Geological disaster monitoring and early warning method and system based on digital twinborn technology

Abstract

The invention discloses a geological disaster monitoring and early warning method and system based on a digital twinborn technology, and relates to the technical field of data monitoring. The method comprises the following steps: acquiring satellite image information, meteorological information and geological detection information of a mountain area to be monitored in a preset sampling period; based on the satellite image information, the meteorological information and the geological detection information, constructing a three-dimensional digital model of the mountain area to be monitored; receiving current mountain data, geological data and weather conditions of the mountain area to be monitored, and determining a plurality of risk sources of the mountain area to be monitored under the weather conditions and positions corresponding to the risk sources in the three-dimensional digital model in combination with the mountain data and the geological data; and for each risk source, when the change rate of any monitoring index in the mountain data or geological data of the risk source exceeds a change rate threshold value, performing disaster early warning based on the position of the risk source. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of geological disaster monitoring and early warning is achieved.

Description

Technical Field

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

[0002] With the frequent occurrence of geological disasters, such as landslides and mud-rock flows, the safety of people's lives and property has become a huge threat. Therefore, an effective geological disaster monitoring and early warning system is essential to identify potential risks in advance and take preventive measures. The purpose of such monitoring systems is to reduce the losses caused by disasters and ensure public safety.

[0003] At present, the existing geological disaster monitoring and early warning methods rely on the monitoring data collected by ground monitoring stations to analyze and issue early warnings for the monitored mountain areas. However, in actual applications, due to the variability of the geological environment, data monitoring only through ground monitoring stations and single-dimensional monitoring often have errors with the actual geological changes in the monitored mountain areas, resulting in low accuracy in geological disaster monitoring and early warning. Summary of the invention

[0004] The present application provides a geological disaster monitoring and early warning method, system, electronic device and storage medium based on digital twin technology, which has the effect of improving the accuracy of geological disaster monitoring and early warning.

[0005] In the first aspect, the present application provides a geological disaster monitoring and early warning method based on digital twin technology, including: Obtain satellite image information, meteorological information and geological exploration 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, construct a three-dimensional digital model of the mountain area to be monitored; Receiving current mountain data, geological data and weather conditions of the mountain area to be monitored, and determining, in the three-dimensional digital model, a plurality of risk sources of the mountain area to be monitored under the weather conditions and positions corresponding to the risk sources, in combination with the mountain data and the geological data; For each of the risk sources, when the change rate of any monitoring indicator in the mountain data or geological data of the risk source exceeds a change rate threshold, a disaster warning is issued based on the location of the risk source.

[0006] In a second aspect of the present application, a geological disaster monitoring and early warning system based on digital twin technology is provided, the system comprising: An information acquisition module is used to obtain satellite image information, meteorological information and geological exploration information of the mountain area to be monitored within a preset sampling period; A digital model determination module, 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 detection information; A 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 determine, in the three-dimensional digital model, a plurality of risk sources of the mountain area to be monitored under the weather conditions and the corresponding positions of the risk sources in combination with the mountain data and the geological data; The disaster warning module is used to issue a disaster warning based on the location of each risk source when the change rate of any monitoring indicator in the mountain data or geological data of the risk source exceeds a change rate threshold.

[0007] In the third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a geological disaster monitoring and early warning method based on digital twin technology when loaded and executed by the processor.

[0008] In the fourth aspect of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor implements a geological disaster monitoring and early warning method based on digital twin technology.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solution, satellite images, meteorological information and geological exploration data of multiple dimensions of the mountain area to be monitored are obtained. By integrating multi-dimensional data, the mountain morphology and geological structure under the influence of weather conditions can be judged more comprehensively and accurately, and the location of the risk source can be determined more accurately. The dynamic early warning for the risk source is realized by real-time monitoring of the change rate of mountain data and geological data, and the corresponding positioning information is released, which realizes the overall targeted intelligent early warning of mountain disasters. Through this technical solution, combined with digital twin technology, that is, combining multi-dimensional data to generate a three-dimensional digital model of the mountain area to be monitored, the advantages of multi-source data are utilized to make the mountain condition assessment more accurate and the risk identification more accurate and dynamic, thereby improving the accuracy of geological disaster monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of a geological disaster monitoring and early warning method based on digital twin technology provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a geological disaster monitoring and early warning system based on digital twin technology provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] This application embodiment provides a geological disaster monitoring and early warning method based on digital twin technology. In one embodiment, please refer to Figure 1 , Figure 1 This is a flow chart of a geological disaster monitoring and early warning method based on digital twin technology provided in an embodiment of the present application. The method can be implemented by a computer program, which can be integrated in an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on a geological disaster monitoring and early warning system based on digital twin technology based on the von Neumann system. Specifically, the method can include the following steps: Step 101: Obtain satellite image information, meteorological information, and geological exploration information of the mountain area to be monitored within a preset sampling period.

[0016] Satellite image information refers to digital images obtained by satellite remote sensing of the mountain area to be monitored, including satellite image data of different bands such as visible light, multi-spectral, radar, etc. In the embodiment of the present application, satellite image information can be understood as high-resolution mountain visible light images, radar images and other data.

[0017] Meteorological information: refers to various meteorological data of the mountain area collected by meteorological monitoring equipment, including time series data of meteorological elements such as temperature, humidity, rainfall, wind speed, etc. In the embodiment of the present application, meteorological information can be understood as temperature, humidity and rainfall data of the meteorological station in the mountain area.

[0018] Geological exploration information: refers to various geological data information obtained through geological drilling, seismic wave measurement, electrical detection, etc. in the mountain area. In the embodiment of the present application, geological exploration information can be understood as core drilling data, seismic profile data, etc. in the mountain area.

[0019] Specifically, in order to build an accurate digital twin model of a mountain, it is necessary to first collect rich multi-source heterogeneous data of the mountain as the basis for modeling, plan the collection of various types of data, and conduct comprehensive data collection on the mountain area through satellite remote sensing, meteorological monitoring, and geological exploration according to the preset sampling period. Satellite remote sensing can obtain high-resolution mountain image information, and the terrain contour, vegetation distribution, and building distribution of the mountain can be extracted by analyzing the image. 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 stratigraphic structure, lithology, and faults of the mountain through drilling and testing. The collection of various types of data will continue within the preset period to ensure the continuity of the data. After obtaining all these heterogeneous data, the condition of the mountain can be fully understood from different dimensions, including information on the terrain, underground geology, and surface meteorology. Various types of data can verify and complement each other, 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.

[0020] Step 102: construct a three-dimensional digital model of the mountain area to be monitored based on satellite image information, meteorological information and geological exploration information.

[0021] Among them, the three-dimensional digital model refers to the use of three-dimensional computer graphics methods to construct a virtual model that describes the three-dimensional shape, internal structure and characteristics of the target based on the digital data of the target entity. In the embodiment of the present application, the three-dimensional digital model can be understood as: by performing three-dimensional computer modeling on satellite images, meteorological data and geological data of the monitored mountain area, a visualized three-dimensional digital twin model of the mountain is constructed.

[0022] Specifically, after obtaining complete satellite image information, meteorological information and geological exploration 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. In the process of model construction, satellite image information can extract the terrain characteristics of the mountain and use it to construct a terrain model of the mountain area; meteorological information can reflect the meteorological distribution of the mountain and use it to build a meteorological model; geological exploration information can determine the strata and structure of the mountain and use it to build a geological model. The construction of the three sub-models follows a unified coordinate system and specifications, and the data is aligned, and finally integrated into a digital twin model of the mountain that includes information in various dimensions. The digital twin model constructed in this way integrates detailed data from topography, meteorology, geology and other aspects, and fully simulates the characteristics and conditions of the mountain from different dimensions. Compared with a single field monitoring, the digital model can better reflect the overall condition and development changes of the mountain, and provide an effective technical means for subsequent simulation prediction and risk assessment, making geological disaster monitoring and early warning more accurate and efficient.

[0023] Based on the above embodiment, as an optional embodiment, in 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, this step may also include the following steps: Step 201: construct a terrain model of the mountain area to be monitored based on the terrain contour, ground vegetation distribution and ground building distribution in the satellite image information.

[0024] The terrain contour refers to the contour line information of the surface terrain extracted by analyzing the satellite image of the mountain area, reflecting the three-dimensional terrain of the mountain area. In the embodiment of the present application, the terrain contour can be understood as the contour line data such as the peak line and the mountainside line obtained by image processing.

[0025] Ground vegetation distribution: refers to the distribution information of various types of vegetation in the mountain area obtained by analyzing satellite images. In the embodiment of the present application, ground vegetation distribution can be understood as the data of vegetation types and coverage density generated for the mountain area.

[0026] Ground building distribution: refers to the distribution information of buildings in the mountain area obtained by analyzing satellite images. In the embodiment of the present application, ground building distribution can be understood as the location and category information of buildings in the mountain area.

[0027] Terrain model: refers to a three-dimensional virtual model describing the surface terrain of the target area constructed based on digitized terrain data using three-dimensional computer graphics methods. In the embodiment of the present application, the terrain model can be understood as: a high-precision three-dimensional terrain model of the mountain is constructed using a computer algorithm based on the terrain contours, vegetation distribution and building distribution data of the mountain area extracted from satellite images, and a three-dimensional model construction technology.

[0028] Specifically, the terrain contours of the mountain area extracted from satellite images can depict the facial morphology of the mountain; vegetation distribution information can indicate the vegetation coverage on the surface of the mountain; and building distribution information can display artificial buildings in the mountain area. These information can be combined to construct a high-precision mountain terrain model using three-dimensional computer graphics methods. The terrain model constructed in this way can intuitively display the three-dimensional surface morphology of the mountain, such as the direction of peaks and canyons, the distribution of vegetation coverage, and the conditions of roads and buildings. The terrain model is an important part of the digital twin model. Together with the meteorological model and the geological model, it can simulate the characteristics of the mountain from multiple dimensions. Compared with a single on-site monitoring method, the digital model can more comprehensively reflect the surface characteristics and morphological changes of the mountain area.

[0029] Step 202: construct a meteorological model of the mountain area to be monitored based on the temperature distribution, humidity distribution and rainfall distribution in the meteorological information.

[0030] Wherein, temperature distribution refers to the temperature field information integrated from the temperature data at different locations in the mountain area. In the embodiment of the present application, temperature distribution can be understood as the three-dimensional temperature field data of the mountain area obtained through monitoring by a meteorological station.

[0031] Humidity distribution: refers to the humidity field information integrated from the relative humidity data at different locations in the mountain area. In the embodiment of the present application, humidity distribution can be understood as the three-dimensional humidity field data of the mountain area obtained through monitoring by a meteorological station.

[0032] Rainfall distribution: refers to rainfall field information integrated from rainfall data at different locations in a mountain area. In the embodiment of the present application, rainfall distribution can be understood as three-dimensional rainfall field data in a mountain area obtained by a weather radar.

[0033] Meteorological model: refers to the use of a three-dimensional computer simulation method to construct a three-dimensional virtual model describing the meteorological characteristics of the target area based on digital meteorological data. In the embodiment of the present application, the meteorological model can be understood as: using a computer algorithm, based on the temperature, humidity and rainfall data of the mountain area collected by the meteorological station, using three-dimensional simulation technology to construct a three-dimensional meteorological field model of the mountain area.

[0034] Specifically, the construction of an accurate mountain digital twin model also needs to consider the meteorological characteristics of the mountain area, so it is necessary to build a meteorological model to simulate the meteorological environment of the mountain. The meteorological model is built because the temperature, humidity and rainfall in the mountain area have an important impact on the mountain state, and the meteorological characteristics need to be simulated. Specifically, according to the data monitored by the meteorological station, three-dimensional computer simulation technology can be used to construct a three-dimensional distribution view of the temperature field, humidity field and rainfall field in the mountain area. The meteorological model constructed in this way can intuitively display the temperature gradient distribution in the mountain area, the spatial difference of air humidity, and the rainfall size at different locations. The meteorological model is an important part of the digital twin model. It can simulate the meteorological environment of the mountain area and is another dimensional information besides the terrain model and geological model. Compared with the monitoring of a single meteorological station, the meteorological model can realize the three-dimensional visualization of meteorological elements through simulation calculation, more comprehensively and accurately reflect the meteorological characteristics of the mountain, and provide data support for subsequent mountain state simulation and risk assessment.

[0035] Step 203: construct a geological structure model of the mountain area to be monitored based on the stratigraphic structure, lithology distribution and fault location in the geological exploration information.

[0036] The stratigraphic structure refers to the comprehensive data of thickness, dip, reservoir characteristics, etc. of each stratum inside the mountain obtained through geological drilling and testing. In the embodiment of the present application, the stratigraphic structure can be understood as the measurement results of the lithology, dip, and thickness of the mountain area.

[0037] Lithology distribution: refers to the data of various rock types and their spatial distribution in the mountain area obtained through sampling and testing. In the embodiment of the present application, lithology distribution can be understood as the data of lithology categories and their distribution patterns in the mountain area obtained by core drilling.

[0038] Fault location: refers to the data on the location and running direction of faults in a mountain area obtained through geological surveys and geophysical detection. In the embodiment of the present application, the fault location can be understood as the spatial location data of mountain faults obtained by seismic profile detection.

[0039] Geological structure model: refers to the use of three-dimensional computer simulation methods to construct a three-dimensional virtual model describing the strata and structure of the target area based on digital geological data. In the embodiment of the present application, the geological structure model can be understood as: using computer algorithms, based on the strata and fault data of the mountain area obtained by geological drilling, and using three-dimensional modeling technology to construct a three-dimensional geological structure model of the mountain area.

[0040] Specifically, to build an accurate digital twin model of a mountain, it is also necessary to consider the geological structural characteristics inside the mountain. Therefore, it is necessary to build a geological structural model to simulate the strata and structure of the mountain. The geological structural model is built because the strata and fault structures of the mountain are important factors affecting its stability, and it is necessary to simulate the geological characteristics. Specifically, the geometric shape of the strata inside the mountain and the spatial position information of various faults can be constructed using three-dimensional modeling technology based on the strata and fault data obtained from drilling and geological testing. The geological structural model constructed in this way can intuitively display the strata structure and fault distribution inside the mountain, such as the dip angle, thickness change of the strata, the direction and dip angle of the faults, etc. The geological model is an important part of the digital twin model. It can simulate the geological characteristics inside the mountain and form a digital twin model of the mountain together with the terrain model and the meteorological model. The geological structural model can fully reflect the strata and faults of the mountain through three-dimensional visualization, which is very important for evaluating the impact of geological structure on the stability of the mountain.

[0041] Step 204: The terrain model, the meteorological model and the geological structure model are integrated to obtain a three-dimensional digital model of the mountain area to be monitored.

[0042] Specifically, after constructing the terrain model, meteorological model and geological structure model, the three sub-models need to be fused to finally generate a three-dimensional digital twin model of the mountain area. The fusion of sub-models is to organically combine mountain data of different dimensions in a unified digital framework, realize the fusion and utilization of multi-source heterogeneous data of the mountain, and make the digital twin model a virtual sample of the mountain containing all-round information. The fusion of the three sub-models needs to ensure the consistency of the model coordinate system, align and calibrate the spatial data, and ensure that the positions and scales of the three sub-models in the same coordinate framework are consistent. Then, the model fusion algorithm of three-dimensional computer graphics is used to realize the integration of the three sub-models and generate a grid-optimized three-dimensional digital twin model of the mountain. The digital twin model constructed in this way 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 and multi-dimensional information of the mountain. Compared with single sensor monitoring, the digital twin model realizes the effective fusion of data, can observe the state of the mountain from multiple angles, and make the monitoring more accurate.

[0043] Based on the above embodiment, as an optional embodiment, in step 204: the terrain model, the meteorological model and the geological structure model are integrated to obtain a three-dimensional digital model of the mountain area to be monitored. This step may also include the following steps: Step 214: Establish a reference space coordinate system for the area to be monitored.

[0044] The reference spatial coordinate system refers to a standard coordinate system used to define the spatial position of a target area when collecting and processing spatial information. In the embodiment of the present application, the reference spatial coordinate system can be understood as a three-dimensional mountain coordinate system established based on control points arranged in a mountain area and their spatial coordinates determined by satellite positioning.

[0045] Specifically, in the process of building 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 positioning and registering various types of data. The reference coordinate system is established because the subsequent multi-source data needs to be positioned and fused in a unified coordinate system to ensure the spatial correspondence between various types of data. Specifically, stable control points in the mountain area can be selected, and their spatial coordinates can be determined using satellite positioning technology. The three-dimensional coordinate system of the mountain area can be established based on these control points. The reference coordinate system established in this way needs to cover the entire mountain area, and the control points should be reasonably arranged to ensure the accuracy and stability of the coordinate system. The positioning information of various types of data in this coordinate system will be recorded during the collection process. For example, the collection of terrain data and geological data can be calibrated with the reference coordinate system. By constructing the reference coordinate system, a spatial basis can be provided for the alignment and fusion of multi-source data. Analyzing various types of data in the same coordinate system can ensure their spatial matching relationship and avoid errors caused by different coordinate references.

[0046] Step 224: Map the terrain model, meteorological model and geological structure model to the reference space coordinate system, and align the spatial positions and scales of the terrain model, meteorological model and geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored.

[0047] Wherein, spatial position refers to the specific position of an object in a spatial coordinate system, which is represented by the coordinates of the feature points of the object. In the embodiment of the present application, spatial position can be understood as the three-dimensional coordinate value of the feature points in the sub-model in the reference coordinate system.

[0048] Spatial scale: refers to the size and range of an object in a spatial coordinate system. In the embodiment of the present application, the spatial scale can be understood as the length, area and volume of the mountain area reflected by the sub-model in the reference coordinate system.

[0049] Specifically, after the reference coordinate system is established, each sub-model needs to be registered in the coordinate system to achieve spatial consistency of the model, in order to solve the problem of spatial position deviation between sub-models due to different acquisition methods and data formats. Specifically, the feature points in the sub-model can be extracted separately, and the coordinates of the sub-model can be converted to the reference coordinate system using a geometric transformation algorithm according to the actual position of the feature points in the reference coordinate system. The position and scale of the sub-model are adjusted by coordinate transformation and feature point alignment, so that the three sub-models have consistent orientation and scale in the same coordinate system. In this way, the terrain model, meteorological model and geological model are constructed under a unified spatial coordinate system, ensuring the precise spatial correspondence between the sub-models. The targeted data processing adopted for different sub-models not only ensures the accuracy of each data, but also realizes the spatial fusion of the sub-models, avoiding the negative impact of the position deviation between the data on the model accuracy. By registering each sub-model in a unified coordinate system, a spatial basis is provided for subsequent model integration, so that the digital twin model can truly reflect the spatial correlation between the various features of the mountain.

[0050] Step 103: Receive current mountain data, geological data and weather conditions of the mountain area to be monitored, combine the mountain data and geological data, and determine multiple risk sources and corresponding positions of each risk source in the mountain area to be monitored under weather conditions in the three-dimensional digital model.

[0051] Among them, risk sources refer to various factors that may cause mountain instability and disasters under specific environmental conditions. In the embodiment of the present application, risk sources can be understood as locations with safety hazards in mountain areas, such as cracks, accumulations, etc., discovered by comparing the digital twin model and real-time monitoring data.

[0052] Specifically, after the three-dimensional digital twin model of the mountain is built, it is necessary to receive the measured data of the mountain in real time and simulate and analyze the risk status of the mountain on the digital model. The purpose of receiving the measured data is to obtain the current actual condition of the mountain, compare it with the digital model, and realize the dynamic assessment of the mountain risk. Specifically, the deformation data, crack data and other mountain feature information of the mountain area can be obtained from the monitoring equipment, and the rainfall, temperature and other weather data of the area can be obtained. These data are integrated with the digital model to identify the unstable areas in the model and determine the potential risk sources and their locations. In this way, the real-time status of the mountain can be viewed intuitively on the digital model, and the deviation between the digital model and the measured data, that is, the risky areas, can be found. The system can automatically identify these risk sources and locate their spatial positions. Compared with single field monitoring, the digital model integrates multi-source data, which can realize the visual monitoring and early warning of mountain risks. By comparing the model with real-time data, the stability of the mountain can be dynamically evaluated to ensure the timely and accurate identification of risk sources, which provides important support for subsequent early warning decisions and improves the efficiency of mountain disaster monitoring.

[0053] Based on the above embodiment, as an optional embodiment, in step 103: combining the mountain data and the geological data, determining multiple risk sources and the corresponding positions of the risk sources in the mountain area to be monitored under weather conditions in the three-dimensional digital model, this step may also include the following steps: Step 301: In the three-dimensional digital model, a plurality of displacement anomaly points and a first risk index corresponding to each displacement anomaly point are identified based on mountain data, and a plurality of stress anomaly points and a second risk index corresponding to each stress anomaly point are identified based on geological data.

[0054] Among them, the abnormal displacement point refers to the location where the displacement value actually observed in the mountain area is significantly different from the displacement value under normal conditions. In the embodiment of the present application, the abnormal displacement point can be understood as the location where the abnormal deformation displacement value of the mountain surface is found by comparing the mountain digital twin model with the real-time monitoring data, such as near the cracks and the plastic zone. The identified abnormal displacement point reflects the abnormal deformation state of the mountain at that location.

[0055] The first risk index refers to the risk level obtained by quantitatively evaluating the external deformation risk of the location of the displacement abnormal point. In the embodiment of the present application, the first risk index can be understood as the deformation risk index of the displacement abnormal point calculated by using the risk assessment model by analyzing the displacement value, displacement rate, displacement direction and other information of the displacement abnormal point. The first risk index quantifies the risk of the displacement abnormal point.

[0056] Stress anomaly point: refers to the position where the stress value deviates significantly from the normal state in the stress distribution inside the mountain. In the embodiment of the present application, the stress anomaly point can be understood as the abnormal position where the stress concentration is too large, which is found through finite element numerical simulation calculation of the internal stress field of the mountain, such as the turning point of steep strata. The identified stress anomaly point directly reflects the hidden dangers of the internal stability of the mountain.

[0057] The second risk index refers to the risk level obtained by quantitatively evaluating the internal stability risk of the location of the stress anomaly point. In the embodiment of the present application, the second risk index can be understood as the stability risk index of the stress anomaly point calculated by using the risk assessment model by analyzing the stress magnitude, stress multiple and other information of the stress anomaly point. The second risk index quantifies the risk of the stress anomaly point.

[0058] Specifically, in order to achieve an accurate assessment of mountain risks, it is necessary to perform multi-faceted anomaly identification and risk analysis on the digital twin model. Identification of displacement anomalies and stress anomalies is because mountain deformation and internal stress are two key indicators reflecting mountain stability. Specifically, by comparing the real-time monitored mountain deformation data with the deformation conditions under normal conditions in the digital model, the machine learning algorithm can be used to identify the displacement points of abnormal deformation and their risk levels. At the same time, the finite element method can be used to simulate the stress distribution inside the mountain, identify abnormal points with excessive stress concentration and their risk levels, so that risks can be identified from both inside and outside the mountain. Displacement anomalies characterize external deformation risks, and stress anomalies reflect internal stability risks. Compared with single deformation monitoring, digital models can achieve multi-angle assessment of the overall stability of the mountain.

[0059] Based on the above embodiment, as an optional embodiment, in step 301: according to the mountain data, a plurality of abnormal displacement points and a first risk index corresponding to each abnormal displacement point are identified, and according to the geological data, a plurality of abnormal stress points and a second risk index corresponding to each abnormal stress point are identified. This step may also include the following steps: 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.

[0060] Among them, the surface displacement refers to the displacement of a certain position on the surface of the mountain within a certain time interval. In the embodiment of the present application, the surface displacement can be understood as the displacement value of a designated monitoring point on the surface of the mountain between two monitoring times collected by the displacement monitoring equipment. The surface displacement reflects the deformation of the mountain and is used to judge the stability of the surface of the mountain.

[0061] Stress value: refers to the magnitude of rock stress acting at a certain position inside the mountain. In the embodiment of the present application, the stress value can be understood as the rock stress value at a specific monitoring point inside the mountain collected by the buried stress monitoring sensor. The stress value reflects the mechanical state inside the mountain and is used to judge the internal stability of the mountain.

[0062] Specifically, in order to achieve real-time monitoring of the stability of the mountain, it is necessary to obtain the real-time deformation and stress data of the mountain. The displacement and stress of the monitoring point are determined because they are the two key parameters for evaluating the stability of the mountain. The surface displacement data of the mountain and the internal stress monitoring data of the mountain at the covered monitoring points can be collected in real time through monitoring equipment deployed at key positions of the mountain. In this way, the changes inside and outside the mountain can be dynamically obtained, including mountain deformation and internal mechanical response. Compared with the static early warning model, the real-time monitoring data can reflect the real-time status of the mountain and help improve the accuracy of the early warning. The displacement and stress data of the monitoring point are the basis of the mountain stability analysis. Through the real-time collection of data, abnormal changes in the mountain can be identified and possible risks can be warned in time.

[0063] Step 321: For each monitoring point, the monitoring point whose surface displacement is greater than the displacement threshold is regarded as a displacement anomaly point, and a first risk index of the displacement anomaly point is determined according to the difference between the surface displacement and the displacement threshold.

[0064] Specifically, in order to realize the risk assessment of the real-time status of the mountain, it is necessary to dynamically identify the abnormal displacement points and determine their risk index based on displacement monitoring. The displacement threshold is set to identify abnormal points because the size of the displacement can directly reflect the severity of the deformation risk. Specifically, the displacement threshold of the normal deformation range can be determined in advance based on historical statistical analysis. When the real-time displacement of the monitoring point exceeds the threshold, it is confirmed as a displacement abnormal point. And according to the difference exceeding the threshold, the deformation risk index, that is, the first risk index, is calculated and determined by the risk assessment model. In this way, it is possible to quickly determine which locations on the surface of the mountain have abnormal deformation risks, and obtain quantitative results of the degree of abnormality. Compared with simple displacement monitoring, this method realizes risk analysis of monitoring data, can intuitively reflect risk distribution, and improve decision-making efficiency.

[0065] Step 331: taking a monitoring point whose stress value is greater than a stress value threshold as a stress abnormal point, and determining a second risk index of the stress abnormal point according to a difference between the stress value and the stress value threshold.

[0066] Specifically, in order to realize the risk assessment of the real-time status of the mountain, it is necessary to dynamically identify stress anomaly points and determine their risk index based on stress monitoring. The reason for setting the stress value threshold to identify the anomaly point is that the size of the stress value can directly reflect the severity of the internal stability risk. Specifically, the stress value threshold of the normal stress range can be determined in advance based on historical statistical analysis. When the real-time stress value of the monitoring point exceeds the threshold, it is confirmed as a stress anomaly point. And according to the difference between the threshold and the internal stability risk index, i.e., the second risk index, is calculated by the risk assessment model. In this way, it is possible to quickly determine which locations inside the mountain have stability anomaly risks, and obtain quantitative results of the degree of anomaly. Compared with simple stress monitoring, this method realizes the risk analysis of monitoring data, can intuitively reflect the risk distribution, and improve decision-making efficiency. Stress anomaly point detection is the key to the real-time stability assessment of the mountain, and the determination of the second risk index provides a basis for risk judgment.

[0067] Step 302: Based on a 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.

[0068] The preset weather condition mapping table refers to a pre-established correlation model between different weather conditions and the degree of mountain risk impact. In the embodiment of the present application, the preset weather condition mapping table can be understood as determining the correspondence between different weather conditions such as rainfall and temperature and the degree of mountain risk aggravation based on historical statistical analysis.

[0069] Impact coefficient: refers to a quantitative parameter of the degree of impact of different weather conditions on mountain risk. In the embodiment of the present application, the impact coefficient can be understood as searching for a matching risk impact correlation model in a preset weather condition mapping table based on the real-time observed weather parameters to determine the corresponding degree of aggravation or reduction of mountain risk.

[0070] Target risk index: refers to the real-time risk level of the mountain after considering the influence of current weather conditions. In the embodiment of the present application, the target risk index can be understood as a new risk index value reflecting the current actual risk level obtained by adjusting the first risk index and the second risk index by the influence coefficient.

[0071] Specifically, in order to make an accurate mountain risk warning, it is necessary to consider the impact of weather conditions and obtain a target risk index that reflects the risk in the current environment. The impact coefficient is determined because different weather conditions will aggravate or weaken the risk level of the mountain. Specifically, a mapping table of the correlation between different weather conditions and risk impacts can be established in advance, and the current corresponding impact coefficient can be determined based on real-time weather parameters. Then, it is combined with the first risk index and the second risk index, and the risk correction model is used to calculate the target risk index that takes into account environmental impact. In this way, the actual risk level of the same anomaly under different weather conditions can be evaluated, and dynamic correction of risks can be achieved. Compared with a single static risk model, the target risk index fully considers the impact of environmental factors, which can better determine the current actual risk and improve the accuracy of decision-making.

[0072] Step 303: For each stress abnormal point and each displacement abnormal point, the stress abnormal point or displacement abnormal point whose target risk index is greater than the risk index threshold is taken as a risk source, and the position of the risk source is recorded.

[0073] Specifically, in order to accurately locate the risk of the mountain, it is necessary to determine the actual risk source based on the target risk index and set a threshold to screen the risk source because the target risk index reflects the current actual risk level and can be used to judge the severity of the risk. Specifically, a threshold for the risk index can be set in advance. When the target risk index of a certain abnormal point is higher than this value, the point is confirmed as the actual risk source and its location information is recorded. In this way, the location where the risk actually exists in the current situation can be identified among many abnormal points, and the risk can be segmented and located. Compared with the direct use of the original risk index, the target risk index combines various aspects of information, can more accurately determine the actual risk source, clarify the spatial distribution of the risk source, and can help monitoring personnel formulate targeted monitoring and early warning plans.

[0074] Step 104: For each risk source, when the change rate of any monitoring indicator in the mountain data or geological data of the risk source exceeds the change rate threshold, a disaster warning is issued based on the location of the risk source.

[0075] Among them, monitoring indicators refer to key parameters used to evaluate the stability of the mountain. In the embodiment of the present application, monitoring indicators can be understood as displacement, strain and other data obtained by monitoring the deformation of the mountain, as well as stress, pore water pressure and other data obtained by monitoring the internal mechanics of the mountain. The monitoring indicators reflect the key factors affecting the stability of the mountain and are used to judge the changing trend of the stability of the mountain.

[0076] Specifically, in order to achieve effective disaster warning for the determined risk source, it is necessary to monitor the changes in the risk source data and conduct positioning warning when the changes exceed the threshold. The reason for monitoring the change rate and setting the threshold is that the change trend of the risk source data directly reflects the development status of the risk at that location. Specifically, the determined risk source location can continue to be monitored for multiple parameters such as mountain deformation and stress, and the change rate of each data can be calculated. When the change rate exceeds the preset threshold, it is determined that the disaster risk of the risk source has entered a rapid development stage. At this time, it is necessary to immediately conduct targeted warnings for the risk source location. In this way, the development dynamics of the risk source can be grasped in real time, and timely and effective intervention in risk warnings can be carried out. The change rate monitoring realizes the dynamic management of risks and improves the warning efficiency. Real-time monitoring and warning are the key links in active disaster prevention, which can control risks and reduce losses caused by disasters.

[0077] Based on the above embodiment, as an optional embodiment, in step 104: when the change rate of any monitoring indicator in the mountain data or geological data of the risk source exceeds the change rate threshold, a disaster warning is performed based on the location of the risk source. Before this step, the following steps may also be included: Step 401: Acquire multiple sampling values ​​of each monitoring indicator in mountain data and geological data within a preset time window.

[0078] The preset time window refers to the data time span used to collect training samples. In the embodiment of the present application, the preset time window can be understood as a predetermined period of time for collecting mountain monitoring data for model training.

[0079] Sampling value: refers to the mountain monitoring data collected within the time window. In the embodiment of the present application, the sampling value can be understood as the data obtained by collecting the displacement, stress, etc. of the mountain monitoring point within the time window at a preset time interval.

[0080] Specifically, in order to realize the forecast and early warning of mountain risks, it is necessary to collect sample data for modeling training and obtain multiple sampling values ​​within the time window, because the data of a certain time span can reflect the changing law of mountain state. A time window can be set in advance, and multiple monitoring samples such as displacement and stress of each monitoring point of the mountain can be collected regularly during this time period. In this way, a time series sample data set containing the characteristics of mountain changes is obtained. Compared with a single static sample, time window sampling can provide monitoring data under different time states, more comprehensively reflect the trend of mountain changes, and obtain regular training samples through time window sampling. It is possible to build a model with learning and prediction capabilities for the law of mountain state changes, and realize the forecast and early warning of mountain disaster risks.

[0081] Step 402: For each monitoring indicator, a change curve corresponding to each sampling value is generated, and the slope of the change curve is used as the change rate of the monitoring indicator.

[0082] The change curve refers to the trend line of the monitoring indicator changing over time. In the embodiment of the present application, the change curve can be understood as a curve reflecting the change trend of the monitoring indicator drawn by using multiple sampling values ​​in the time window in the order of their collection time.

[0083] Specifically, in order to evaluate the changing trend of the mountain state, it is necessary to process the collected sample data to obtain the rate of change of the monitoring indicators. The reason for generating a change curve and calculating the slope is that the change curve can reflect the changing trend of the monitoring data over time, and its slope directly indicates the speed of the change. Multiple sampling values ​​within the time window can be used to draw the change curve of each monitoring indicator in chronological order. Then, the slope value of the change curve is calculated by curve fitting as the rate of change of the monitoring indicator. In this way, the rate of change of each state parameter of the mountain can be quantitatively obtained. The rate of change can represent the dynamic process of mountain change and is an effective indicator for judging the stability of the mountain.

[0084] Based on the above embodiment, as an optional embodiment, in step 104: performing disaster warning based on the location of the risk source, this step may also include the following steps: Step 403: Generate warning information based on the location of the risk source, and send the warning information to the staff end corresponding to the location.

[0085] Among them, early warning information refers to risk warnings used to notify disaster prevention departments to take corresponding measures. In the embodiment of the present application, early warning information can be understood as warning information generated for a determined risk source, including key contents such as risk level and possible impact. The early warning information is used to remind the corresponding disaster prevention personnel to take targeted preventive measures by clarifying the relevant situation of the risk source.

[0086] Specifically, in order to achieve targeted early warning for the determined risk sources, it is necessary to generate and send appropriate early warning information. Early warning information is generated based on the location of the risk source because different preventive measures need to be taken for risk sources in different regions. According to the pre-set correspondence, the staff responsible for the area can be determined according to the spatial location of the risk source. Then, for the possible disaster situation of the risk source, early warning information containing risk level, possible impact range, etc. is generated and pushed to the terminal of the corresponding staff, so that the risk source can be controlled by region and targeted preventive measures can be implemented. Compared with broadcast early warning, the positioning and sending of early warning information can improve work efficiency. Accurate and timely transmission of early warning information is the prerequisite for implementing disaster prevention measures. Regionalized early warning can improve the refinement of risk management.

[0087] Reference Figure 2 , a geological disaster monitoring and early warning system based on digital twin technology provided in an embodiment of the present application, the system includes: an information acquisition module, a digital model determination module, a risk source identification module, and a disaster early warning module, wherein: An information acquisition module is used to obtain satellite image information, meteorological information and geological exploration information of the mountain area to be monitored within a preset sampling period; A digital model determination module is used to construct a three-dimensional digital model of the mountain area to be monitored based on 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 determine multiple risk sources and the corresponding positions of each risk source in the mountain area to be monitored under weather conditions in the three-dimensional digital model by combining the mountain data and geological data; The disaster warning module is used to issue a disaster warning based on the location of each risk source when the change rate of any monitoring indicator in the mountain data or geological data of the risk source exceeds the change rate threshold.

[0088] On the basis of the above embodiments, the digital model determination module is also used to construct a terrain model of the mountain area to be monitored based on the terrain contour, ground vegetation distribution and ground building distribution in the 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 the meteorological information; to construct a geological structure model of the mountain area to be monitored based on the stratigraphic structure, lithology distribution and fault position in the geological exploration information; and to fuse the terrain model, meteorological model and geological structure model to obtain a three-dimensional digital model of the mountain area to be monitored.

[0089] Based on the above embodiments, the digital model determination module is also used to establish a reference space coordinate system for the area to be monitored; map the terrain model, meteorological model and geological structure model to the reference space 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.

[0090] On the basis of the above embodiments, the risk source identification module is also used to identify, in a three-dimensional digital model, multiple displacement anomaly points and a first risk index corresponding to each displacement anomaly point according to mountain data, and to identify multiple stress anomaly points and a second risk index corresponding to each stress anomaly point according to geological data; determine the influence coefficient corresponding to the weather condition based on a preset weather condition mapping table; determine the target risk index of each displacement anomaly point by combining the influence coefficient and each first risk index, and determine the target risk index of each stress anomaly point by combining the influence coefficient and each second risk index; for each stress anomaly point and each displacement anomaly point, the stress anomaly point or displacement anomaly point whose target risk index is greater than the risk index threshold is taken as a risk source, and the location of the risk source is recorded.

[0091] On the basis of 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 whose surface displacement is greater than the displacement threshold is regarded as a 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 whose stress value is greater than the stress value threshold is regarded as a 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.

[0092] Based on the above embodiments, the disaster warning module is also used to obtain multiple sampling 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 sampling value is generated, and the slope of the change curve is used as the change rate of the monitoring indicator.

[0093] Based on the above embodiment, the disaster warning module is also used to generate warning information based on the location of the risk source, and send the warning information to the staff end corresponding to the location.

[0094] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0095] The present application also discloses an electronic device. Figure 3 , Figure 3 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 .

[0096] The communication bus 302 is used to realize the connection and communication between these components.

[0097] 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.

[0098] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0099] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0100] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a geological disaster monitoring and early warning method based on digital twin technology.

[0101] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the memory 305 to store an application program 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 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0107] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0108] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A geological disaster monitoring and early warning method based on digital twin technology, characterized in that: include: Obtain satellite image information, meteorological information and geological exploration 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, construct a three-dimensional digital model of the mountain area to be monitored; Receiving current mountain data, geological data and weather conditions of the mountain area to be monitored, and determining, in the three-dimensional digital model, a plurality of risk sources of the mountain area to be monitored under the weather conditions and positions corresponding to the risk sources, in combination with the mountain data and the geological data; For each of the risk sources, when the change rate of any monitoring indicator in the mountain data or geological data of the risk source exceeds a change rate threshold, a disaster warning is issued based on the location of the risk source.

2. The geological disaster monitoring and early warning method based on digital twin technology according to claim 1 is characterized in that: The step of constructing 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: Constructing a terrain model of the mountain area to be monitored based on the terrain contour, ground vegetation distribution and ground building distribution in the satellite image information; Based on the temperature distribution, humidity distribution and rainfall distribution in the meteorological information, construct a meteorological model of the mountain area to be monitored; Based on the stratigraphic structure, lithology 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 integrated to obtain a three-dimensional digital model of the mountain area to be monitored.

3. The geological disaster monitoring and early warning method based on digital twin technology according to claim 2 is characterized in that: The fusion of the terrain model, the meteorological model and the geological structure model to obtain the three-dimensional digital model of the mountain area to be monitored includes: Establishing a reference space coordinate system for the area to be monitored; The terrain model, the meteorological model and the geological structure model are mapped into the reference space 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.

4. The geological disaster monitoring and early warning method based on digital twin technology according to claim 1 is characterized in that: The receiving of current mountain data, geological data and weather conditions of the mountain area to be monitored, combining the mountain data and the geological data, and determining in the three-dimensional digital model a plurality of risk sources of the mountain area to be monitored under the weather conditions and positions corresponding to the risk sources, comprises: In the three-dimensional digital model, a plurality of abnormal displacement points and a first risk index corresponding to each abnormal displacement point are identified according to the mountain data, and a plurality of abnormal stress points and a second risk index corresponding to each abnormal stress point are identified according to the geological data; Determine the influence coefficient corresponding to the weather condition based on a preset weather condition mapping table; Determine the target risk index of each of the displacement abnormal points by combining the influence coefficient and each of the first risk indexes, and determine the target risk index of each of the stress abnormal points by combining the influence coefficient and each of the second risk indexes; For each of the stress abnormal points and each of the displacement abnormal points, the stress abnormal point or the displacement abnormal point whose target risk index is greater than the risk index threshold is taken as a risk source, and the position of the risk source is recorded.

5. The geological disaster monitoring and early warning method based on digital twin technology according to claim 4 is characterized in that: The step of identifying a plurality of abnormal displacement points and a first risk index corresponding to each abnormal displacement point according to the mountain data, and identifying a plurality of abnormal stress points and a second risk index corresponding to each abnormal stress point according to 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 point whose surface displacement is greater than the displacement threshold is taken as a displacement abnormal point, and a first risk index of the displacement abnormal point is determined according to the difference between the surface displacement and the displacement threshold; A monitoring point whose stress value is greater than a stress value threshold is taken as a stress abnormal point, and a second risk index of the stress abnormal point is determined according to a difference between the stress value and the stress value threshold.

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

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

8. A geological disaster monitoring and early warning system based on digital twin technology, characterized in that: The system comprises: An information acquisition module is used to obtain satellite image information, meteorological information and geological exploration information of the mountain area to be monitored within a preset sampling period; A digital model determination module, 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 detection information; A 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 determine multiple risk sources of the mountain area to be monitored under the weather conditions and the corresponding positions of each risk source in the three-dimensional digital model in combination with the mountain data and the geological data; The disaster warning module is used to issue a disaster warning based on the location of each risk source when the change rate of any monitoring indicator in the mountain data or geological data of the risk source exceeds a change rate threshold.

9. An electronic device, characterized in that: It 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 so that the electronic device executes the geological disaster monitoring and early warning method based on digital twin technology as described in any one of claims 1-7.

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

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