meteorological information. By performing feature extraction on regional meteorological data, the obtained meteorological characteristic data can reflect the changes in important meteorological factors. Based on the meteorological characteristic data, multi-source fusion processing is performed on the digital terrain model, and the generated data fusion model integrates terrain and meteorological information, providing a more comprehensive basis for the subsequent identification of freeze-thaw critical conditions. The implementation of freeze-thaw critical condition identification based on the data fusion model ensures that the conditions for the occurrence of freeze-thaw phenomena can be accurately defined. The obtained freeze-thaw critical condition set provides a clear basis for the subsequent positioning of prone areas. The data fusion model is used to locate freeze-thaw prone areas according to the freeze-thaw critical condition set, and the generated freeze-thaw prone areas can intuitively display potential risk areas. The implementation of geological structure calculations in freeze-thaw prone areas ensures an in-depth understanding of regional geological characteristics. The obtained geological structure data provides a necessary basis for subsequent stability analysis. By conducting stability analysis on freeze-thaw prone areas based on geological structure data, the mudflow risk data generated can effectively identify potential mudflow risks and provide a scientific basis for disaster prevention and mitigation measures in the region. The implementation of dynamic change simulation of freeze-thaw prone areas based on mudflow risk data can comprehensively evaluate the behavior pattern of mudflows. The obtained simulated mudflow nodes provide a basis for subsequent chain connections. The simulated disaster trajectory generated by chain connection of simulated mudflow nodes can clearly depict the propagation path of mudflows and provide important data support for actual risk assessment and prediction. The implementation of disaster risk assessment on simulated disaster trajectories can quantify the potential impact of mudflows on the region. The generated disaster risk data provides a basis for emergency management. The disaster warning data generated by emergency warning processing based on disaster risk data can provide timely information to relevant departments, ensuring that effective response measures are taken before mudflow disasters occur, reducing the losses caused, and improving the efficiency and effectiveness of disaster prevention and mitigation.

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

[0013] Step S11: acquiring regional topographic survey data; reconstructing the spatial coordinates of the regional topographic survey data to obtain spatial coordinate data;

[0014] Step S12: performing anomaly detection on the spatial coordinate data to generate abnormal coordinate data;

[0015] Step S13: removing abnormal values ​​from the spatial coordinate data according to the abnormal coordinate data to obtain terrain point cloud data; performing topological relationship analysis on the terrain point cloud data to obtain point cloud topological relationships;

[0016] Step S14: Perform three-dimensional modeling on the terrain point cloud data based on the point cloud topological relationship to generate a digital terrain model.

[0017] The present invention ensures the comprehensiveness and accuracy of data by acquiring regional topographic survey data. By reconstructing the spatial coordinates of the regional topographic survey data, the obtained spatial coordinate data provides high-precision basic information for subsequent analysis, can truly reproduce the terrain features, and ensure the reliability of subsequent processing. The implementation of anomaly detection on the spatial coordinate data can effectively identify errors or inconsistencies in the data. The generated abnormal coordinate data provides a key basis for subsequent cleaning, ensuring the improvement of data quality. By timely discovering and marking abnormal data, the efficiency of data processing is improved. The implementation of outlier removal of spatial coordinate data based on the abnormal coordinate data can significantly improve the quality of data. The obtained terrain point cloud data has higher accuracy. Subsequently, the terrain point cloud data is subjected to topological relationship analysis. The obtained point cloud topological relationship provides structured information for subsequent three-dimensional modeling, enhancing the operability of the model. The implementation of three-dimensional modeling of terrain point cloud data based on the point cloud topological relationship ensures the accurate construction of the digital terrain model. The generated digital terrain model truly reflects the terrain features of the region, improves the reliability and accuracy of subsequent analysis, provides a high-quality model foundation for mudflow risk assessment and prediction, and supports more complex calculation and simulation requirements.

[0018] Preferably, step S2 comprises the following steps:

[0019] Step S21: Defining the monitoring range according to the digital terrain model to obtain monitoring range data; deploying equipment monitoring based on the monitoring range data to obtain regional meteorological data;

[0020] Step S22: performing feature extraction processing on the regional meteorological data to obtain meteorological feature data; performing time series analysis on the meteorological feature data to generate meteorological change trends;

[0021] Step S23: performing feature correlation processing on the meteorological feature data and the meteorological change trend to obtain meteorological feature correlation data;

[0022] Step S24: Perform multi-source superposition fusion on the meteorological feature correlation data and the digital terrain model to generate a data fusion model.

[0023] The present invention ensures the pertinence and effectiveness of monitoring by defining the monitoring range through the digital terrain model. The obtained monitoring range data provides a scientific basis for the reasonable arrangement of subsequent meteorological equipment. Equipment monitoring deployment is carried out based on the monitoring range data. The generated regional meteorological data provides basic data for understanding meteorological characteristics, ensuring the real-time and accuracy of information. The implementation of feature extraction processing on regional meteorological data can screen out key meteorological indicators. The obtained meteorological characteristic data provides an effective information basis for subsequent analysis. Subsequently, the meteorological characteristic data is subjected to time series analysis. The generated meteorological change trend can reveal the evolution law of meteorological conditions, provide important clues for understanding the occurrence mechanism of freeze-thaw phenomenon, and provide important clues for understanding meteorological characteristics. The implementation of feature correlation processing of meteorological data and meteorological change trends can reveal the mutual influence of meteorological factors. The obtained meteorological feature correlation data provides a rich information level for subsequent comprehensive analysis, enhances the model's ability to understand complex meteorological phenomena, and enables more influencing factors to be considered in mudflow risk assessment. The implementation of multi-source superposition and fusion of meteorological feature correlation data and digital terrain models ensures the effective integration of different data sources. The generated data fusion model provides a comprehensive perspective for subsequent analysis, can more accurately reflect the meteorological and terrain characteristics of the region, support more complex analysis and simulation needs, and provide a solid foundation for the dynamic simulation of freeze-thaw mudflows, thereby improving the adaptability and accuracy of the overall model.

[0024] Preferably, step S3 comprises the following steps:

[0025] Step S31: Analyze the temperature change characteristics of the data fusion model to obtain a temperature change data set;

[0026] Step S32: identifying freeze-thaw critical conditions for the temperature change data set according to a preset critical temperature threshold to obtain a freeze-thaw critical condition set;

[0027] Step S33: performing spatial coordinate matching on the freeze-thaw critical condition set and the data fusion model to obtain a superimposed data set;

[0028] Step S34: positioning the freeze-thaw prone areas on the superimposed data set to generate freeze-thaw prone areas.

[0029] The present invention ensures a deep understanding of temperature changes by implementing temperature change characteristic analysis on the data fusion model. The obtained temperature change data set provides an important basis for subsequent freeze-thaw condition identification, can reveal the temporal and spatial variation law of temperature in the region, and improves the sensitivity to changes in meteorological conditions. The implementation of freeze-thaw critical condition identification on the temperature change data set according to the preset critical temperature threshold can accurately define the key temperature conditions for mudflow occurrence. The obtained freeze-thaw critical condition set provides a clear reference standard for subsequent analysis, so that potential freeze-thaw risk points can be quickly identified under complex climatic conditions. The implementation of spatial coordinate matching of the freeze-thaw critical condition set and the data fusion model ensures effective integration between different data sets. The obtained superimposed data set provides a rich information hierarchy for subsequent geographic information analysis, enhances the positioning accuracy of freeze-thaw prone areas in the region, and makes subsequent analysis more scientific and reasonable. The implementation of freeze-thaw prone areas positioning on the superimposed data set can systematically identify potential mudflow risk areas. The generated freeze-thaw prone areas provide clear goals for subsequent risk management and emergency response, ensure the rational allocation and effective utilization of resources, provide a solid scientific basis for the formulation of disaster prevention and mitigation strategies, and improve the overall response capability to freeze-thaw mudflow risks.

[0030] Preferably, step S4 comprises the following steps:

[0031] Step S41: Analyze the geological characteristics of the freeze-thaw prone area to obtain regional geological characteristic data;

[0032] Step S42: performing geological structure calculation on the regional geological characteristic data to obtain geological structure data;

[0033] Step S43: performing stability analysis on the freeze-thaw prone area according to the geological structure data to obtain a stability assessment data set;

[0034] Step S44: performing mudflow risk identification on the stability assessment data set to generate mudflow risk data.

[0035] The present invention ensures a comprehensive understanding of regional geological conditions by implementing geological characteristic analysis on freeze-thaw prone areas. The obtained regional geological characteristic data provides a necessary basis for subsequent analysis, can reveal the physical and chemical properties of different strata, and enhance the understanding of the mudflow occurrence mechanism. The implementation of geological structure calculation on regional geological characteristic data ensures an in-depth analysis of the geological structure in the region. The obtained geological structure data can accurately describe the distribution and characteristics of the strata, and provide reliable data support for subsequent stability analysis. The implementation of stability analysis on freeze-thaw prone areas based on geological structure data can evaluate the changes in geological structure under the action of freeze-thaw, and the obtained stability assessment data set provides a key basis for the identification of mudflow risks, and can effectively identify potential mudflow occurrence areas. The implementation of mudflow risk identification on the stability assessment data set can systematically evaluate the risk of mudflow occurrence in the region, and the generated mudflow risk data provides an intuitive reference basis for subsequent risk management, so that effective response measures can be taken in time before a disaster occurs, thereby improving the disaster prevention capacity and emergency response efficiency of the region.

[0036] Preferably, step S5 comprises the following steps:

[0037] Step S51: constructing a kinematic model for the freeze-thaw prone area based on the mudflow risk data to obtain a regional kinematic model;

[0038] Step S52: simulating the dynamic changes of the regional kinematic model to obtain simulated mudflow nodes;

[0039] Step S53: performing connection topology analysis on the simulated mudflow nodes to obtain node topology information;

[0040] Step S54: Chain-connect the simulated mudflow nodes according to the node topology information to generate a simulated disaster trajectory.

[0041] The present invention ensures an in-depth understanding of the movement behavior of mudflows by implementing the construction of a kinematic model for freeze-thaw prone areas based on mudflow risk data. The obtained regional kinematic model can accurately describe the movement characteristics of mudflows under different conditions, provide a scientific basis for subsequent dynamic change simulation, and enhance the reliability of risk prediction. The implementation of dynamic change simulation of the regional kinematic model can comprehensively evaluate the behavior of mudflows under different environmental conditions. The obtained simulated mudflow nodes provide important information for understanding the propagation mode and impact range of mudflows, and support the accuracy and scientificity of subsequent analysis. The implementation of connection topology analysis of simulated mudflow nodes can reveal the spatial relationship between mudflow nodes. The obtained node topology information provides structured data for subsequent chain connection, enhances the understanding of mudflow propagation paths, and makes the analysis under complex terrain conditions more systematic. The implementation of chain connection of simulated mudflow nodes according to node topology information ensures accurate reconstruction of mudflow propagation paths. The generated simulated disaster trajectory can clearly depict the movement trajectory and impact range of mudflows, provide a scientific basis for actual disaster risk assessment and emergency response, and effectively improve the efficiency and effectiveness of disaster prevention and mitigation in the region.

[0042] Preferably, step S6 comprises the following steps:

[0043] Step S61: Analyze the elements of the simulated disaster trajectory to obtain disaster element data;

[0044] Step S62: performing disaster risk assessment on the disaster element data to generate disaster risk data;

[0045] Step S63: performing key risk calculation on the disaster risk data to obtain key risk data;

[0046] Step S64: Perform emergency warning processing on key risk data to generate disaster warning data.

[0047] The present invention ensures comprehensive identification of disaster characteristics by implementing factor analysis on simulated disaster trajectories. The obtained disaster factor data provides specific indicators for subsequent risk assessment, can effectively describe the nature and scope of impact of the disaster, and provides a scientific basis for in-depth understanding of the potential impact of mudslide disasters. The implementation of disaster risk assessment on disaster factor data can quantify the impact of mudslide disasters on the region. The generated disaster risk data provides a reliable basis for decision-making, can effectively identify high-risk areas, and provides important support for the rational allocation and optimal management of resources. The implementation of key risk calculation on disaster risk data can focus on the most threatening risk factors. The obtained key risk data provides clear priorities for subsequent emergency warnings, ensuring that the most serious risks can be dealt with first when resources are limited. The implementation of emergency warning processing on key risk data can achieve efficient information transmission. The generated disaster warning data provides timely warnings for relevant departments, ensuring that effective preventive measures can be taken before mudslide disasters occur, reducing the losses caused, and enhancing the region's disaster prevention and mitigation capabilities.

[0048] The present invention also provides a freeze-thaw mudflow chain disaster dynamic simulation system, which is used to execute the freeze-thaw mudflow chain disaster dynamic simulation method as described above. The freeze-thaw mudflow chain disaster dynamic simulation includes:

[0049] The terrain analysis module is used to obtain regional terrain measurement data; perform point cloud conversion on the regional terrain measurement data to obtain terrain point cloud data; perform three-dimensional modeling on the terrain point cloud data to generate a digital terrain model;

[0050] The meteorological analysis module is used to collect regional meteorological data; extract features from regional meteorological data to obtain meteorological characteristic data; perform multi-source fusion processing on the digital terrain model based on the meteorological characteristic data to generate a data fusion model;

[0051] The freeze-thaw area positioning module is used to identify the freeze-thaw critical conditions based on the data fusion model and obtain the freeze-thaw critical condition set; the data fusion model is used to locate the freeze-thaw prone area according to the freeze-thaw critical condition set to generate the freeze-thaw prone area;

[0052] The stability analysis module is used to calculate the geological structure of the freeze-thaw prone area and obtain geological structure data; perform stability analysis on the freeze-thaw prone area based on the geological structure data and generate mudflow risk data;

[0053] The change simulation module is used to simulate the dynamic changes of freeze-thaw prone areas based on mudflow risk data to obtain simulated mudflow nodes; the simulated mudflow nodes are chained to generate simulated disaster trajectories;

[0054] The emergency warning module is used to conduct disaster risk assessment on the simulated disaster trajectory and generate disaster risk data; perform emergency warning processing based on the disaster risk data and generate disaster warning data.

[0055] The present invention ensures the accuracy and comprehensiveness of data by acquiring regional topographic survey data, and generates topographic point cloud data by performing point cloud conversion on regional topographic survey data, which provides a high-precision basis for subsequent three-dimensional modeling. Subsequently, three-dimensional modeling is performed on the topographic point cloud data, and the generated digital terrain model can truly reflect the terrain characteristics of the region, thereby enhancing the reliability of subsequent analysis. The meteorological analysis module provides real-time environmental information by collecting regional meteorological data, and then extracts features from the regional meteorological data. The obtained meteorological feature data can reflect the changes in key meteorological factors. Multi-source fusion processing is performed on the digital terrain model based on the meteorological feature data, and the generated data fusion model integrates meteorological and topographic information, providing a comprehensive basis for the identification of freeze-thaw critical conditions. The freeze-thaw area positioning module identifies freeze-thaw critical conditions based on the data fusion model, ensuring that the conditions for the occurrence of freeze-thaw phenomena can be accurately defined. The obtained freeze-thaw critical condition set provides a clear basis for the subsequent positioning of prone areas. The data fusion model is used to locate freeze-thaw prone areas according to the freeze-thaw critical condition set, and the generated freeze-thaw prone areas can intuitively display potential risk areas. The stability analysis module performs The geological structure calculation of the freeze-thaw prone area ensures a deep understanding of the regional geological characteristics. The obtained geological structure data provides a necessary basis for the subsequent stability analysis. The stability analysis of the freeze-thaw prone area is carried out based on these data. The mudflow risk data generated can effectively identify the potential risk of mudflow, and provide a scientific basis for disaster prevention and mitigation measures in the region. The change simulation module simulates the dynamic changes of the freeze-thaw prone area based on the mudflow risk data, and can comprehensively evaluate the behavior pattern of mudflow. The obtained simulated mudflow nodes provide a basis for subsequent chain connection. The simulated disaster trajectory generated by chain connection of the simulated mudflow nodes can clearly depict the propagation path of mudflow, providing important data support for actual risk assessment and prediction. The emergency warning module conducts disaster risk assessment on the simulated disaster trajectory to ensure that the potential impact of mudflow on the region can be quantified. The generated disaster risk data provides a basis for emergency management. The disaster warning data generated by emergency warning processing based on disaster risk data provides timely information for relevant departments, ensuring that effective response measures are taken before mudflow disasters occur, reducing the losses caused, and improving the efficiency and effectiveness of disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the steps of a dynamic simulation method for freeze-thaw mudflow chain disaster;

[0057] Figure 2 Detailed implementation flow chart of step S2;

[0058] Figure 3 is a schematic diagram of a detailed implementation process of step S3;

[0059] 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

[0060] 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.

[0061] 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.

[0062] 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.

[0063] To achieve this, please refer to Figures 1 to 3 A method for simulating the dynamics of freeze-thaw mudflow chain disasters comprises the following steps:

[0064] Step S1: acquiring regional topographic survey data; performing point cloud conversion on the regional topographic survey data to obtain topographic point cloud data; performing three-dimensional modeling on the topographic point cloud data to generate a digital terrain model;

[0065] Step S2: collecting regional meteorological data; extracting features from the regional meteorological data to obtain meteorological feature data; performing multi-source fusion processing on the digital terrain model based on the meteorological feature data to generate a data fusion model;

[0066] Step S3: identifying freeze-thaw critical conditions based on the data fusion model to obtain a freeze-thaw critical condition set; locating the freeze-thaw prone area based on the freeze-thaw critical condition set using the data fusion model to generate a freeze-thaw prone area;

[0067] Step S4: Calculate the geological structure of the freeze-thaw prone area to obtain geological structure data; perform stability analysis on the freeze-thaw prone area based on the geological structure data to generate mudflow risk data;

[0068] Step S5: Based on the mudflow risk data, dynamic changes of the freeze-thaw prone area are simulated to obtain simulated mudflow nodes; the simulated mudflow nodes are chained to generate simulated disaster trajectories;

[0069] Step S6: Conduct disaster risk assessment on the simulated disaster trajectory to generate disaster risk data; perform emergency warning processing based on the disaster risk data to generate disaster warning data.

[0070] The present invention ensures the accuracy and comprehensiveness of data by acquiring regional topographic survey data. By performing point cloud conversion on regional topographic survey data, the terrain point cloud data obtained provides a high-resolution basis for three-dimensional modeling. The terrain point cloud data is subsequently subjected to three-dimensional modeling, and the generated digital terrain model can truly reflect the terrain characteristics of the region, thereby improving the reliability of subsequent analysis and laying a solid foundation for mudflow risk assessment. The implementation of collecting regional meteorological data ensures the real-time nature of meteorological information. By performing feature extraction on regional meteorological data, the obtained meteorological characteristic data can reflect the changes in important meteorological factors. Based on the meteorological characteristic data, multi-source fusion processing is performed on the digital terrain model, and the generated data fusion model integrates terrain and meteorological information, providing a more comprehensive basis for the subsequent identification of freeze-thaw critical conditions. The implementation of freeze-thaw critical condition identification based on the data fusion model ensures that the conditions for the occurrence of freeze-thaw phenomena can be accurately defined. The obtained freeze-thaw critical condition set provides a clear basis for the subsequent positioning of prone areas. The data fusion model is used to locate freeze-thaw prone areas according to the freeze-thaw critical condition set, and the generated freeze-thaw prone areas can intuitively display potential risk areas. The implementation of geological structure calculations in freeze-thaw prone areas ensures an in-depth understanding of regional geological characteristics. The obtained geological structure data provides a necessary basis for subsequent stability analysis. By conducting stability analysis on freeze-thaw prone areas based on geological structure data, the mudflow risk data generated can effectively identify potential mudflow risks and provide a scientific basis for disaster prevention and mitigation measures in the region. The implementation of dynamic change simulation of freeze-thaw prone areas based on mudflow risk data can comprehensively evaluate the behavior pattern of mudflows. The obtained simulated mudflow nodes provide a basis for subsequent chain connections. The simulated disaster trajectory generated by chain connection of simulated mudflow nodes can clearly depict the propagation path of mudflows and provide important data support for actual risk assessment and prediction. The implementation of disaster risk assessment on simulated disaster trajectories can quantify the potential impact of mudflows on the region. The generated disaster risk data provides a basis for emergency management. The disaster warning data generated by emergency warning processing based on disaster risk data can provide timely information to relevant departments, ensuring that effective response measures are taken before mudflow disasters occur, reducing the losses caused, and improving the efficiency and effectiveness of disaster prevention and mitigation.

[0071] In the embodiment of the present invention, refer to Figure 1 , is a schematic diagram of the steps of the freeze-thaw mudflow chain disaster dynamic simulation method of the present invention. In this example, the freeze-thaw mudflow chain disaster dynamic simulation method includes the following steps:

[0072] Step S1: acquiring regional topographic survey data; performing point cloud conversion on the regional topographic survey data to obtain topographic point cloud data; performing three-dimensional modeling on the topographic point cloud data to generate a digital terrain model;

[0073] In this embodiment, laser radar (LiDAR) technology is used to conduct regional topographic surveys, and multiple measurement points are set to ensure coverage of the entire study area. After the data is acquired, point cloud conversion is performed using professional software (such as CloudCompare or Pix4D) to generate high-precision topographic point cloud data. Then, these point cloud data are cleaned and processed to remove noise and outliers to ensure data quality. Subsequently, a digital terrain model is constructed based on the processed point cloud data using 3D modeling software (such as ArcGIS or AutoCAD Civil 3D). The model can accurately display terrain features such as ridges, valleys, and slope changes, forming a comprehensive understanding of the regional terrain.

[0074] Step S2: collecting regional meteorological data; extracting features from the regional meteorological data to obtain meteorological feature data; performing multi-source fusion processing on the digital terrain model based on the meteorological feature data to generate a data fusion model;

[0075] In this embodiment, a meteorological monitoring station is installed in the same area to regularly collect meteorological data including temperature, humidity, precipitation, wind speed, etc. to ensure the timeliness and accuracy of the data. Feature extraction is performed on the collected meteorological data to identify key meteorological indicators, such as the average annual temperature and the frequency of extreme precipitation events. Data analysis tools (such as Python or R) are used for statistical analysis to generate a time series of meteorological changes. Subsequently, the meteorological characteristic data is multi-source fused with the digital terrain model, and a data fusion model is generated using GIS software (such as QGIS or ArcGIS) to derive the relationship between meteorology and terrain, thereby providing a comprehensive information basis for subsequent analysis.

[0076] Step S3: identifying freeze-thaw critical conditions based on the data fusion model to obtain a freeze-thaw critical condition set; locating the freeze-thaw prone area based on the freeze-thaw critical condition set using the data fusion model to generate a freeze-thaw prone area;

[0077] In this embodiment, based on the generated data fusion model, meteorological factors such as temperature and humidity are analyzed to identify critical conditions of freeze-thaw phenomena, such as daily temperature difference, number of continuous low temperature days, etc., to obtain a set of freeze-thaw critical conditions. The data fusion model is analyzed using spatial analysis tools to identify areas prone to freeze-thaw under specific meteorological conditions. These potential risk areas are marked through a geographic information system, and a risk map is generated to provide decision support for subsequent risk management.

[0078] Step S4: Calculate the geological structure of the freeze-thaw prone area to obtain geological structure data; perform stability analysis on the freeze-thaw prone area based on the geological structure data to generate mudflow risk data;

[0079] In this embodiment, geological characteristics analysis is performed on the identified freeze-thaw prone areas, geological data in the area are collected, including rock and soil properties, stratigraphic distribution and other information, and geological structure calculations are performed using geological analysis software (such as GeoStudio or PLAXIS). After obtaining detailed geological structure data, stability analysis is performed to evaluate the impact of freeze-thaw on soil and rock, calculate shear strength, permeability and other parameters, generate mudflow risk data, identify high-risk areas most prone to mudflow under freeze-thaw conditions, and provide a basis for subsequent disaster prevention and mitigation measures.

[0080] Step S5: Based on the mudflow risk data, dynamic changes of the freeze-thaw prone area are simulated to obtain simulated mudflow nodes; the simulated mudflow nodes are chained to generate simulated disaster trajectories;

[0081] In this embodiment, based on mudflow risk data, a regional kinematic model is constructed, and dynamic simulation software (such as MATLAB or ANSYS) is used to simulate the dynamic changes of mudflows, evaluate the flow behavior of mudflows under different conditions, and obtain simulated mudflow nodes. Then, connection topology analysis is performed to identify the spatial relationship and connection mode between mudflow nodes, and generate node topology information. Based on this topological information, the simulated mudflow nodes are chained to generate simulated disaster trajectories, depicting the potential propagation paths of mudflows under different meteorological and geological conditions, providing decision makers with an intuitive understanding of mudflow disasters.

[0082] Step S6: Conduct disaster risk assessment on the simulated disaster trajectory to generate disaster risk data; perform emergency warning processing based on the disaster risk data to generate disaster warning data.

[0083] In this embodiment, a disaster risk assessment is performed on the simulated disaster trajectory to analyze the impact of mudslides on the surrounding environment and human activities, including potential threats to infrastructure, the ecological environment and the safety of residents. The generated disaster risk data contains information such as risk level and potential loss assessment, which supports relevant departments in formulating emergency response plans. Subsequently, emergency warning processing is performed based on the disaster risk data, disaster warning data is generated, and warning information is promptly issued to local residents and relevant departments to ensure that the public can take appropriate preventive measures before the mudslide disaster occurs, minimize losses, and enhance the region's disaster prevention and mitigation capabilities.

[0084] Preferably, step S1 comprises the following steps:

[0085] Step S11: acquiring regional topographic survey data; reconstructing the spatial coordinates of the regional topographic survey data to obtain spatial coordinate data;

[0086] Step S12: performing anomaly detection on the spatial coordinate data to generate abnormal coordinate data;

[0087] Step S13: removing abnormal values ​​from the spatial coordinate data according to the abnormal coordinate data to obtain terrain point cloud data; performing topological relationship analysis on the terrain point cloud data to obtain point cloud topological relationships;

[0088] Step S14: Perform three-dimensional modeling on the terrain point cloud data based on the point cloud topological relationship to generate a digital terrain model.

[0089] In this embodiment, the terrain within the regional range is measured by a laser scanner, and the original measurement data of the terrain, including information such as elevation, slope and surface reflectivity, is collected. The collected measurement data is georeferenced and calibrated by a differential GPS system. The measurement data is converted into a three-dimensional spatial coordinate form by a coordinate conversion algorithm. The converted data is processed in blocks and its accuracy is adjusted to ensure the consistency of subsequent processing. The spatial coordinate reconstruction is completed and the spatial coordinate data is obtained. The spatial coordinate data is detected for anomalies. The density-based spatial clustering algorithm (DBSCAN) is used to identify isolated points and noise points in the coordinate data. The local density of each coordinate point and its difference with the neighboring points are calculated through statistical analysis. The probability of the abnormal point is detected according to the density distribution and marked. The weighted least squares method is used to verify the overall trend of the data in combination with the distribution law of the abnormal point to generate abnormal coordinate data. The spatial coordinate data is detected for anomalies according to the abnormal coordinate data. The point cloud data is formed by calibrating the spatial connection relationship between points in the terrain point cloud data using topological analysis tools, generating a point cloud topological map, extracting the topological relationship information between points and surfaces, obtaining the point cloud topological relationship, and performing 3D modeling of the terrain point cloud data based on the point cloud topological relationship. The point cloud data is reconstructed using a layer-by-layer grid generation algorithm, and a 3D grid structure of the terrain surface is generated using a triangulation method. The grid surface is smoothed and optimized using normal vector calculation to reduce the impact of noise on the modeling results. The characteristic attributes of the regional terrain are superimposed on the 3D model through texture mapping technology, and the generated 3D model is adjusted for multi-scale resolution and data is compressed to complete the construction of the digital terrain model and generate a standardized output format.

[0090] Preferably, step S2 comprises the following steps:

[0091] Step S21: Defining the monitoring range according to the digital terrain model to obtain monitoring range data; deploying equipment monitoring based on the monitoring range data to obtain regional meteorological data;

[0092] Step S22: performing feature extraction processing on the regional meteorological data to obtain meteorological feature data; performing time series analysis on the meteorological feature data to generate meteorological change trends;

[0093] Step S23: performing feature correlation processing on the meteorological feature data and the meteorological change trend to obtain meteorological feature correlation data;

[0094] Step S24: Perform multi-source superposition fusion on the meteorological feature correlation data and the digital terrain model to generate a data fusion model.

[0095] In this embodiment, based on the digital terrain model, GIS software (such as ArcGIS) is used to define the monitoring range, analyze the important terrain features in the region, determine the key areas to be monitored, and obtain the monitoring range data. Subsequently, the meteorological monitoring equipment is reasonably deployed based on the monitoring range data, and the meteorological monitoring station is installed at a suitable location to ensure that the equipment can obtain comprehensive regional meteorological data. After the equipment is deployed, meteorological data is collected regularly, including temperature, humidity, precipitation, wind speed, etc. The collected regional meteorological data is subjected to feature extraction processing, and the meteorological data is analyzed using data processing tools (such as Python's Pandas library) to extract key indicators, such as the annual average temperature, the frequency of extreme weather events, etc., to generate meteorological feature data. Subsequently, the meteorological feature data is subjected to time series analysis, and statistical analysis methods (such as the ARIMA model) are applied to generate meteorological change trends, identify the changing laws of meteorological conditions, and provide important information for subsequent analysis. Perform feature association processing on meteorological characteristic data and meteorological change trends, use association rule mining algorithms (such as Apriori algorithm) to analyze the relationship between different meteorological factors, obtain meteorological characteristic association data, ensure that the mutual influence between key meteorological variables can be identified, form a more comprehensive description of meteorological characteristics, and provide a basis for subsequent data fusion. Multi-source superposition and fusion of meteorological characteristic association data and digital terrain models, use GIS software (such as QGIS or ArcGIS) for data superposition, integrate meteorological data and terrain data, and generate a data fusion model. This model can comprehensively reflect the meteorological changes and terrain characteristics in the region, ensure that a comprehensive information basis is provided for subsequent analysis, and support more complex simulation and decision-making needs.

[0096] Preferably, step S3 comprises the following steps:

[0097] Step S31: Analyze the temperature change characteristics of the data fusion model to obtain a temperature change data set;

[0098] Step S32: identifying freeze-thaw critical conditions for the temperature change data set according to a preset critical temperature threshold to obtain a freeze-thaw critical condition set;

[0099] Step S33: performing spatial coordinate matching on the freeze-thaw critical condition set and the data fusion model to obtain a superimposed data set;

[0100] Step S34: positioning the freeze-thaw prone areas on the superimposed data set to generate freeze-thaw prone areas.

[0101] In this embodiment, the generated data fusion model is subjected to temperature change characteristic analysis, and the temperature data is processed using data analysis tools (such as R or Python), and the temperature change trend and fluctuation range are extracted to generate a temperature change data set, so as to ensure that the data set can reflect the temperature change situation in different time periods, and provide basic data for subsequent freeze-thaw condition identification. According to the preset critical temperature threshold, the temperature change data set is subjected to freeze-thaw critical condition identification, and the set threshold includes the range of variation of the lowest temperature at night and the highest temperature during the day. The time period that meets the freeze-thaw conditions is screened out using the condition judgment algorithm, and the freeze-thaw critical condition set is obtained to ensure that the identified data can accurately reflect the potential risk of freeze-thaw phenomenon. The freeze-thaw critical condition set and the data fusion model are spatially matched, and the two are spatially analyzed using GIS software (such as ArcGIS or QGIS) to ensure that the freeze-thaw critical condition can be combined with the corresponding geographical location, and an overlay data set is generated, which integrates meteorological and topographic information, and provides a basis for subsequent regional positioning. The overlay dataset is used to locate areas prone to freeze-thaw events, and spatial analysis tools (such as ArcGIS SpatialAnalyst) are used to perform regional analysis to identify areas most prone to freeze-thaw events under specific meteorological and topographic conditions, and to generate risk maps of areas prone to freeze-thaw events to ensure that clear target areas can be provided for subsequent risk assessment and emergency management.

[0102] Preferably, step S4 comprises the following steps:

[0103] Step S41: Analyze the geological characteristics of the freeze-thaw prone area to obtain regional geological characteristic data;

[0104] Step S42: performing geological structure calculation on the regional geological characteristic data to obtain geological structure data;

[0105] Step S43: performing stability analysis on the freeze-thaw prone area according to the geological structure data to obtain a stability assessment data set;

[0106] Step S44: performing mudflow risk identification on the stability assessment data set to generate mudflow risk data.

[0107] In this embodiment, the surface geological characteristics of the freeze-thaw prone area are preliminarily identified through remote sensing image data, the geological mineral distribution information of the area is obtained by using hyperspectral analysis technology, and the regional geological information is classified and extracted in combination with the surface erosion degree, soil type and other characteristics. At the same time, geological survey instruments are used to obtain the rock layer thickness and soil layer structure data inside the region, and the collected geological data are formatted and cleaned to obtain regional geological characteristic data. The stress distribution of the regional geological characteristic data is simulated and calculated by the finite element method, and the deformation characteristics and stress response of the rock layers in the region under different environmental conditions are simulated. The fracture characteristics and potential slip surface distribution between the rock layers are calculated using a multi-scale geological structure analysis algorithm, and the sliding characteristics of the surface soil are mechanically analyzed in combination with the soil shear strength model. The calculation results are aggregated and visualized to obtain the regional geological structure data. Based on the geological structure data of the region, a geological stability analysis model is established. The friction coefficient and cohesion parameters between the sliding body and the bedrock interface are used to evaluate the sliding risk of the region. The shear stress distribution and pore water pressure inside the rock layer are combined to perform dynamic simulation calculations on the stability of the soil. The key geological stability indicators are extracted by principal component analysis and weighted calculations are performed. The spatiotemporal correlation analysis of the calculation results is performed to obtain the regional stability assessment data set. The freeze-thaw mudflow risk is identified for the stability assessment data set. The support vector machine algorithm is used to classify and predict the probability of mudflow in different risk areas. The potential mudflow path and accumulation area are simulated and analyzed in combination with meteorological data. The gradient boosting decision tree is used to rank the importance of influencing factors and optimize the classification criteria. The generated risk assessment results are graded and mudflow risk data is generated to ensure a unified data format and clear visualization.

[0108] Preferably, step S5 comprises the following steps:

[0109] Step S51: constructing a kinematic model for the freeze-thaw prone area based on the mudflow risk data to obtain a regional kinematic model;

[0110] Step S52: simulating the dynamic changes of the regional kinematic model to obtain simulated mudflow nodes;

[0111] Step S53: performing connection topology analysis on the simulated mudflow nodes to obtain node topology information;

[0112] Step S54: Chain-connect the simulated mudflow nodes according to the node topology information to generate a simulated disaster trajectory.

[0113] In this embodiment, risk points and mudflow starting positions in the region are extracted based on mudflow risk data, basic kinematic equations are established by analyzing the terrain undulations and slope changes in the mudflow path, geographic information systems are used to divide the study area into spatial grids and calculate the motion parameters of each grid, such as flow velocity, direction, inertial force, etc., the motion characteristics under different soil types and climatic conditions are determined through parameter calibration, and a kinematic model suitable for freeze-thaw prone areas is constructed by integrating regional environmental data and mudflow physical characteristics to obtain a regional kinematic model, based on the regional kinematic model, numerical simulation technology is used to perform step-by-step iterative calculations on the dynamic propagation process of the mudflow, the Reynolds equation is used to simulate the flow characteristics of different particles in the mudflow and calculate the velocity field and pressure distribution of the mudflow, the mudflow diffusion range and propagation time are predicted by simulating the dynamic changes of the mudflow under different terrains, precipitation and temperature conditions, the key nodes of the mudflow are determined in combination with the simulation results, and the node characteristics are labeled. The simulated mudflow nodes are processed and the connection topology analysis of the simulated mudflow nodes is carried out by graph theory algorithm. Firstly, the preliminary connection network between nodes is generated by Delaunay triangulation method, and the mudflow propagation path is optimized by using the shortest path algorithm. The weight of the node connection is calculated by combining the mudflow velocity and the node stability parameter. The main and secondary paths of mudflow propagation are identified by weight analysis. The connection patterns between nodes are graded and marked to generate topological map information to obtain the node topology information. The simulated mudflow nodes are chained according to the node topology information. All chain paths are found by the depth-first search algorithm. The optimal disaster trajectory is determined by combining the geographical distribution of the nodes and the connection weight. The mudflow intensity, propagation range and accumulation area of ​​different paths are comprehensively analyzed. The chain connections are grouped by hierarchical clustering method to generate a set of disaster trajectories. The simulation results are marked and verified in combination with the final path to generate a simulated disaster trajectory.

[0114] Preferably, step S6 comprises the following steps:

[0115] Step S61: Analyze the elements of the simulated disaster trajectory to obtain disaster element data;

[0116] Step S62: performing disaster risk assessment on the disaster element data to generate disaster risk data;

[0117] Step S63: performing key risk calculation on the disaster risk data to obtain key risk data;

[0118] Step S64: Perform emergency warning processing on key risk data to generate disaster warning data.

[0119] In this embodiment, the simulated disaster trajectory is decomposed into path elements. By extracting information such as node distribution, path length, slope change and surface characteristics in the trajectory, the geological structure, vegetation coverage and population density along the trajectory are superimposed and analyzed using a distributed computing method. The high-risk area of ​​the disaster trajectory is marked in combination with the changes in velocity field and pressure distribution during trajectory propagation. The key elements that affect the diffusion range of mudflow in the trajectory are extracted through statistical analysis and converted into structured data to obtain disaster element data. The disaster risk is assessed on the disaster element data. A risk assessment model is constructed based on historical disaster data. The probability of disaster occurrence is calculated using the Bayesian reasoning method. The intensity, impact range and loss of mudflows in different regions are quantitatively analyzed in combination with geological and climatic conditions. The disaster elements are associated with the economic, infrastructure and population distribution data of the region through spatial overlay technology to generate a regional graded disaster risk map and convert it into a disaster risk number. Based on the disaster risk data, key risks are calculated. First, the hierarchical analysis method is used to assign weights to different risk factors, and the key factors that have the greatest impact on disasters are extracted. The optimization algorithm based on spatiotemporal analysis is used to simulate the high-risk areas for disaster propagation, and the hazard level, affected population and economic losses in the key risk areas are calculated. The risk level distribution map is obtained through data aggregation and modeling, and the key risk point information is extracted to generate key risk data. Emergency warning processing is performed on the key risk data. A dynamic warning model is constructed by combining key risk data with real-time meteorological information. The short-term prediction algorithm is used to simulate the evolution trend of disasters in high-risk areas. The warning response level is classified in combination with the risk level and propagation speed, and detailed emergency response suggestions are generated. The regional distributed warning information map is generated through the geographic information system and converted into disaster warning data in a unified format to ensure that the data is accurate and can be used for the rapid release of disaster warnings.

[0120] The present invention also provides a freeze-thaw mudflow chain disaster dynamic simulation system, which is used to execute the freeze-thaw mudflow chain disaster dynamic simulation method as described above. The freeze-thaw mudflow chain disaster dynamic simulation includes:

[0121] The terrain analysis module is used to obtain regional terrain measurement data; perform point cloud conversion on the regional terrain measurement data to obtain terrain point cloud data; perform three-dimensional modeling on the terrain point cloud data to generate a digital terrain model;

[0122] The meteorological analysis module is used to collect regional meteorological data; extract features from regional meteorological data to obtain meteorological characteristic data; perform multi-source fusion processing on the digital terrain model based on the meteorological characteristic data to generate a data fusion model;

[0123] The freeze-thaw area positioning module is used to identify the freeze-thaw critical conditions based on the data fusion model and obtain the freeze-thaw critical condition set; the data fusion model is used to locate the freeze-thaw prone area according to the freeze-thaw critical condition set to generate the freeze-thaw prone area;

[0124] The stability analysis module is used to calculate the geological structure of the freeze-thaw prone area and obtain geological structure data; perform stability analysis on the freeze-thaw prone area based on the geological structure data and generate mudflow risk data;

[0125] The change simulation module is used to simulate the dynamic changes of freeze-thaw prone areas based on mudflow risk data to obtain simulated mudflow nodes; the simulated mudflow nodes are chained to generate simulated disaster trajectories;

[0126] The emergency warning module is used to conduct disaster risk assessment on the simulated disaster trajectory and generate disaster risk data; perform emergency warning processing based on the disaster risk data and generate disaster warning data.

[0127] The present invention ensures the accuracy and comprehensiveness of data by acquiring regional topographic survey data, and generates topographic point cloud data by performing point cloud conversion on regional topographic survey data, which provides a high-precision basis for subsequent three-dimensional modeling. Subsequently, three-dimensional modeling is performed on the topographic point cloud data, and the generated digital terrain model can truly reflect the terrain characteristics of the region, thereby enhancing the reliability of subsequent analysis. The meteorological analysis module provides real-time environmental information by collecting regional meteorological data, and then extracts features from the regional meteorological data. The obtained meteorological feature data can reflect the changes in key meteorological factors. Multi-source fusion processing is performed on the digital terrain model based on the meteorological feature data, and the generated data fusion model integrates meteorological and topographic information, providing a comprehensive basis for the identification of freeze-thaw critical conditions. The freeze-thaw area positioning module identifies freeze-thaw critical conditions based on the data fusion model, ensuring that the conditions for the occurrence of freeze-thaw phenomena can be accurately defined. The obtained freeze-thaw critical condition set provides a clear basis for the subsequent positioning of prone areas. The data fusion model is used to locate freeze-thaw prone areas according to the freeze-thaw critical condition set, and the generated freeze-thaw prone areas can intuitively display potential risk areas. The stability analysis module performs The geological structure calculation of the freeze-thaw prone area ensures a deep understanding of the regional geological characteristics. The obtained geological structure data provides a necessary basis for the subsequent stability analysis. The stability analysis of the freeze-thaw prone area is carried out based on these data. The mudflow risk data generated can effectively identify the potential risk of mudflow, and provide a scientific basis for disaster prevention and mitigation measures in the region. The change simulation module simulates the dynamic changes of the freeze-thaw prone area based on the mudflow risk data, and can comprehensively evaluate the behavior pattern of mudflow. The obtained simulated mudflow nodes provide a basis for subsequent chain connection. The simulated disaster trajectory generated by chain connection of the simulated mudflow nodes can clearly depict the propagation path of mudflow, providing important data support for actual risk assessment and prediction. The emergency warning module conducts disaster risk assessment on the simulated disaster trajectory to ensure that the potential impact of mudflow on the region can be quantified. The generated disaster risk data provides a basis for emergency management. The disaster warning data generated by emergency warning processing based on disaster risk data provides timely information for relevant departments, ensuring that effective response measures are taken before mudflow disasters occur, reducing the losses caused, and improving the efficiency and effectiveness of disaster prevention and mitigation.

[0128] 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 therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0129] 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 method for simulating the dynamics of freeze-thaw mudflow chain disasters, characterized in that: The following steps are involved: Step S1: acquiring regional topographic survey data; performing point cloud conversion on the regional topographic survey data to obtain topographic point cloud data; performing three-dimensional modeling on the topographic point cloud data to generate a digital terrain model; Step S2: collecting regional meteorological data; extracting features from the regional meteorological data to obtain meteorological feature data; performing multi-source fusion processing on the digital terrain model based on the meteorological feature data to generate a data fusion model; Step S3: identifying freeze-thaw critical conditions based on the data fusion model to obtain a freeze-thaw critical condition set; locating the freeze-thaw prone area based on the freeze-thaw critical condition set using the data fusion model to generate a freeze-thaw prone area; Step S4: Calculate the geological structure of the freeze-thaw prone area to obtain geological structure data; Conduct stability analysis on freeze-thaw prone areas based on geological structure data and generate mudflow risk data; Step S5: simulating the dynamic changes of the freeze-thaw prone area based on the mudflow risk data to obtain simulated mudflow nodes; Chain the simulated mudflow nodes to generate simulated disaster trajectories; Step S6: Conduct disaster risk assessment on the simulated disaster trajectory to generate disaster risk data; Carry out emergency warning processing based on disaster risk data to generate disaster warning data.

2. The freeze-thaw mudflow chain disaster dynamic simulation method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: acquiring regional topographic survey data; reconstructing the spatial coordinates of the regional topographic survey data to obtain spatial coordinate data; Step S12: performing anomaly detection on the spatial coordinate data to generate abnormal coordinate data; Step S13: removing abnormal values ​​from the spatial coordinate data according to the abnormal coordinate data to obtain terrain point cloud data; performing topological relationship analysis on the terrain point cloud data to obtain point cloud topological relationships; Step S14: Perform three-dimensional modeling on the terrain point cloud data based on the point cloud topological relationship to generate a digital terrain model.

3. The freeze-thaw mudflow chain disaster dynamic simulation method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Defining the monitoring range according to the digital terrain model to obtain monitoring range data; deploying equipment monitoring based on the monitoring range data to obtain regional meteorological data; Step S22: performing feature extraction processing on the regional meteorological data to obtain meteorological feature data; performing time series analysis on the meteorological feature data to generate meteorological change trends; Step S23: performing feature correlation processing on the meteorological feature data and the meteorological change trend to obtain meteorological feature correlation data; Step S24: Perform multi-source superposition fusion on the meteorological feature correlation data and the digital terrain model to generate a data fusion model.

4. The freeze-thaw mudflow chain disaster dynamic simulation method according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Analyze the temperature change characteristics of the data fusion model to obtain a temperature change data set; Step S32: identifying freeze-thaw critical conditions for the temperature change data set according to a preset critical temperature threshold to obtain a freeze-thaw critical condition set; Step S33: performing spatial coordinate matching on the freeze-thaw critical condition set and the data fusion model to obtain a superimposed data set; Step S34: positioning the freeze-thaw prone areas on the superimposed data set to generate freeze-thaw prone areas.

5. The freeze-thaw mudflow chain disaster dynamic simulation method according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Analyze the geological characteristics of the freeze-thaw prone area to obtain regional geological characteristic data; Step S42: performing geological structure calculation on the regional geological characteristic data to obtain geological structure data; Step S43: performing stability analysis on the freeze-thaw prone area according to the geological structure data to obtain a stability assessment data set; Step S44: performing mudflow risk identification on the stability assessment data set to generate mudflow risk data.

6. The freeze-thaw mudflow chain disaster dynamic simulation method according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: constructing a kinematic model for the freeze-thaw prone area based on the mudflow risk data to obtain a regional kinematic model; Step S52: simulating the dynamic changes of the regional kinematic model to obtain simulated mudflow nodes; Step S53: performing connection topology analysis on the simulated mudflow nodes to obtain node topology information; Step S54: Chain-connect the simulated mudflow nodes according to the node topology information to generate a simulated disaster trajectory.

7. The freeze-thaw mudflow chain disaster dynamic simulation method according to claim 1 is characterized in that: Step S6 includes the following steps: Step S61: Analyze the elements of the simulated disaster trajectory to obtain disaster element data; Step S62: performing disaster risk assessment on the disaster element data to generate disaster risk data; Step S63: performing key risk calculation on the disaster risk data to obtain key risk data; Step S64: Perform emergency warning processing on key risk data to generate disaster warning data.

8. A freeze-thaw mudflow chain disaster dynamic simulation system, characterized in that: Used to execute the freeze-thaw mudflow chain disaster dynamic simulation method as claimed in claim 1, the freeze-thaw mudflow chain disaster dynamic simulation system comprises: The terrain analysis module is used to obtain regional terrain measurement data; perform point cloud conversion on the regional terrain measurement data to obtain terrain point cloud data; perform three-dimensional modeling on the terrain point cloud data to generate a digital terrain model; The meteorological analysis module is used to collect regional meteorological data; extract features from regional meteorological data to obtain meteorological characteristic data; perform multi-source fusion processing on the digital terrain model based on the meteorological characteristic data to generate a data fusion model; The freeze-thaw area positioning module is used to identify the freeze-thaw critical conditions based on the data fusion model and obtain the freeze-thaw critical condition set; the data fusion model is used to locate the freeze-thaw prone area according to the freeze-thaw critical condition set to generate the freeze-thaw prone area; The stability analysis module is used to calculate the geological structure of the freeze-thaw prone area and obtain geological structure data; perform stability analysis on the freeze-thaw prone area based on the geological structure data and generate mudflow risk data; The change simulation module is used to simulate the dynamic changes of freeze-thaw prone areas based on mudflow risk data to obtain simulated mudflow nodes; the simulated mudflow nodes are chained to generate simulated disaster trajectories; The emergency warning module is used to conduct disaster risk assessment on the simulated disaster trajectory and generate disaster risk data; perform emergency warning processing based on the disaster risk data and generate disaster warning data.

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