1822 results about "Landslide" patented technology

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention discloses a wide-area landslide rapid identification method based on an interpretable intelligent algorithm, and relates to the field of remote sensing science and technology, and the method comprises the steps: building a dual-channel feature extraction architecture through multi-source spatio-temporal data fusion and knowledge graph dynamic weighting: capturing image local textures through a lightweight CNN, modeling geological spatial correlation through a graph convolutional network, and carrying out the recognition of the landslide. Combining the SHAP value and causal reasoning to generate an interpretable contribution degree thermodynamic diagram and a rule chain; a knowledge graph bidirectional verification system is introduced, spatial logic contradictions are verified by using prior rules, and co-evolution of a model and a rule base is triggered based on misjudgment samples; outputting a multi-dimensional credibility report, quantifying uncertainty by Monte Carlo Dropout, and customizing interpretation granularity according to roles; a terrain-adaptive block-stream processing architecture is adopted, and edge lightweight deployment and federated learning are combined, so that wide-area real-time early warning and model dynamic updating are realized. According to the scheme, the limitation of a traditional black box model is broken through, and a disaster prevention closed loop with physical driving, transparent decision and second-level response is formed.

The invention discloses a slope deformation monitoring and dynamic early warning method and system based on multi-sensor data, and relates to the technical field of slope monitoring, and the method comprises the steps: collecting multi-source sensor data by using pre-deployed multi-class sensors, constructing graph structure data according to the sensor distribution and the pre-processed multi-source sensor data, and carrying out the graph structure data; a graph convolutional network is used for modeling, and a slope deformation monitoring model is constructed; introducing a clustering federation learning strategy to carry out joint training on the slope deformation monitoring models of the plurality of sites, and carrying out risk grade division by using the trained slope deformation monitoring models; key influence factors of landslide disasters are extracted, an improved firefly algorithm is introduced to dynamically optimize an early warning threshold value, the optimized early warning threshold value and the current risk level are used for judgment, and early warning information is generated. According to the invention, the reliability of monitoring and the timeliness of early warning are improved through multi-source data fusion and intelligent analysis, and the crossing of slope deformation monitoring from single-point static state to networked intelligence is realized.

The invention discloses a landslide hidden danger point displacement monitoring system applying multi-source data fusion, and the system comprises a multi-source sensing unit which comprises a spaceborne radar sensing module which is used for obtaining large-range ground surface deformation data through a synthetic aperture radar; the earth surface deformation sensing module is used for monitoring earth surface displacement in real time through a double-frequency GNSS receiver and an optical fiber grating sensor; the underground stress sensing module is used for acquiring the stress change of an underground rock-soil body through a micro-seismic monitoring array and a distributed optical fiber sensor; and the edge calculation unit comprises a dynamic weight fusion module which is used for dynamically distributing the weight of the multi-source sensor according to the geological state and generating high-precision fusion displacement field data. According to the invention, through multi-source data fusion, model parameter real-time updating and a closed-loop optimization mechanism, bottlenecks of a traditional monitoring system in aspects of data quality, model precision and response efficiency are broken through, and data support is provided for accurate early warning and intelligent emergency of landslide disasters.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention discloses a rock slope risk assessment method and system based on artificial intelligence, and belongs to the technical field of geological disaster risk assessment.The rock slope risk assessment method comprises the steps that a slope three-dimensional digital model is constructed, and geological-environment-monitoring data are integrated; extracting spatio-temporal characteristics, and predicting a landslide risk probability; quantifying a risk space diffusion path, and identifying a high-risk area; the early warning threshold value is dynamically adjusted, and accurate early warning is achieved; the problems that in the prior art, how to integrate multi-modal data (geological data, environment data and monitoring data) to achieve real-time sensing of the slope state and how to construct a deep learning model with high generalization ability to deal with risk prediction under the complex geological condition are solved. The problem of how to establish a risk transfer model considering spatial heterogeneity to improve high-risk area identification precision and how to realize dynamic adaptive adjustment of an early warning threshold to match personalized risk features of different slopes is solved.

The invention belongs to the technical field of risk prediction, and discloses a landslide risk prediction method and system based on data intelligent analysis, and the method comprises the steps: obtaining corresponding multi-source heterogeneous information, carrying out the feature extraction and data fusion of the information, and obtaining a mountain monitoring basic data set; constructing a mountain twinborn feature model based on the mountain monitoring basic data set, and performing landslide scene simulation based on the mountain twinborn feature model to obtain corresponding risk feature information; constructing a trigger factor association network based on the risk feature information, and performing zoning evaluation and grading on the target landslide risk in combination with the mountain monitoring basic data set; constructing a corresponding grading risk zoning map; obtaining a landslide risk dynamic prediction result based on the grading risk zoning map; a corresponding grading early warning rule base is constructed; and the target mountain area is monitored in real time based on the grading early warning rule base, and the monitoring result is fed back. According to the invention, a comprehensive and efficient solution is provided for prevention and treatment of landslide disasters.

The invention relates to the technical field of geological landslide monitoring, in particular to a tower footing geological landslide monitoring and early warning system and method. The method comprises the following steps: arranging an inclination angle sensor, a Beidou-GNSS module, a soil moisture content probe and a micro-seismic accelerometer on a tower footing and the periphery of the tower footing, and collecting the inclination amount, three-dimensional displacement, soil moisture content and vibration response information of the tower footing to form a monitoring original data set; timestamp unification, WGS-84 projection coordinate system conversion and multi-frequency vibration elimination are carried out on the monitored original data set, and an air-ground integrated displacement profile is constructed; the whole process realizes high integration and automation in the aspects of data acquisition, processing, fusion and transmission, so that the timeliness, accuracy and data integrity of landslide risk monitoring are ensured, and scientific decision support is provided for disaster prevention and reduction.

The invention discloses a plateau mountain road disaster identification method and system based on multi-source remote sensing image restoration and super-resolution reconstruction. The method comprises the following steps: acquiring and preprocessing a multi-source remote sensing image of a plateau mountain region, and extracting landform measurement parameters based on a digital elevation model; a super-resolution reconstruction network fusing deformable convolution and Transform is constructed, and a low-resolution image is reconstructed by using constraint training of a composite loss function containing geomorphic measurement parameters; performing feature extraction and adaptive weighted fusion on the preprocessed image and the reconstructed high-resolution image; based on the fused image, utilizing a multi-task deep learning model to identify landslide, debris flow and roadbed subsidence disasters along the highway; and carrying out morphological optimization and boundary refinement under GIS constraint on an identification result, and outputting a disaster thematic map. According to the invention, the precision and reliability of road disaster identification in a complex terrain environment are effectively improved.

The invention provides an intelligent landslide identification method and system based on hierarchical frame selection and boundary feature fusion, and the method comprises the steps: obtaining a target remote sensing image data set, carrying out the hierarchical frame selection of the target remote sensing image data set, obtaining a hierarchical frame selection region set, carrying out the boundary feature extraction of the hierarchical frame selection region set, and obtaining a boundary feature extraction result; obtaining a boundary feature set of each hierarchical framed area, inputting the boundary feature sets into a pre-trained boundary feature fusion model for feature fusion processing to generate a fused boundary feature set, performing feature comparison processing with a preset landslide morphological feature library based on the fused boundary feature set to generate a landslide identification feature set, and performing landslide identification on the landslide identification feature set. And performing region labeling processing on the target remote sensing image data set according to the landslide recognition feature set to generate a landslide region recognition result. According to the invention, through topology reconstruction of hierarchical frame selection and feature fusion, a landslide identification link with a self-interpretation capability is formed, and the problem of misjudgment accumulation of the model caused by feature black box transmission is avoided.

A geological disasters monitoring method, device, medium and product are provided, which relates to the technical field of geological monitoring. The method includes determining microscopic deformation parameters of an area to be monitored according to remote sensing observation data corresponding to a slope body of the area to be monitored, determining macroscopic deformation parameters of the area to be monitored according to optical remote sensing data and terrain data of the area to be monitored, and determining the landslide remote sensing geomechanical deformation type in the area to be monitored according to material composition, movement mode, slope structure, the microscopic deformation parameters and the macroscopic deformation parameters of the area to be monitored. The present disclosure improves the accuracy of monitoring geological disasters.

The invention relates to the technical field of image data processing, in particular to a difficult mountainous area slope three-dimensional modeling method and system based on a remote sensing technology. The method comprises the following steps: obtaining an original remote sensing data set of a target hard mountain area, and carrying out data preprocessing to obtain a standardized remote sensing data set; performing target hard mountainous area surface feature extraction on the standardized remote sensing data set to obtain hard mountainous area surface feature data; constructing a three-dimensional terrain model by using the earth surface feature data of the hard mountain area to obtain preliminary three-dimensional terrain model data; and performing time sequence analysis on the standardized remote sensing data set, and performing slope dynamic change analysis on the initial three-dimensional terrain model data by using a time sequence analysis result to obtain slope displacement change data. Through three-dimensional modeling, dynamic change analysis, stability evaluation and landslide early warning, a set of complete disaster prevention and reduction system for the hard mountainous area is constructed, and the prediction and prevention capability for landslide disasters is effectively improved.

The invention relates to the technical field of landslide information extraction, in particular to an SAR image landslide information extraction method based on a deep learning algorithm. The method comprises the following steps: acquiring an SAR image and an optical image of a seismic region; mapping the landslide boundary marked in the optical image to the SAR image so as to mark the SAR image to obtain a label image; constructing a data set based on the original image and the tag image of the SAR image; training a pre-constructed semantic segmentation model by using the data set; and generating a segmentation image based on the trained semantic segmentation model to extract a landslide region. On the basis of the SAR image with rich polarization characteristics, the deep learning semantic segmentation network is used for landslide region segmentation, and the better segmentation precision is realized.

The invention discloses a tower foundation landslide monitoring method, system and device and a medium. The method comprises the steps that InSAR data and GNSS data are fused to conduct wide-area deformation monitoring, a quadratic fitting model is established to conduct error dynamic correction on InSAR observation values, and a potential landslide hidden danger area is recognized; the method comprises the following steps: performing multi-dimensional cooperative monitoring on a potential landslide hidden danger area through an unmanned aerial vehicle carrying a sensor, and fusing multi-source data by utilizing edge computing equipment to generate a landslide risk thermodynamic diagram; an active waveguide acoustic emission system is adopted to capture landslide deep deformation signals, and early warning is achieved through ringing counting and acoustic emissivity parameters. According to the invention, the InSAR and GNSS technologies are fused, and the Kalman filtering algorithm is combined, so that the precision of surface deformation monitoring is improved; the InSAR observation value is constrained and corrected through the GNSS observation value, so that the deformation monitoring precision is improved; more accurate data support is provided for landslide monitoring through the unmanned aerial vehicle carrying sensor equipment; powerful support is provided for early warning of landslide disasters through an active waveguide acoustic emission technology.

The invention discloses a multi-sensor monitoring landslide early warning method, device and equipment and a medium, and the method comprises the steps: collecting three-dimensional displacement data and multi-source environmental factor data of a to-be-monitored slope region through a plurality of environmental sensors, and carrying out the preprocessing of the collected data; constructing an initial landslide probability prediction model, and training based on a preset algorithm to obtain a target landslide probability prediction model; inputting the current monitoring data into the trained prediction model, and outputting a landslide probability prediction result; a preset feature contribution interpretation mechanism is introduced to quantify the influence of each environment feature in the prediction of the landslide probability; and dynamically adjusting a landslide probability index based on the predicted landslide probability and the feature contribution degree, and setting a multi-stage early warning threshold to realize graded early warning of the landslide risk. According to the invention, a landslide early warning technical framework with adaptive updating and interpretable analysis capabilities is constructed, and the accuracy, stability and engineering adaptability of landslide early warning are improved.

The invention discloses a high dam and large reservoir area early warning landslide factor threshold determination method based on deep learning. The method comprises the steps of obtaining a multi-source landslide monitoring data set with a uniform structure; forming a factor-frequency band separation data tensor; inputting the factor-frequency band separation data tensor into an FEDform frequency domain decoupling module to jointly form a multi-scale prediction component; jointly inputting the multi-scale prediction component and the factor-frequency band separation data tensor into a residual gating fusion layer, and outputting a residual gating fusion feature tensor; in a dynamic threshold generation module, monitoring factor weight distribution is extracted based on a residual gating fusion feature tensor, and a dynamic landslide early warning threshold is formed; and dividing risk early warning grades according to preset risk early warning grades. According to the method, the dynamic upper threshold and the dynamic lower threshold can be automatically contracted or expanded according to the landslide risk change, and the problems of static threshold hysteresis and misjudgment under the extreme working condition are effectively avoided.

The invention discloses a tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, and belongs to the field of tunnel-landslide masses, and the method comprises the following steps: S1, extracting a feature parameter set and an abnormal event mark set; s2, constructing a three-dimensional geological-mechanical model; s3, space-time multi-scale modeling of multi-physics field coupling is carried out; s4, outputting a stability prediction level by using the constructed ISSA-LSTM-GARCH combined prediction model; s5, generating an early warning in combination with an association rule engine; and S6, knowledge updating and systematic evolution of closed-loop optimization are carried out. According to the tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, multi-source monitoring data and a multi-physical field coupling model are fused, landslide mass stability under the influence of tunnel engineering is accurately and comprehensively analyzed through high-precision positioning and parameter inversion optimization, and powerful support is provided for engineering decision making.

The invention discloses a landslide mass monitoring method based on an unmanned aerial vehicle laser radar. The landslide mass monitoring method comprises the following steps: S1, constructing an exposed earth surface point cloud model of a first time phase; s2, constructing an exposed earth surface point cloud model of a second time phase; s3, establishing a standard point cloud data set; s4, constructing a multi-dimensional time sequence feature vector sequence; s5, introducing a fusion prediction model of the ARIMA and the neural network, and outputting a final prediction sequence of the deformation trend of the landslide mass; and S6, constructing a disaster chain propagation model, outputting a landslide early warning grade according to a preset grading threshold value, and performing real-time early warning information release and response linkage. According to the invention, through fusion of laser radar point cloud registration analysis and ARIMA-neural network prediction modeling, high-precision dynamic monitoring and early warning of tiny deformation of the landslide mass are realized, and the method is suitable for landslide early identification and disaster emergency response scenes in a complex terrain environment.

The invention relates to the technical field of monitoring and identification, and discloses a rainfall type group landslide risk area and hidden danger point comprehensive identification and evaluation method. The method comprises the following steps: determining a landslide danger index and a disaster-bearing body vulnerability index of each calculation unit in a target area according to an updated comprehensive database; determining a risk evaluation result according to a landslide risk index of each sub-region determined by the landslide risk index and the disaster-bearing body vulnerability index, wherein the risk evaluation result comprises a landslide risk grade corresponding to each sub-region; determining a deformation area in the target area according to synthetic aperture radar data; determining a hidden danger point identification result corresponding to the deformation area, wherein the hidden danger point identification result comprises disaster hidden danger points in the deformation area and hidden danger risk levels corresponding to the disaster hidden danger points; adjusting a hidden danger point identification result according to the risk evaluation result; and adjusting a risk evaluation result according to a hidden danger point identification result and a disaster hidden danger point treatment result. According to the method, dynamic feedback and optimization of risk and hidden danger assessment can be realized, and misjudgment is reduced.

The invention relates to the technical field of geological disaster recognition, and discloses a slope cutting house building landslide hidden danger recognition method and device based on a remote sensing image, and the method comprises the steps: obtaining remote sensing image data and SAR image data for a target region; analyzing the remote sensing image data to obtain a landslide hidden danger position identification result corresponding to the target area; the landslide hidden danger position identification result comprises position information corresponding to a plurality of landslide hidden danger points related to slope cutting and house building; based on a PS-InSAR technology, analyzing the SAR image data to obtain a deformation area identification result corresponding to the target area; and determining landslide hidden danger information corresponding to a plurality of target hidden danger areas in the target area according to the landslide hidden danger position identification result and the deformation area identification result. Therefore, the landslide hidden danger is analyzed by combining the remote sensing image data and the SAR image data, and the landslide hidden danger recognition efficiency and recognition accuracy of slope cutting and house building can be improved.

The invention relates to the technical field of geological disaster monitoring and early warning, in particular to a complex high-position landslide risk dynamic identification system based on multi-source InSAR cooperation, and the system comprises a data collection module which is used for constructing a normalized feature vector representing the state of each monitoring node; the correlation modeling module is used for quantifying the physical influence degree among the heterogeneous nodes; the state prediction module is used for iteratively updating the hidden state of each heterogeneous node by adopting a space-time diagram neural network model and interpreting the hidden state into incremental displacement; the risk assessment module is used for calculating a risk precursor index of each heterogeneous node deviating from a normal evolution mode based on the hidden state, and judging and issuing graded early warning according to a preset threshold system; according to the method, the overfitting problem of a pure data driving model during data sparsity is effectively relieved, and the generalization ability and the physical interpretability of a prediction result are remarkably improved.

The invention relates to the technical field of landslide displacement data prediction, in particular to a GCN-MHA-GRU-based landslide displacement prediction method for a black square platform. Acquiring synthetic aperture radar monitoring data of the black square table area; extracting landslide surface deformation data of the black square table area from the synthetic aperture radar monitoring data; decomposing the landslide earth surface deformation data by adopting a three-dimensional deformation decomposition method according to a satellite orbit; establishing a landslide prediction model based on a graph neural network, a multi-head attention mechanism and a gating circulation unit; inputting the decomposed landslide surface deformation data into the landslide prediction model for training; and carrying out landslide displacement prediction on the black square table area according to the trained landslide prediction model. According to the method, an efficient, accurate and robust landslide displacement prediction model is constructed, and powerful technical support can be provided for scientific prevention and control of geological disasters.

The invention relates to a landslide grading early warning method based on multi-modal data change characteristics. The method comprises the following steps: establishing a mountain digital twinborn body; the method comprises the following steps: deploying a multi-node sensor network in a target area, configuring an edge computing unit, collecting geological data in real time, and screening the geological data based on mountain digital twin to form effective local data; constructing a lightweight multi-modal neural network model at each node, dynamically searching hyper-parameters by using an ant colony optimization algorithm, and generating an encryption model weight update quantity packet; the central server dynamically calculates node weights according to disaster feature vectors output by the digital twins, generates a global model through weighted aggregation, and directionally distributes and updates the global model; real-time monitoring data and a model prediction result are fused, millimeter-level disaster evolution simulation is executed through a variable step size physical engine, an advanced early warning signal is triggered when a deduced prediction risk exceeds a threshold value, and the edge model adaptability, federal aggregation precision and early warning advancement are remarkably improved.

The invention relates to the technical field of earthquakes, in particular to an optical remote sensing image feature landslide information extraction method. A double-time-phase NDVI time sequence analysis strategy is adopted, and landslide area recognition is achieved by constructing a vegetation index difference chart before and after an earthquake. A landslide mass area before an earthquake presents a high NDVI value due to complete vegetation coverage, and a vegetation index of the area in an image after a disaster is significantly attenuated due to surface disturbance, so that an obvious change response is formed. If the normalized vegetation index does not change, a non-landslide area can be determined, and the greater the change of the normalized vegetation index is, the greater the possibility of landslide occurrence is, and a landslide preselection area is determined; then Otsu threshold segmentation is carried out by combining cloud layer features and water body features, cloud layer and water body change parts are effectively eliminated, object-oriented geometric shape rule fine recognition is carried out on a preselected area, a DEM is adopted to calculate a slope value to constrain low-lying or flat earth surface changes, and efficient and accurate landslide information extraction is achieved.

The invention relates to the technical field of slope stability analysis and landslide disaster prevention and control, in particular to a slope three-dimensional stability calculation method and system based on a potential sliding direction. The method comprises the following steps: acquiring slope point cloud data, and constructing a digital elevation model according to the slope point cloud data; geologic structure data are obtained, and a side slope three-dimensional geologic model is constructed according to the geologic structure data and the digital elevation model; according to the digital elevation model, side slope flow direction and slope runoff characteristics are obtained, and according to the side slope flow direction and the slope runoff characteristics, the potential sliding direction of the side slope is determined; and according to the potential sliding direction of the slope and the three-dimensional geological model of the slope, constructing a three-dimensional stability calculation model of the slope, and according to the three-dimensional stability calculation model of the slope, calculating the three-dimensional stability of the slope. According to the method, the problem that the calculation result is not accurate enough due to the fact that calculation of the three-dimensional stability of the side slope is not comprehensively considered in the prior art is solved.

The invention relates to the technical field of geological disaster monitoring, and discloses a landslide dynamic simulation monitoring and early warning method based on multi-source sensing fusion, and the method comprises the steps: collecting multi-source data of a landslide body through a plurality of heterogeneous sensors, and enabling the multi-source data to be in space-time alignment after preprocessing; building a multi-parameter fusion model fusing a displacement field, a mechanical field and an environment field based on the preprocessed data, enabling the multi-parameter fusion model to output a deformation rate and a stability coefficient, and dynamically adjusting the weights of the displacement field, the mechanical field and the environment field according to a landslide evolution stage; predicting a future deformation trend of the landslide mass in combination with a geological structure and historical data, comparing a stability coefficient with a dynamic safety threshold to judge a risk level, and generating early warning information; and dynamically correcting a reference weight coefficient in the model based on the deviation between the monitoring data and the model output, so that the model is adaptively optimized. The problems of single monitoring dimension and static model solidification in the prior art are solved, and accurate and adaptive monitoring and early warning of the risk state of the landslide mass are realized.

The invention discloses an automatic identification method for a popular shallow soil landslide disaster risk area. The method comprises a landslide risk area identification method and an intra-area landslide threat house identification method. The method comprises the following steps: firstly, acquiring terrain, soil layer structure, vegetation and rock-soil distribution information of a region through a remote sensing technology, and performing data acquisition and processing by adopting remote sensing images, infrared images, laser radar data and synthetic aperture radar data to generate a high-precision digital elevation model and a geologic structure map; through data fusion and analysis, in combination with information such as soil layers, terrains and vegetation, a potential landslide risk area is identified, and houses in the landslide risk area are identified and evaluated. According to the method, the identification precision of the landslide risk area and the threatened house is improved, and efficient and accurate technical support is provided for early warning and prevention of landslide disasters. The system has the advantages of automation, high efficiency and accuracy, and can be widely applied to real-time monitoring, risk assessment and emergency management of landslide disasters.

The embodiment of the invention discloses a landslide disaster evolution analysis method and system based on a coupling model, and the method comprises the steps: carrying out the multi-source data integration of geological structure monitoring data, landform distribution data and hydrological environment monitoring data of a target seismic region, and generating a comprehensive monitoring data set; based on the data set, establishing an interaction association relationship of the target equation set, and constructing a geology-landform-hydrology coupling model; performing dynamic simulation and quantitative evaluation on the instability probability and the landslide scale of the potential landslide mass by using the coupling model according to a preset scene parameter combination, and generating an image-text annotation quantitative evaluation report; and finally, carrying out vulnerability analysis and risk assessment on the disaster-bearing body based on the report, generating a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution diagram, landslide risk prevention and control measure suggestions and a regional space planning optimization scheme, and providing a scientific basis for landslide disaster prevention and control and regional planning.

The invention discloses a slope physical similarity model device and a slope internal crack evolution identification method, and relates to the technical field of open pit coal mine slope ecological protection, and the slope physical similarity model device comprises a similar material simulation system, a monitoring system and an artificial rainfall system. Through cooperation of the similar material simulation system, the monitoring system and the artificial rainfall system, surface water seepage field change and instability process simulation caused by slope rock-soil mass fracture evolution under different main control factors can be artificially controlled, and temperature, rainfall, stress, fracture development and other characteristic changes in the whole process can be monitored in real time; the method can deeply explore the internal relationship with slope instability, obtain the slope instability failure path, failure source and key block, determine the main control factor threshold of each stage, reveal the seepage and instability mechanism of the open pit coal mine slope under the freeze-thaw cycle, and improve the stability of the open pit coal mine slope. And a theoretical basis is provided for establishment of multi-source criteria of seasonal freeze-thaw landslide of the high and cold opencast coal mine and early-and-middle-stage early warning.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring system for landslide and a method thereof, and relates to the field of geological disaster monitoring. The method has a high-precision deformation monitoring effect, through multi-source SAR data fusion, interference pair screening optimization and multi-source error correction (such as troposphere and ionosphere delay correction), the fidelity of a deformation phase sequence is remarkably improved, and the precision limitation under a complex terrain is overcome; the method has an intelligent geological constraint inversion capability, introduces geological prior knowledge (such as fault and fracture characteristics), enables a deformation field to better conform to the actual geomechanical law through deep learning semantic segmentation and a Bayesian inversion model, and reduces misinformation and missing report. The method has self-adaptive landslide recognition, adopts deformation gradient field calculation and a dynamic threshold segmentation algorithm, automatically recognizes a potential sliding zone boundary, improves the landslide partitioning efficiency, and is suitable for large-range monitoring.