A geological disaster monitoring and early warning method and system based on digital twinning technology

Abstract

A geological disaster monitoring and early warning method and system based on digital twin technology, relating to the technical field of data monitoring. The method comprises: acquiring satellite image information, meteorological information and geological exploration information of a to-be-monitored mountain area within a preset sampling period; based on the satellite image information, meteorological information and geological exploration information, a three-dimensional digital model of the to-be-monitored mountain area is constructed; receiving current mountain data, geological data and weather conditions of the to-be-monitored mountain area, combining the mountain data and the geological data, determining a plurality of risk sources and positions corresponding to each risk source of the to-be-monitored mountain area under the weather conditions in the three-dimensional digital model; for each risk source, when the change rate of any monitoring index in the mountain data or the geological data of the risk source exceeds the change rate threshold, a disaster warning is performed based on the position of the risk source. The technical solution provided in the application achieves the effect of improving the accuracy of geological disaster monitoring and early warning.

Description

Technical Field

[0001] This application relates to the field of data monitoring technology, specifically to a geological disaster monitoring and early warning method, system, electronic device and storage medium based on digital twin technology. Background Technology

[0002] With the increasing frequency of geological disasters, such as landslides and mudslides, the safety of people's lives and property has been greatly threatened. Therefore, an effective geological disaster monitoring and early warning system is crucial for identifying potential risks in advance and taking preventive measures. The purpose of such monitoring systems is to reduce the losses caused by disasters and ensure public safety.

[0003] Currently, existing methods for monitoring and early warning of geological disasters rely on monitoring data collected by ground monitoring stations to analyze the mountainous areas to be monitored and issue warnings. However, in practical applications, due to the variability of the geological environment, monitoring data solely through ground monitoring stations, and relying on a single dimension for monitoring, often results in errors compared to the actual geological changes in the mountainous areas to be monitored, leading to low accuracy in geological disaster monitoring and early warning. Summary of the Invention

[0004] This application provides a geological disaster monitoring and early warning method, system, electronic device and storage medium based on digital twin technology, which can improve the accuracy of geological disaster monitoring and early warning.

[0005] Firstly, this application provides a geological disaster monitoring and early warning method based on digital twin technology, including:

[0006] Acquire satellite imagery, meteorological information, and geological survey information of the mountain area to be monitored within a preset sampling period;

[0007] Based on the satellite image information, meteorological information, and geological exploration information, a three-dimensional digital model of the mountain area to be monitored is constructed.

[0008] Receive current mountain data, geological data, and weather conditions of the mountain area to be monitored; combine the mountain data and the geological data to determine multiple risk sources of the mountain area to be monitored under the weather conditions and the corresponding locations of each risk source in the three-dimensional digital model.

[0009] For each of the aforementioned risk sources, when the rate of change of any monitoring indicator in the mountain data or geological data of the risk source exceeds the rate of change threshold, a disaster warning is issued based on the location of the risk source.

[0010] A second aspect of this application provides a geological disaster monitoring and early warning system based on digital twin technology, the system comprising:

[0011] The information acquisition module is used to acquire satellite image information, meteorological information, and geological exploration information of the mountain area to be monitored within a preset sampling period;

[0012] The digital model determination module is used to construct a three-dimensional digital model of the mountain area to be monitored based on the satellite image information, meteorological information, and geological exploration information.

[0013] The risk source identification module is used to receive the current mountain data, geological data and weather conditions of the mountain area to be monitored, and combine the mountain data and the geological data to determine multiple risk sources of the mountain area to be monitored under the weather conditions and the corresponding locations of each risk source in the three-dimensional digital model.

[0014] The disaster early warning module is used to issue a disaster early warning based on the location of each of the aforementioned risk sources when the rate of change of any monitoring indicator in the mountain data or geological data of the risk source exceeds the rate of change threshold.

[0015] A third aspect of this application provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, which, when loaded and executed by the processor, implements a geological disaster monitoring and early warning method based on digital twin technology.

[0016] In a fourth aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement a geological disaster monitoring and early warning method based on digital twin technology.

[0017] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0018] By employing the aforementioned technical solution, satellite images, meteorological information, and geological survey data from multiple dimensions are acquired for the monitored mountain area. The fusion of these multi-dimensional data allows for a more comprehensive and accurate assessment of the mountain's morphology and geological structure under the influence of weather conditions, leading to more precise identification of risk sources. Real-time monitoring of the rate of change in mountain and geological data enables dynamic early warning systems targeting risk sources, along with the dissemination of corresponding location information, resulting in highly targeted intelligent early warning for mountain disasters. This technical solution, combined with digital twin technology—generating a three-dimensional digital model of the monitored mountain area from multi-dimensional data—leverages the advantages of multi-source data, making mountain condition assessments more precise and risk identification more accurate and dynamic, thereby improving the accuracy of geological disaster monitoring and early warning. Attached Figure Description

[0019] Figure 1This is a flowchart illustrating a geological disaster monitoring and early warning method based on digital twin technology provided in an embodiment of this application;

[0020] Figure 2 This is a schematic diagram of the structure of a geological disaster monitoring and early warning system based on digital twin technology provided in an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0022] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] This application provides a geological disaster monitoring and early warning method based on digital twin technology. In one embodiment, please refer to... Figure 1 , Figure 1This is a flowchart illustrating a geological disaster monitoring and early warning method based on digital twin technology provided in this application. This method can be implemented using a computer program, which can be integrated into an application or run as a standalone tool application. The method can also be implemented using a microcontroller or run on a geological disaster monitoring and early warning system based on digital twin technology and the von Neumann architecture. Specifically, the method may include the following steps:

[0027] Step 101: Obtain satellite image information, meteorological information, and geological exploration information of the mountain area to be monitored within the preset sampling period.

[0028] Satellite image information refers to digital images acquired through satellite remote sensing of the mountain area to be monitored, including satellite image data in different bands such as visible light, multispectral, and radar. In the embodiments of this application, satellite image information can be understood as high-resolution visible light images, radar images, and other data of the mountain.

[0029] Meteorological information refers to various meteorological data collected from mountainous areas through meteorological monitoring equipment, including time-series data of meteorological elements such as temperature, humidity, rainfall, and wind speed. In the embodiments of this application, meteorological information can be understood as temperature, humidity, and rainfall data from meteorological stations in the mountainous area.

[0030] Geological exploration information refers to various geological data obtained through geological drilling, seismic wave measurement, electrical resistivity tomography, and other methods in mountainous areas. In the embodiments of this application, geological exploration information can be understood as core drilling data, seismic profile data, etc., of mountainous areas.

[0031] Specifically, to construct an accurate digital twin model of a mountain, it is first necessary to collect abundant multi-source heterogeneous data on the mountain as the foundation for modeling. The collection of various data types will be planned and conducted according to a preset sampling period, using methods such as satellite remote sensing, meteorological monitoring, and geological exploration to comprehensively collect data on the mountain area. Satellite remote sensing can acquire high-resolution mountain images, and by analyzing these images, the topographic contours, vegetation distribution, and building distribution of the mountain can be extracted. Meteorological monitoring can collect various meteorological data such as temperature, humidity, and rainfall to determine the meteorological distribution status of the mountain area. Geological exploration can obtain geological information such as the mountain's stratigraphic structure, lithology, and faults through drilling and testing. Data collection will continue continuously within the preset period to ensure data continuity. After obtaining all this heterogeneous data, the condition of the mountain can be comprehensively understood from different dimensions, including topographic information, underground geology, and surface meteorological information. These various data types can be cross-validated and complemented, providing reliable basic data support for the subsequent construction of an accurate digital twin model. Through data fusion, the final digital twin model can more accurately and comprehensively reflect the overall condition of the mountain.

[0032] Step 102: Based on satellite imagery, meteorological information, and geological survey information, construct a three-dimensional digital model of the mountain area to be monitored.

[0033] Among them, the three-dimensional digital model refers to a virtual model constructed using three-dimensional computer graphics methods based on the digital data of the target entity, describing the three-dimensional shape, internal structure, and characteristics of the target. In the embodiments of this application, the three-dimensional digital model can be understood as: constructing a visualized three-dimensional digital twin model of the mountain by performing three-dimensional computer modeling on satellite images, meteorological data, and geological data of the mountain area to be monitored.

[0034] Specifically, after obtaining complete satellite imagery, meteorological, and geological survey information, the next step is to integrate and apply this information to construct a three-dimensional digital model of the mountain area to be monitored. During model construction, satellite imagery can extract the mountain's topographic features for building a topographic model; meteorological information reflects the mountain's meteorological distribution for building a meteorological model; and geological survey information determines the mountain's strata and structure for building a geological model. The construction of these three sub-models follows a unified coordinate system and standards, and data alignment is performed, ultimately integrating them into a single digital twin model of the mountain encompassing information from all dimensions. This constructed digital twin model integrates detailed data from topography, meteorology, and geology, comprehensively simulating the characteristics and conditions of the mountain from different dimensions. Compared to single-source field monitoring, the digital model can better reflect the overall condition and development changes of the mountain, providing an effective technical means for subsequent simulation, prediction, and risk assessment, making geological disaster monitoring and early warning more accurate and efficient.

[0035] Based on the above embodiments, as an optional embodiment, step 102: constructing a three-dimensional digital model of the mountain area to be monitored based on satellite image information, meteorological information, and geological exploration information, may further include the following steps:

[0036] Step 201: Based on the terrain contours, ground vegetation distribution, and ground building distribution in the satellite image information, construct a terrain model of the mountain area to be monitored.

[0037] Among them, terrain contour refers to the contour line information of the land surface extracted by analyzing satellite images of mountainous areas, reflecting the three-dimensional terrain of the mountainous area. In the embodiments of this application, terrain contour can be understood as contour line data such as mountain peak lines and mountain waist lines obtained through image processing.

[0038] Ground vegetation distribution: refers to the distribution information of various types of vegetation within a mountainous area obtained through the analysis of satellite images. In the embodiments of this application, ground vegetation distribution can be understood as data on the types and coverage densities of vegetation generated for the mountainous area.

[0039] Ground building distribution: refers to the distribution information of buildings within a mountainous area obtained through the analysis of satellite images. In this embodiment, ground building distribution can be understood as the location and category information of buildings within the mountainous area.

[0040] Terrain model: refers to a three-dimensional virtual model describing the surface topography of a target area, constructed using three-dimensional computer graphics methods based on digitized terrain data. In the embodiments of this application, the terrain model can be understood as: a high-precision three-dimensional terrain model of a mountain area is constructed using computer algorithms based on the terrain outline, vegetation distribution, and building distribution data of the mountain area extracted from satellite images, and employing three-dimensional model construction technology.

[0041] Specifically, the terrain contours extracted from satellite images can depict the facial features of a mountain; vegetation distribution information can represent the vegetation cover on the mountain surface; and building distribution information can display man-made structures within the mountain area. These pieces of information, combined, can be used to construct a high-precision mountain terrain model using 3D computer graphics methods. This constructed terrain model can visually display the three-dimensional surface morphology of the mountain, such as the orientation of peaks and valleys, the distribution of vegetation cover, and the presence of roads and buildings. The terrain model is an important component of the digital twin model, and together with meteorological and geological models, it can simulate the characteristics of mountains from multiple dimensions. Compared to single on-site monitoring methods, digital models can more comprehensively reflect the surface features and morphological changes of mountain areas.

[0042] Step 202: Based on the temperature distribution, humidity distribution and rainfall distribution in the meteorological information, construct a meteorological model for the mountain area to be monitored.

[0043] Temperature distribution refers to the integrated temperature field information of air temperature data at different locations within the mountain area. In this embodiment, temperature distribution can be understood as three-dimensional temperature field data of the mountain area obtained through meteorological station monitoring.

[0044] Humidity distribution: refers to the humidity field information integrating relative humidity data at different locations within a mountainous area. In this embodiment, humidity distribution can be understood as three-dimensional humidity field data of the mountainous area obtained through meteorological station monitoring.

[0045] Rainfall distribution: refers to the integrated rainfall field information of rainfall data at different locations within a mountainous area. In this embodiment, rainfall distribution can be understood as three-dimensional rainfall field data of the mountainous area obtained through meteorological radar.

[0046] Meteorological model: refers to a three-dimensional virtual model describing the meteorological characteristics of a target area, constructed using three-dimensional computer simulation methods based on digitized meteorological data. In the embodiments of this application, the meteorological model can be understood as: constructing a three-dimensional meteorological field model of a mountain area using computer algorithms and based on temperature, humidity, and rainfall data collected by meteorological stations.

[0047] Specifically, constructing an accurate digital twin model of a mountain also requires considering the meteorological characteristics of the mountain region. Therefore, a meteorological model is needed to simulate the meteorological environment of the mountain. This is because temperature, humidity, and rainfall in the mountain region have a significant impact on its condition, necessitating the simulation of these meteorological characteristics. Specifically, based on data monitored by meteorological stations, three-dimensional computer simulation technology can be used to construct a three-dimensional distribution view of the temperature, humidity, and rainfall fields of the mountain region. This constructed meteorological model can visually display the temperature gradient distribution, spatial differences in air humidity, and the amount of rainfall at different locations within the mountain region. The meteorological model is a crucial component of the digital twin model, simulating the meteorological environment of the mountain region and providing an additional dimension of information beyond topographic and geological models. Compared to single meteorological station monitoring, the meteorological model can achieve three-dimensional visualization of meteorological elements through simulation calculations, reflecting the meteorological characteristics of the mountain more comprehensively and accurately, and providing data support for subsequent mountain condition simulation and risk assessment.

[0048] Step 203: Based on the stratigraphic structure, lithological distribution, and fault location in the geological exploration information, construct a geological structure model of the mountain area to be monitored.

[0049] The term "stratigraphic structure" refers to the comprehensive data obtained through geological drilling and testing, including the thickness, dip angle, and reservoir characteristics of various strata within the mountain. In this embodiment, the stratigraphic structure can be understood as the measurement results of the lithology, dip direction, and thickness of the strata in the mountain area.

[0050] Lithological distribution: refers to data on the various rock types and their spatial distribution within a mountainous area, obtained through sampling and testing. In this embodiment, lithological distribution can be understood as data on the lithological categories and distribution patterns of a mountainous area obtained through core drilling.

[0051] Fault location: refers to the location and direction of movement of faults within a mountainous area, obtained through geological surveys and geophysical exploration. In this embodiment, fault location can be understood as the spatial location data of mountain faults obtained using seismic profile exploration.

[0052] Geological structure model: refers to a three-dimensional virtual model describing the strata and structure of a target area, constructed using three-dimensional computer simulation methods based on digitized geological data. In the embodiments of this application, the geological structure model can be understood as: a three-dimensional geological structure model of a mountain area constructed using computer algorithms and three-dimensional modeling technology, based on strata and fault data of the mountain area obtained by geological drilling.

[0053] Specifically, constructing an accurate digital twin model of a mountain requires considering its internal geological structure. Therefore, a geological structure model is needed to simulate the mountain's strata and structure. This is because the strata and fault structures of a mountain are crucial factors affecting its stability, necessitating the simulation of these geological features. Specifically, based on strata and fault data obtained through drilling and geological testing, 3D modeling techniques can be used to construct the geometric morphology of the strata within the mountain and the spatial location information of various faults. This constructed geological structure model can visually display the internal strata structure and fault distribution of the mountain, such as the dip angle and thickness variations of the strata, and the strike and dip angle of faults. The geological model is a vital component of the digital twin model, simulating the internal geological features of the mountain. Together with the topographic and meteorological models, it constitutes the digital twin model of the mountain. The geological structure model can comprehensively reflect the strata and faults of the mountain through 3D visualization, which is crucial for assessing the impact of geological structures on mountain stability.

[0054] Step 204: Integrate the terrain model, meteorological model, and geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored.

[0055] Specifically, after constructing the terrain model, meteorological model, and geological structure model, these three sub-models need to be fused to ultimately generate a 3D digital twin model of the mountain region. The fusion of these sub-models aims to organically combine mountain data from different dimensions within a unified digital framework, enabling the integrated utilization of multi-source heterogeneous data on the mountain. This makes the digital twin model a virtual sample of the mountain containing comprehensive information. The fusion of the three sub-models requires ensuring the consistency of the model coordinate system, aligning and calibrating the spatial data to guarantee that the three sub-models are positioned and scaled consistently within the same coordinate framework. Then, using model fusion algorithms from 3D computer graphics, the three sub-models are integrated to generate a mesh-optimized 3D digital twin model of the mountain. This constructed digital twin model integrates the geomorphic features of the terrain model, the climate distribution of the meteorological model, and the stratigraphic structure of the geological model, becoming a complete digital simulation system containing multi-source, multi-dimensional information about the mountain. Compared to single-sensor monitoring, the digital twin model achieves effective data fusion, allowing for observation of the mountain's state from multiple angles, making monitoring more accurate.

[0056] Based on the above embodiments, as an optional embodiment, in step 204: fusing the terrain model, meteorological model, and geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored, this step may further include the following steps:

[0057] Step 214: Establish the reference spatial coordinate system for the area to be monitored.

[0058] The reference spatial coordinate system refers to the standard coordinate system used to define the spatial location of the target area when collecting and processing spatial information. In this embodiment, the reference spatial coordinate system can be understood as a three-dimensional coordinate system of the mountain established based on control points set up in the mountain area and their spatial coordinates determined by satellite positioning.

[0059] Specifically, in constructing a digital twin model of a mountain, it is necessary to establish a reference spatial coordinate system for the mountain area as the basis for locating and registering various types of data. Establishing a reference coordinate system is crucial because subsequent multi-source data needs to be located and fused within a unified coordinate system to ensure spatial correspondence between different data types. Specifically, stable control points within the mountain area can be selected, and their spatial coordinates can be determined using satellite positioning technology. A three-dimensional coordinate system for the mountain area can then be established based on these control points. This established reference coordinate system needs to cover the entire mountain area, with control points strategically placed to ensure the accuracy and stability of the coordinate system. During data acquisition, the location information within this coordinate system will be recorded. For example, topographic and geological data can be calibrated using the reference coordinate system. Constructing a reference coordinate system provides a spatial basis for the alignment and fusion of multi-source data. Analyzing various data within the same coordinate system ensures spatial matching and avoids errors caused by different coordinate references.

[0060] Step 224: Map the terrain model, meteorological model, and geological structure model to the reference spatial coordinate system, and align the spatial position and scale of the terrain model, meteorological model, and geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored.

[0061] Spatial location refers to the specific orientation of an object in a spatial coordinate system, represented by the coordinates of the object's feature points. In the embodiments of this application, spatial location can be understood as the three-dimensional coordinate values ​​of feature points in the sub-model in the reference coordinate system.

[0062] Spatial scale: refers to the size and range of an object reflected in a spatial coordinate system. In the embodiments of this application, spatial scale can be understood as the length, area, and volume of the mountain region reflected by the sub-model in the reference coordinate system.

[0063] Specifically, after establishing a baseline coordinate system, each sub-model needs to be registered within this system to achieve spatial consistency and address spatial positional discrepancies caused by different acquisition methods and data formats. Specifically, feature points in each sub-model can be extracted, and based on their actual positions in the baseline coordinate system, a geometric transformation algorithm is used to convert the sub-model's coordinates to the baseline coordinate system. Through coordinate transformation and feature point alignment, the position and scale of the sub-models are adjusted, ensuring that the three sub-models have consistent orientation and scale within the same coordinate system. In this way, the terrain model, meteorological model, and geological model are constructed under a unified spatial coordinate system, ensuring accurate spatial correspondence between the sub-models. Targeted data processing for different sub-models ensures the accuracy of their respective data while achieving spatial fusion, avoiding the negative impact of positional discrepancies between data on model accuracy. Registering each sub-model under a unified coordinate system provides a spatial foundation for subsequent model integration, enabling the digital twin model to accurately reflect the spatial relationships between various features of the mountain.

[0064] Step 103: Receive the current mountain data, geological data, and weather conditions of the mountain area to be monitored. Combine the mountain data and geological data to determine multiple risk sources and their corresponding locations in the three-dimensional digital model of the mountain area to be monitored under the weather conditions.

[0065] Risk sources refer to various influencing factors that may lead to mountain instability and disasters under specific environmental conditions. In this application embodiment, risk sources can be understood as locations in mountain areas where safety hazards exist, such as cracks and deposits, discovered by comparing digital twin models and real-time monitoring data.

[0066] Specifically, after constructing a three-dimensional digital twin model of the mountain, it is necessary to receive real-time measured data of the mountain and simulate and analyze its risk status on the digital model. Receiving measured data is to obtain the current actual condition of the mountain, compare it with the digital model, and achieve dynamic assessment of mountain risk. Specifically, mountain feature information such as deformation data and fissure data can be obtained from monitoring equipment, along with weather data such as rainfall and temperature in the area. Integrating this data with the digital model identifies unstable areas in the model and determines potential risk sources and their locations. This allows for a direct view of the mountain's real-time status on the digital model, revealing deviations between the digital model and measured data, i.e., areas with risk. The system can automatically identify these risk sources and pinpoint their spatial locations. Compared to single-source field monitoring, the digital model integrates multi-source data, enabling visualized monitoring and early warning of mountain risks. By comparing the model with real-time data, mountain stability can be dynamically assessed, ensuring timely and accurate identification of risk sources. This provides crucial support for subsequent early warning decisions and improves the effectiveness of mountain disaster monitoring.

[0067] Based on the above embodiments, as an optional embodiment, in step 103: combining mountain data and geological data, determining multiple risk sources and their corresponding locations in the three-dimensional digital model for the mountain area to be monitored under weather conditions, this step may further include the following steps:

[0068] Step 301: In the three-dimensional digital model, based on the mountain data, identify multiple displacement anomalies and the first risk index corresponding to each displacement anomaly, and based on the geological data, identify multiple stress anomalies and the second risk index corresponding to each stress anomaly.

[0069] Anomaly points refer to locations within a mountain area where the observed displacement values ​​differ significantly from those under normal conditions. In this embodiment, anomaly points can be understood as locations where abnormal deformation displacement values ​​on the mountain surface are detected by comparing the digital twin model of the mountain with real-time monitoring data, such as near cracks and in plastic zones. The identified anomaly points reflect abnormal deformation states of the mountain at those locations.

[0070] The first risk index refers to the risk level obtained by quantitatively assessing the external deformation risk at the location of the displacement anomaly. In the embodiments of this application, the first risk index can be understood as the deformation risk index of the displacement anomaly calculated by analyzing information such as the displacement value, displacement rate, and displacement direction of the displacement anomaly using a risk assessment model. The first risk index quantifies the risk of the displacement anomaly.

[0071] Stress anomaly point: refers to the location in the stress distribution inside the mountain where the stress value deviates significantly from the normal state. In the embodiments of this application, stress anomaly point can be understood as an abnormal location with excessive stress concentration found by finite element numerical simulation calculation of the stress field inside the mountain, such as a steep stratum bend. The identified stress anomaly point directly reflects the potential for hidden dangers in the stability of the mountain.

[0072] The second risk index refers to the risk level obtained by quantitatively assessing the internal stability risk of the location of the stress anomaly point. In the embodiments of this application, the second risk index can be understood as the stability risk index of the stress anomaly point calculated by analyzing information such as the stress magnitude and stress multiple of the stress anomaly point using a risk assessment model. The second risk index quantifies the risk of the stress anomaly point.

[0073] Specifically, to achieve accurate risk assessment of mountain structures, multi-faceted anomaly identification and risk analysis are required on a digital twin model. Identifying displacement and stress anomalies is crucial because mountain deformation and internal stress are two key indicators reflecting mountain stability. Specifically, by comparing real-time monitored mountain deformation data with the deformation under normal conditions in the digital model, machine learning algorithms can be used to identify displacement points and their risk levels associated with deformation anomalies. Simultaneously, the finite element method can be used to simulate the stress distribution within the mountain, identifying anomalies with excessive stress concentration and their risk levels. This allows for risk identification from both internal and external perspectives: displacement anomalies characterize external deformation risks, while stress anomalies reflect internal stability risks. Compared to single deformation monitoring, digital models enable multi-dimensional assessments of the overall stability of the mountain.

[0074] Based on the above embodiments, as an optional embodiment, in step 301: identifying multiple displacement anomaly points and a first risk index corresponding to each displacement anomaly point based on mountain data, and identifying multiple stress anomaly points and a second risk index corresponding to each stress anomaly point based on geological data, this step may further include the following steps:

[0075] Step 311: Determine the surface displacement of each monitoring point in the mountain data and the stress value of each monitoring point in the geological data.

[0076] Among them, the surface displacement refers to the magnitude of the displacement that occurs at a certain location on the mountain surface within a certain time interval. In the embodiments of this application, the surface displacement can be understood as the displacement value of a designated monitoring point on the mountain surface collected by the displacement monitoring equipment between two monitoring times. The surface displacement reflects the deformation of the mountain and is used to determine the stability of the mountain surface.

[0077] Stress value: refers to the magnitude of rock stress acting on a certain location inside a mountain. In the embodiments of this application, stress value can be understood as the rock stress value at a specific monitoring point inside the mountain collected by buried stress monitoring sensors. The stress value reflects the mechanical state inside the mountain and is used to determine the stability inside the mountain.

[0078] Specifically, to achieve real-time monitoring of mountain stability, it is necessary to acquire real-time deformation and stress data. Determining the displacement and stress at monitoring points is crucial because they are two key parameters for assessing mountain stability. Monitoring equipment deployed at key locations along the mountain can collect real-time data on surface displacement and internal stress at the covered monitoring points. This allows for dynamic monitoring of changes both inside and outside the mountain, including deformation and internal mechanical responses. Compared to static early warning models, real-time monitoring data reflects the mountain's real-time state, improving the accuracy of early warnings. Displacement and stress data at monitoring points are fundamental to mountain stability analysis; real-time data acquisition allows for the identification of abnormal changes and timely warnings of potential risks.

[0079] Step 321: For each monitoring point, the monitoring points with surface displacement greater than the displacement threshold are designated as displacement anomaly points, and the first risk index of the displacement anomaly point is determined based on the difference between the surface displacement and the displacement threshold.

[0080] Specifically, to achieve real-time risk assessment of the mountain's condition, it is necessary to dynamically identify displacement anomalies and determine their risk indices based on displacement monitoring. Setting a displacement threshold for anomaly identification is crucial because the magnitude of displacement directly reflects the severity of deformation risk. Specifically, a displacement threshold within the normal deformation range can be pre-determined based on historical statistical analysis. When the real-time displacement of a monitoring point exceeds this threshold, it is identified as a displacement anomaly. Based on the difference exceeding the threshold, a deformation risk index, i.e., the first risk index, is calculated using a risk assessment model. This allows for rapid identification of locations on the mountain's surface with deformation anomaly risks and provides a quantitative result of the anomaly's severity. Compared to simple displacement monitoring, this method enables risk analysis of monitoring data, intuitively reflects risk distribution, and improves decision-making efficiency.

[0081] Step 331: The monitoring points with stress values ​​greater than the stress value threshold are designated as stress anomaly points, and the second risk index of the stress anomaly points is determined based on the difference between the stress value and the stress value threshold.

[0082] Specifically, to achieve real-time risk assessment of the mountain's condition, it is necessary to dynamically identify stress anomalies and determine their risk indices based on stress monitoring. Setting stress value thresholds to identify anomalies is crucial because the magnitude of stress values ​​directly reflects the severity of internal stability risks. Specifically, a stress value threshold within the normal stress range can be pre-determined based on historical statistical analysis. When the real-time stress value at a monitoring point exceeds this threshold, it is identified as a stress anomaly. Based on the difference exceeding the threshold, an internal stability risk index, or second risk index, is calculated using a risk assessment model. This allows for rapid identification of locations within the mountain where stability anomalies exist and provides a quantitative assessment of the degree of anomaly. Compared to simple stress monitoring, this method enables risk analysis of monitoring data, intuitively reflects risk distribution, and improves decision-making efficiency. Stress anomaly detection is key to real-time mountain stability assessment, and the determination of the second risk index provides a basis for risk assessment.

[0083] Step 302: Based on the preset weather condition mapping table, determine the influence coefficient corresponding to the weather condition; combine the influence coefficient and each first risk index to determine the target risk index of each displacement anomaly point, and combine the influence coefficient and each second risk index to determine the target risk index of each stress anomaly point.

[0084] The preset weather condition mapping table refers to a pre-established correlation model between different weather conditions and the degree of impact on mountain risks. In this embodiment, the preset weather condition mapping table can be understood as determining the correspondence between different rainfall, temperature, and other weather conditions and the degree of aggravation of mountain risks based on historical statistical analysis.

[0085] Impact coefficient: This refers to a quantitative parameter that measures the degree of impact of different weather conditions on mountain risks. In this embodiment, the impact coefficient can be understood as determining the degree of increase or decrease in mountain risk by searching for a matching risk impact correlation model in a preset weather condition mapping table based on real-time observed weather parameters.

[0086] Target risk index: refers to the real-time risk level of the mountain after taking into account the impact of current weather conditions. In the embodiments of this application, the target risk index can be understood as a new risk index value that reflects the current actual risk level, obtained by adjusting the first risk index and the second risk index through the influence coefficient.

[0087] Specifically, to make accurate mountain risk warnings, it is necessary to consider the impact of weather conditions and obtain a target risk index that reflects the risk under the current environment. The influence coefficient is determined because different weather conditions can exacerbate or reduce the degree of mountain risk. Specifically, a mapping table of the correlation between different weather conditions and risk impact can be established in advance, and the corresponding influence coefficient can be determined based on real-time weather parameters. This coefficient is then combined with a first risk index and a second risk index, and a risk correction model is used to calculate the target risk index that takes into account environmental impact. This allows for the assessment of the actual risk level of the same anomaly point under different weather conditions, enabling dynamic risk correction. Compared to a single static risk model, the target risk index fully considers the impact of environmental factors, can better determine the current actual risk, and improve the accuracy of decision-making.

[0088] Step 303: For each stress anomaly point and each displacement anomaly point, the stress anomaly point or displacement anomaly point with a target risk index greater than the risk index threshold is taken as the risk source, and the location of the risk source is recorded.

[0089] Specifically, to achieve precise location of mountain risks, it is necessary to determine the actual risk sources based on a target risk index and set thresholds to filter risk sources. This is because the target risk index reflects the current actual risk level and can be used to judge the severity of the risk. Specifically, a risk index threshold can be pre-set. When the target risk index of an anomaly point exceeds this value, the point is confirmed as an actual risk source, and its location information is recorded. This allows for the identification of the truly risky location among numerous anomalies, enabling risk segmentation and localization. Compared to directly using the raw risk index, the target risk index combines information from various aspects, allowing for a more accurate determination of the actual risk source and a clearer understanding of its spatial distribution. This helps monitoring personnel develop targeted monitoring and early warning plans.

[0090] Step 104: For each risk source, when the rate of change of any monitoring indicator in the mountain data or geological data of the risk source exceeds the rate of change threshold, a disaster warning is issued based on the location of the risk source.

[0091] Among them, monitoring indicators refer to key parameters used to assess the stability of a mountain. In the embodiments of this application, monitoring indicators can be understood as data such as displacement and strain obtained from mountain deformation monitoring, and data such as stress and pore water pressure obtained from internal mechanical monitoring of the mountain. Monitoring indicators reflect the key factors affecting mountain stability and are used to judge the changing trend of mountain stability.

[0092] Specifically, to achieve effective disaster early warning for identified risk sources, it is necessary to monitor changes in risk source data and issue a location-based early warning when the changes exceed a threshold. Monitoring the rate of change and setting a threshold is crucial because the trend of risk source data changes directly reflects the development status of the risk at that location. Specifically, multiple parameters such as mountain deformation and stress can be monitored at the identified risk source location, and the rate of change for each data point can be calculated. When the rate of change exceeds a preset threshold, it is determined that the disaster risk of that risk source has entered a rapid development stage. At this point, a targeted early warning needs to be issued immediately for that risk source location. This allows for real-time monitoring of the risk source's development dynamics and timely and effective intervention. Rate of change monitoring enables dynamic risk management, improves early warning efficiency, and real-time monitoring and early warning are key links in proactive disaster prevention, enabling controllable risks and reducing losses from disasters.

[0093] Based on the above embodiments, as an optional embodiment, in step 104: when the rate of change of any monitoring indicator in the mountain data or geological data of the risk source exceeds the rate of change threshold, a disaster warning is issued based on the location of the risk source. Before this step, the following steps may also be included:

[0094] Step 401: Obtain multiple sampled values ​​of each monitoring indicator in the mountain data and geological data within a preset time window.

[0095] The preset time window refers to the time span for collecting training samples. In this embodiment, the preset time window can be understood as a predetermined period of time for collecting mountain monitoring data for model training.

[0096] Sampling value: refers to the mountain monitoring data collected within a time window. In the embodiments of this application, the sampling value can be understood as the data obtained by collecting the displacement, stress, etc. of the mountain monitoring point within a time window according to a preset time interval.

[0097] Specifically, to achieve the forecasting and early warning of mountain risks, it is necessary to collect sample data for model training and obtain multiple sample values ​​within a time window. This is because data over a certain time span can reflect the changing patterns of the mountain's condition. A time window can be pre-set, and multiple monitoring samples such as displacement and stress at various monitoring points on the mountain can be collected periodically within this period. This yields a time-series sample dataset containing the characteristics of mountain changes. Compared to single static samples, time-window sampling can provide monitoring data under different time states, more comprehensively reflecting the changing trends of the mountain. By obtaining training samples that conform to the patterns through time-window sampling, a model with the ability to learn and predict the changing patterns of the mountain's condition can be built, enabling the forecasting and early warning of mountain disaster risks.

[0098] Step 402: For each monitoring indicator, generate the change curve corresponding to each sample value, and use the slope of the change curve as the rate of change of the monitoring indicator.

[0099] The change curve refers to the trend line of the monitoring indicator changing over time. In the embodiments of this application, the change curve can be understood as a curve reflecting the changing trend of the monitoring indicator, drawn in chronological order using multiple sampled values ​​within a time window.

[0100] Specifically, to assess the changing trends of the mountain's condition, the collected sample data needs to be processed to obtain the rate of change of the monitoring indicators. Generating a change curve and calculating its slope is crucial because the change curve reflects the trend of the monitoring data over time, and its slope directly indicates the rate of change. Multiple sample values ​​within a time window can be used to plot the change curves of each monitoring indicator in chronological order. Then, the slope of the change curve is calculated through curve fitting, serving as the rate of change for that monitoring indicator. This allows for the quantitative acquisition of the rate of change of various mountain state parameters. The rate of change represents the dynamic process of mountain change and is an effective indicator for assessing mountain stability.

[0101] Based on the above embodiments, as an optional embodiment, step 104: disaster early warning based on the location of the risk source may further include the following steps:

[0102] Step 403: Based on the location of the risk source, generate early warning information and send the early warning information to the staff terminal corresponding to the location.

[0103] Among them, early warning information refers to risk alerts used to notify disaster prevention departments to take corresponding measures. In the embodiments of this application, early warning information can be understood as warning information generated for identified risk sources, containing key information such as risk level and possible impact. By clarifying the relevant circumstances of the risk source, early warning information is used to remind relevant disaster prevention personnel to take targeted preventive measures.

[0104] Specifically, to achieve targeted early warning for identified risk sources, it is necessary to generate and send appropriate early warning information. Generating early warning information based on the location of the risk source is crucial because different regions require different preventative measures. Based on pre-set correspondences, personnel responsible for a given area can be identified according to the spatial location of the risk source. Then, early warning information, including the risk level and potential impact range, is generated for the potential disaster situation of that risk source and pushed to the terminals of the corresponding personnel. This allows for regional control of risk sources and the implementation of targeted preventative measures. Compared to broadcast early warnings, location-based early warning transmission improves work efficiency. Accurate and timely transmission of early warning information is a prerequisite for implementing disaster prevention measures, and regionalized early warning enhances the precision of risk management.

[0105] Reference Figure 2 This application provides a geological disaster monitoring and early warning system based on digital twin technology. The system includes: an information acquisition module, a digital model determination module, a risk source identification module, and a disaster early warning module, wherein:

[0106] The information acquisition module is used to acquire satellite image information, meteorological information, and geological exploration information of the mountain area to be monitored within a preset sampling period;

[0107] The digital model determination module is used to construct a three-dimensional digital model of the mountain area to be monitored based on satellite imagery, meteorological information, and geological exploration information.

[0108] The risk source identification module is used to receive the current mountain data, geological data and weather conditions of the mountain area to be monitored, and combine the mountain data and geological data to determine multiple risk sources and their corresponding locations in the three-dimensional digital model of the mountain area to be monitored under the weather conditions.

[0109] The disaster early warning module is used to issue disaster warnings based on the location of each risk source when the rate of change of any monitoring indicator in the mountain data or geological data of the risk source exceeds the rate of change threshold.

[0110] Based on the above embodiments, the digital model determination module is also used to construct a topographic model of the mountain area to be monitored based on the topographic contour, ground vegetation distribution, and ground building distribution in satellite image information; to construct a meteorological model of the mountain area to be monitored based on the temperature distribution, humidity distribution, and rainfall distribution in meteorological information; to construct a geological structure model of the mountain area to be monitored based on the stratigraphic structure, lithological distribution, and fault location in geological exploration information; and to fuse the topographic model, meteorological model, and geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored.

[0111] Based on the above embodiments, the digital model determination module is also used to establish a reference spatial coordinate system for the area to be monitored; map the terrain model, meteorological model and geological structure model to the reference spatial coordinate system, and align the spatial position and scale of the terrain model, meteorological model and geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored.

[0112] Based on the above embodiments, the risk source identification module is also used to identify multiple displacement anomalies and corresponding first risk indices in the three-dimensional digital model according to mountain data, and to identify multiple stress anomalies and corresponding second risk indices according to geological data; to determine the influence coefficients corresponding to weather conditions based on a preset weather condition mapping table; to determine the target risk index of each displacement anomaly by combining the influence coefficients and each first risk index, and to determine the target risk index of each stress anomaly by combining the influence coefficients and each second risk index; for each stress anomaly and each displacement anomaly, stress anomalies or displacement anomalies with target risk indices greater than the risk index threshold are identified as risk sources, and the location of the risk sources is recorded.

[0113] Based on the above embodiments, the risk source identification module is also used to determine the surface displacement of each monitoring point in the mountain data and the stress value of each monitoring point in the geological data; for each monitoring point, the monitoring point with the surface displacement greater than the displacement threshold is regarded as the displacement anomaly point, and the first risk index of the displacement anomaly point is determined according to the difference between the surface displacement and the displacement threshold; the monitoring point with the stress value greater than the stress value threshold is regarded as the stress anomaly point, and the second risk index of the stress anomaly point is determined according to the difference between the stress value and the stress value threshold.

[0114] Based on the above embodiments, the disaster early warning module is also used to acquire multiple sampled values ​​of each monitoring indicator in the mountain data and geological data within a preset time window; for each monitoring indicator, a change curve corresponding to each sampled value is generated, and the slope of the change curve is used as the change rate of the monitoring indicator.

[0115] Based on the above embodiments, the disaster early warning module is also used to generate early warning information based on the location of the risk source and send the early warning information to the staff terminal corresponding to the location.

[0116] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0117] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0118] The communication bus 302 is used to enable communication between these components.

[0119] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0120] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0121] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0122] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a geological disaster monitoring and early warning method based on digital twin technology.

[0123] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a geological disaster monitoring and early warning method based on digital twin technology. When executed by one or more processors 301, the electronic device 300 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0124] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0125] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0128] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0129] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will readily conceive of those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0130] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only.

Claims

1. A geological disaster monitoring and early warning method based on digital twin technology, characterized in that, include: Acquire satellite imagery, meteorological information, and geological survey information of the mountain area to be monitored within a preset sampling period; Based on the satellite image information, meteorological information, and geological exploration information, a three-dimensional digital model of the mountain area to be monitored is constructed. Receive current mountain data, geological data, and weather conditions of the mountain area to be monitored; combine the mountain data and the geological data to determine multiple risk sources of the mountain area to be monitored under the weather conditions and the corresponding locations of each risk source in the three-dimensional digital model. For each of the aforementioned risk sources, when the rate of change of any monitoring indicator in the mountain data or geological data of the risk source exceeds the rate of change threshold, a disaster warning is issued based on the location of the risk source. The construction of a three-dimensional digital model of the mountain area to be monitored based on the satellite image information, meteorological information, and geological exploration information includes: Based on the terrain contours, ground vegetation distribution, and ground building distribution in the satellite image information, a terrain model of the mountain area to be monitored is constructed. Based on the temperature distribution, humidity distribution and rainfall distribution in the meteorological information, a meteorological model of the mountain area to be monitored is constructed. Based on the stratigraphic structure, lithological distribution, and fault location in the geological exploration information, a geological structure model of the mountain area to be monitored is constructed. The terrain model, the meteorological model, and the geological structure model are fused to obtain a three-dimensional digital model of the mountain area to be monitored. The process of fusing the terrain model, the meteorological model, and the geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored includes: Establish a reference spatial coordinate system for the mountain area to be monitored; The terrain model, the meteorological model, and the geological structure model are mapped to the reference spatial coordinate system, and the spatial positions and scales of the terrain model, the meteorological model, and the geological structure model are aligned to obtain a three-dimensional digital model of the mountain area to be monitored. The process of receiving current mountain data, geological data, and weather conditions for the mountain area to be monitored, and combining the mountain data and geological data to determine multiple risk sources and their corresponding locations in the three-dimensional digital model under the given weather conditions, includes: In the three-dimensional digital model, based on the mountain data, multiple displacement anomaly points and a first risk index corresponding to each displacement anomaly point are identified, and based on the geological data, multiple stress anomaly points and a second risk index corresponding to each stress anomaly point are identified. Based on a preset weather condition mapping table, the influence coefficient corresponding to the weather condition is determined; By combining the influence coefficient and each of the first risk indices, the target risk index of each of the displacement anomaly points is determined, and by combining the influence coefficient and each of the second risk indices, the target risk index of each of the stress anomaly points is determined. For each stress anomaly point and each displacement anomaly point, the stress anomaly point or displacement anomaly point with the target risk index greater than the risk index threshold is taken as the risk source, and the location of the risk source is recorded.

2. The geological disaster monitoring and early warning method based on digital twin technology according to claim 1, characterized in that, The step of identifying multiple displacement anomalies and a first risk index corresponding to each displacement anomaly based on the mountain data, and identifying multiple stress anomalies and a second risk index corresponding to each stress anomaly based on the geological data, includes: Determine the surface displacement of each monitoring point in the mountain data and the stress value of each monitoring point in the geological data; For each of the monitoring points, the monitoring points where the surface displacement is greater than the displacement threshold are designated as displacement anomaly points, and the first risk index of the displacement anomaly point is determined based on the difference between the surface displacement and the displacement threshold. Monitoring points where the stress value is greater than the stress value threshold are designated as stress anomaly points, and a second risk index for the stress anomaly points is determined based on the difference between the stress value and the stress value threshold.

3. The geological disaster monitoring and early warning method based on digital twin technology according to claim 1, characterized in that, Before issuing a disaster warning based on the location of the risk source, when the rate of change of any monitoring indicator in the mountain data or geological data of the risk source exceeds the rate of change threshold, the method further includes: Acquire multiple sampled values ​​of each monitoring indicator in the mountain data and geological data within a preset time window; For each of the monitoring indicators, a change curve corresponding to each of the sampled values ​​is generated, and the slope of the change curve is taken as the change rate of the monitoring indicator.

4. The geological disaster monitoring and early warning method based on digital twin technology according to claim 1, characterized in that, The disaster early warning based on the location of the risk source includes: Based on the location of the risk source, an early warning message is generated and sent to the staff terminal corresponding to the location.

5. A geological disaster monitoring and early warning system based on digital twin technology, characterized in that, The system includes: The information acquisition module is used to acquire satellite image information, meteorological information, and geological exploration information of the mountain area to be monitored within a preset sampling period; The digital model determination module is used to construct a three-dimensional digital model of the mountain area to be monitored based on the satellite image information, meteorological information, and geological exploration information. The risk source identification module is used to receive the current mountain data, geological data and weather conditions of the mountain area to be monitored, and combine the mountain data and the geological data to determine multiple risk sources of the mountain area to be monitored under the weather conditions and the corresponding locations of each risk source in the three-dimensional digital model. The disaster early warning module is used to issue a disaster early warning based on the location of each of the aforementioned risk sources when the rate of change of any monitoring indicator in the mountain data or geological data of the risk source exceeds the rate of change threshold. The construction of a three-dimensional digital model of the mountain area to be monitored based on the satellite image information, meteorological information, and geological exploration information includes: Based on the terrain contours, ground vegetation distribution, and ground building distribution in the satellite image information, a terrain model of the mountain area to be monitored is constructed. Based on the temperature distribution, humidity distribution and rainfall distribution in the meteorological information, a meteorological model of the mountain area to be monitored is constructed. Based on the stratigraphic structure, lithological distribution, and fault location in the geological exploration information, a geological structure model of the mountain area to be monitored is constructed. The terrain model, the meteorological model, and the geological structure model are fused to obtain a three-dimensional digital model of the mountain area to be monitored. The process of fusing the terrain model, the meteorological model, and the geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored includes: Establish a reference spatial coordinate system for the mountain area to be monitored; The terrain model, the meteorological model, and the geological structure model are mapped to the reference spatial coordinate system, and the spatial positions and scales of the terrain model, the meteorological model, and the geological structure model are aligned to obtain a three-dimensional digital model of the mountain area to be monitored. The process of receiving current mountain data, geological data, and weather conditions for the mountain area to be monitored, and combining the mountain data and geological data to determine multiple risk sources and their corresponding locations in the three-dimensional digital model under the given weather conditions, includes: In the three-dimensional digital model, based on the mountain data, multiple displacement anomaly points and a first risk index corresponding to each displacement anomaly point are identified, and based on the geological data, multiple stress anomaly points and a second risk index corresponding to each stress anomaly point are identified. Based on a preset weather condition mapping table, the influence coefficient corresponding to the weather condition is determined; By combining the influence coefficient and each of the first risk indices, the target risk index of each of the displacement anomaly points is determined, and by combining the influence coefficient and each of the second risk indices, the target risk index of each of the stress anomaly points is determined. For each stress anomaly point and each displacement anomaly point, the stress anomaly point or displacement anomaly point with the target risk index greater than the risk index threshold is taken as the risk source, and the location of the risk source is recorded.

6. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform the geological disaster monitoring and early warning method based on digital twin technology as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the geological disaster monitoring and early warning method based on digital twin technology as described in any one of claims 1-4.

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