2327 results about "Geological disaster" patented technology

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

This application relates to the field of geological disaster early warning technology, and discloses a method for monitoring, predicting and warning of geological disasters based on artificial intelligence, including the following steps: data collection: collecting geological disaster monitoring data; data preprocessing: time alignment and missing completion of the collected data; spatiotemporal correlation matrix construction: analyzing the preprocessed monitoring data; feature extraction: extracting the nonlinear spatiotemporal characteristics of geological disaster monitoring data; time series prediction: outputting the geological disaster risk level in the future period; feedback optimization: based on the error between the prediction result and the actual observation data; warning release: generating geological disaster warning information based on the predicted risk level. Through data collection, preprocessing, feature extraction, prediction optimization and warning release, a complete geological disaster monitoring and prediction method is constructed, which integrates multi-source data, extracts key features and dynamically predicts risks, so as to ensure the accuracy of the model and provide a scientific basis for disaster prevention and control.

The invention relates to a geological disaster automatic identification method and system based on multi-source remote sensing data, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: collecting the multi-source remote sensing data of a to-be-detected region, carrying out the cross-modal registration, and generating a registered multi-source data set; carrying out multi-modal feature extraction based on the multi-source data set and carrying out space-time correlation analysis to obtain a multi-modal feature map; performing dynamic weight distribution on the multi-modal feature map, generating a fusion feature vector, inputting the fusion feature vector into a pre-trained geological disaster prediction model, and outputting a geological disaster probability map; performing binarization segmentation on the geological disaster probability graph to obtain a potential disaster area mask; and according to the geological disaster probability map and the potential disaster area mask, based on a preset joint determination rule of the surface deformation rate and the gradient characteristics, carrying out risk grade division on the potential disaster area to obtain a risk grade distribution map of the to-be-detected area. The disaster prediction precision can be improved, and the emergency response capability is enhanced.

The invention discloses a slope geological disaster multi-mode early warning method and system, and relates to the technical field of slope monitoring, and the method comprises the steps: laying a distributed sensor network, and collecting slope multi-source monitoring data; the multi-source monitoring data comprises displacement data, stress data and vibration frequency data; feature extraction is performed on the multi-source monitoring data by using a graph neural network, and the feature extraction comprises capturing spatial correlation among sensor nodes and identifying an abnormal mode, identifying a potential instability area of the slope according to the abnormal mode, and outputting features of the potential instability area; and generating a slope stability risk grade assessment report based on the characteristics of the potential instability region and a disaster evolution graph constructed by combining historical disaster data. According to the invention, the comprehensive monitoring of the slope from the outside to the inside and from the static state to the dynamic state can be realized, the abnormal mode and the potential instability area can be accurately identified, and the accurate assessment and timely early warning of the slope risk can be realized based on the historical data.

The invention discloses a geological disaster early warning method and a precise early warning system based on multi-source data fusion, and relates to the technical field of geological disaster early warning. According to the method, multi-source heterogeneous data such as remote sensing, meteorological and geological monitoring are fused, a standardized protocol is utilized to unify a data format and temporal-spatial resolution, a standardized data set is formed, key features are extracted by adopting principal component analysis and a recursive feature elimination algorithm, and a long-short-term memory network and a convolutional neural network model are combined, so that the real-time performance of the system is improved. According to the method, the disaster risk is accurately predicted, the space risk distribution diagram is generated, in addition, through application of the real-time stream processing framework and the self-adaptive learning algorithm, rapid distribution of early warning signals and dynamic optimization of model parameters are achieved, the accuracy and timeliness of an early warning system are remarkably improved, and the geological disaster risk is effectively reduced.

The invention discloses a landslide monitoring and early warning method and system based on a digital twinborn technology, and belongs to the technical field of geological disaster monitoring and early warning, and the method specifically comprises the steps: collecting the geographic information data of a landslide in real time; constructing a dynamically updated multi-source spatial database; the method comprises the following steps: constructing a landslide simulation model, performing mesh generation on the landslide simulation model, introducing a stress field and seepage field numerical coupling algorithm on the basis of a numerical analysis method, establishing a landslide mass hydraulic coupling model, performing solution on the basis of the landslide mass hydraulic coupling model, and accurately analyzing the existing state of the landslide mass. Acquiring the safety coefficient of the landslide mass in real time by adopting a strength reduction method; and establishing a disaster analysis model, if the safety coefficient reaches a preset alarm threshold, deducing a landslide disaster forming range, calculating a disaster risk index, evaluating a risk level according to the disaster risk index, and if the risk level is higher than a preset level, carrying out early warning on the landslide disaster. According to the invention, accurate landslide monitoring and intelligent safety early warning are realized.

The invention relates to the technical field of geological disaster monitoring and early warning, in particular to a side slope slippage monitoring and early warning method based on an image recognition technology, which comprises the following steps: arranging a monitoring target and a reference target in a to-be-monitored area of a side slope, arranging two image displacement monitoring devices at opposite stable positions at equal height to acquire two-dimensional displacement data of the targets; after error correction, an equipment monitoring coordinate system included angle is calculated based on parameters such as equipment spacing, slope inclination displacement is calculated through vector synthesis, and three-dimensional slippage deformation is calculated in combination with vertical displacement; meanwhile, a multi-level early warning mechanism based on the slip rate and the accumulated slip amount is established, a Delaunay triangulation network is constructed, and an improved adaptive Kriging interpolation algorithm is adopted to realize deformation trend prediction. According to the method, binocular vision and multi-algorithm fusion are utilized to realize high-precision three-dimensional monitoring, the evaluation accuracy is improved through the dynamic weighting model and intelligent early warning, the method has the advantages of high monitoring precision, timely early warning, high adaptability and the like, and the safety of slope engineering can be effectively guaranteed.

The invention discloses a geological disaster early warning method and system based on big data analysis, and particularly relates to the technical field of geological disaster early warning. Geological deformation time series data of a target area are obtained through satellite remote sensing monitoring, feature importance is evaluated through a random forest algorithm, and regional geological risk grades are divided; meanwhile, wavelet transformation and long-short-term memory network modeling are carried out on rainfall spatio-temporal data of the meteorological monitoring station, and the potential influence of rainfall is evaluated; high-risk areas are screened, an underground water level change rate and rock-soil body strength correlation model is constructed, and a support vector machine algorithm is adopted to evaluate the landslide probability; and finally, analyzing a geological risk grading result, the potential influence degree of rainfall on the target area and the landslide occurrence probability by adopting an analytic hierarchy process, calculating a comprehensive risk index, comparing the comprehensive risk index with a comprehensive risk threshold value, and judging whether to trigger grading early warning, thereby realizing data fusion and intelligent analysis, early warning in advance, and improving the early warning efficiency. And effective help is provided for disaster prevention and reduction.

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 relates to the technical field of geological disaster prevention and control, in particular to a rock engineering slope stability classification method. According to the technical scheme, the method comprises the following steps: obtaining spatial distribution characteristics of a slope structural plane through combined detection of three-dimensional laser scanning and a geological radar, and establishing a quantitative index system containing occurrence, density and connectivity of the structural plane; the method comprises the following steps: acquiring a fracture signal in a slope in real time by adopting a micro-seismic monitoring system, and extracting a micro-seismic event energy release rate, a dominant frequency offset and a seismic source mechanism parameter through time-frequency analysis; and constructing a geologic structure evolution inversion model, reconstructing a geologic structure evolution process based on the regional geologic database and the field drilling data, and calculating the distribution characteristics of the residual stress field of the structure. According to the method, comprehensive, accurate and real-time monitoring and classified evaluation of the rock engineering slope stability are realized, potential risks can be effectively predicted, a scientific and accurate decision basis is provided for slope support and treatment, and the safety and stability of the rock engineering slope are greatly guaranteed.

The invention discloses a multi-modal remote sensing intelligent identification method for hidden geological disasters. The method comprises the following steps: carrying out normalization processing on multi-modal remote sensing data; constructing a pixel-level cross-modal visual converter; constructing a pixel-level cross-modal attention module, wherein the pixel-level cross-modal attention module comprises a pixel self-attention sub-module, a pixel mutual-attention sub-module and a layer adaptation mixing module; a layer adaptive noise mechanism is introduced, and the weight ratio of intra-modal self-attention and inter-modal mutual attention in different levels is adjusted in a self-adaptive mode; constructing a relation discriminator, and modulating the attention calculation process by evaluating the difference between modals of corresponding points in the space; training the model, adopting a multi-task loss function including semantic segmentation loss and boundary perception loss to perform joint optimization, and generating a concealment geological disaster segmentation result. According to the method, complementary information of an RGB optical image, an InSAR deformation rate image and DEM elevation data is fully utilized, and accurate recognition of the hidden geological disaster is realized through an efficient pixel-level cross-modal fusion mechanism.

The invention relates to the technical field of data processing, and discloses a geological disaster meteorological risk early warning method and system based on machine learning. The method comprises the following steps: acquiring rainfall intensity, soil saturation, underground water level change and slope runoff coefficient by a multi-source sensor, and constructing a geological disaster meteorological data set; performing sensitivity weight distribution on the meteorological factors according to geological conditions to obtain a weight matrix; carrying out weighted fusion on the weight matrix and meteorological time series data, and extracting features through a geological constraint long-short-term memory network to obtain a risk probability vector; dynamically adjusting an early warning threshold value based on the safety coefficient change rate; and carrying out Bayesian fusion on the risk probability vector and an adaptive early warning threshold to obtain a graded early warning result. The technical problem that an existing geological disaster early warning technology lacks a multivariate meteorological factor intelligent weight distribution and geological condition adaptive threshold adjustment mechanism is solved.

The invention relates to the technical field of geological disaster monitoring, and discloses an intelligent early warning method and system for geological disasters in geotechnical engineering, and the system comprises a data collection module, a data processing module, a feature extraction module, an early warning model module, a response execution module and an optimization feedback module. Static geological parameters, dynamic environment parameters and historical disaster data are integrated, a standardized space-time correlation data set is constructed, the limitation of a single data source is broken through, multi-dimensional dynamic response characteristics of a rock-soil body are captured, a reliable data basis is provided for accurate early warning, the rigidity defect of a traditional fixed threshold value is avoided, and the early warning accuracy is improved. The method achieves the self-adaption of the risk early warning sensitivity, reduces the misjudgment and missing judgment caused by environment interference, intercepts a dynamic data stream in real time through a sliding window, calculates the risk mean value and variance, quickly responds to sudden environmental changes such as rainfall sudden change and vibration abnormality, and generates a graded early warning signal.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a railway tunnel portal geological disaster deformation early warning system based on an SAR radar, and the system comprises a digital twinborn body construction module which constructs a digital twinborn body with initial parameters based on basic data; the SAR deformation monitoring module is used for acquiring SAR deformation observation data; the digital twinborn body dynamic optimization module is used for inverting and updating parameters by using an optimization algorithm based on the SAR data, and generating an optimized twinborn body; a risk prediction and key area identification module which deduces a disaster scene based on the optimized twinborn body, predicts the risk and identifies a key risk area; and the intelligent early warning module is used for generating early warning information based on the prediction risk and the key risk area. According to the method, the geomechanical digital twins are dynamically optimized by adopting the SAR data, accurate prediction, key area identification and intelligent grading early warning of the geological disaster of the railway tunnel portal are realized, and the initiative and accuracy of risk cognition and early warning are remarkably improved.

The invention relates to a landslide detection method based on an improved YOLOv10n model, and belongs to the technical field of computer vision and geological disaster monitoring. The method comprises the following steps: firstly, collecting high-resolution remote sensing image data, and preprocessing by using a data enhancement technology; then, dividing the preprocessed remote sensing image data into a training set, a verification set and a test set, and carrying out real frame marking and category marking; then, an improved YOLOv10n model is constructed and trained on the basis of a Swin Transform backbone network, a depth separable convolution module and a context aggregation attention mechanism; and finally, outputting a landslide detection result by using the trained improved YOLOv10n model. According to the method, high efficiency and real-time performance are kept when high-resolution remote sensing images are processed, the method is particularly suitable for landslide real-time monitoring in a resource limited environment, the accuracy and efficiency of landslide detection are improved, the method can better adapt to complex terrains and background conditions, and efficient and reliable technical support is provided for landslide disaster monitoring.

The invention discloses a mine geological environment characteristic monitoring and recovery treatment method, and relates to the field of geological environment monitoring, and the method comprises the steps: collecting mine geological data; adopting a pre-trained LSTM neural network model to predict membership information of geological disaster evolution in a period of time in the future; adjusting a state transition probability matrix in the Markov chain model by using the membership information; predicting the evolution state of the geological disaster in a future period of time by using the adjusted state transition probability matrix; the method comprises the following steps: mapping mine geological data into nodes of a Bayesian network, and constructing a directed acyclic graph containing environmental factors; based on a Bayesian network inference algorithm, calculating the occurrence probability of geological disasters under different environmental factor combinations; fusing the predicted evolution state of the geological disaster in a future period of time with the geological disaster occurrence probability deduced by the Bayesian network to obtain a geological disaster prediction evaluation result; in view of low mine geological disaster time sequence evaluation precision in the prior art, the evaluation precision is improved.

The invention discloses a coal mining subsidence area surface crack intelligent extraction method based on an improved Transform model, and belongs to the technical field of remote sensing image processing. Firstly, an unmanned aerial vehicle carrying a high-resolution optical camera is used for collecting images, the image overlapping rate of 70%-80% is guaranteed, and a training data set is constructed through professional labeling, cutting screening and data enhancement. The encoder of the innovative model is very distinctive, and the adaptive multi-scale patch mapping layer can dynamically adjust the patch size according to the local complexity of the image and efficiently extract features; double-attention fusion is combined with optimization position coding, and long-distance dependency capture is enhanced; and the calculation amount and the overfitting are reduced by the dynamic sparse connection full-connection layer. Residual attention enhancement pyramid pooling and a space-channel attention bottleneck mechanism are adopted, key features are highlighted, and noise is suppressed; and a breakpoint detection and connection rule determination module is utilized to realize complete restoration of the ground fracture. After the data set is used for training a model, deployment is carried out, and through preprocessing, encoding and decoding and post-processing, ground fracture information can be accurately obtained, and a data foundation is built for mining area safety management and geological disaster prevention and control.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster detection method and monitoring system based on unmanned aerial vehicle scanning. Comprising the following steps: S1, configuring an unmanned aerial vehicle-mounted tilt camera, a multispectral laser radar and a high-precision positioning module, planning a route, carrying out multi-angle scanning on a target area, and obtaining earth surface three-dimensional point cloud data, a multispectral image and terrain elevation information; s2, preprocessing the collected original image data, including point cloud denoising, image distortion correction and multi-source data registration, and generating a high-resolution live-action model fused with three-dimensional geographic information data; s3, extracting various data based on the live-action three-dimensional model to construct a geological disaster hidden danger analysis model, calculating a risk index through multi-factor weighted fusion, training a transfer learning model in combination with historical disaster data, and outputting a hidden danger type and probability; according to the invention, the geological disaster type can be analyzed based on the geological condition and the targeted processing strategy can be specified.

The invention belongs to the technical field of tunnel construction geological disaster prevention and control, and discloses a multi-source data fused refined treatment decision-making method for a complex stratum disaster source, which comprises the following steps: collecting and fusing multi-source geological data, and constructing a three-dimensional geological model; generating a disaster source risk dynamic assessment and treatment scheme; based on a fluid-solid coupling similarity theory, verifying the preliminary treatment scheme by adopting a physical model test, and determining an optimal treatment scheme; the optimal treatment scheme is executed, and the treatment process is dynamically regulated and controlled; after treatment, the treatment effect is evaluated through posterior data, and the effect data is fed back to the three-dimensional geologic model and the knowledge base, so that the dynamic updating of the model and the self-learning of the decision-making system are realized. By the adoption of the treatment decision method, the problems that a traditional method depends on experience, information is one-sided, and treatment is extensive are solved, advanced accurate forecasting and refined and personalized treatment of complex stratum disaster sources are achieved, and the safety and efficiency of tunnel construction are remarkably improved.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a Beidou-based geological disaster intelligent monitoring and early warning method and system. Beidou high-precision monitoring equipment is deployed by selecting a geological disaster prone area, earth surface displacement, settlement and inclination deformation data are collected in real time, and a multi-modal database is constructed in combination with environmental parameters. And performing alignment and noise correction on the spatio-temporal data by adopting Kalman filtering and a weighted evidence theory, extracting short-term and long-term deformation characteristics by utilizing a DBSCAN spatial clustering algorithm, and realizing multi-scale abnormal change pattern recognition in combination with a GeoHash grid index. Dimensional differences are eliminated through Z-score standardization processing, a geological stability index and change rate model is established, a causal reasoning framework is further constructed based on a Bayesian network, and a risk prediction model is trained in combination with a space-time neural network. The system can dynamically adjust a monitoring period threshold value and automatically trigger graded early warning, and supports hidden danger rectification whole-process tracing and multi-level gridding management. According to the scheme, the limitation of traditional single-source monitoring is broken through, the full-chain prevention and control of geological disasters from deformation feature extraction, causal relationship modeling to dynamic risk prediction is realized, and the early warning timeliness and accuracy are remarkably improved.

The invention discloses a geological disaster monitoring system based on multi-modal data, and relates to the technical field of geological disaster monitoring, and the system comprises a crack analysis module which carries out the time-space correlation mining of the crack propagation rate of a monitoring region, and analyzes the nonlinear evolution characteristics and spatial differentiation rules of rock mass fracture; and the critical identification module is used for performing wavelet packet energy spectrum analysis on the inclination angle change rate of the geologic body, identifying a critical turning point of rigidity attenuation of the geologic structure in combination with a preset algorithm, judging whether the overall stability enters an instability acceleration stage or not, and performing multi-parameter collaborative detection, crack evolution cross validation and dynamic trend stability verification to obtain the stability of the geologic structure. The accuracy and reliability of geological structure rigidity attenuation critical turning point recognition are remarkably improved, a more accurate rigidity attenuation stage judgment basis is provided for an early warning module, and the capturing capacity of a multi-modal data geological disaster monitoring system for structure instability precursor is enhanced.

The invention relates to the technical field of geological informatization, in particular to a time-space sequence prediction and risk assessment model for geological disaster monitoring data, which comprises a data acquisition module, an edge processing module, a cloud analysis module, a mobile terminal module and a data processing module, the server is connected with the cloud analysis module through a WebSocket protocol, and is used for receiving the prediction result and the visual data; and displaying the three-dimensional illness state evolution graph on augmented reality (AR) equipment. Through the innovative technical scheme, comprehensive breakthrough is achieved in the aspects of multi-modal data fusion, dynamic feature extraction, accurate prediction, robustness design, visual display and the like. The innovation points cooperate with one another and promote one another, and an efficient, reliable and intelligent MS remote monitoring and prognosis prediction system is jointly constructed.

The present invention relates to the field of disaster early warning technology, specifically to a method for early warning and forecasting of geological disasters by satellite remote sensing monitoring, which includes the following steps: based on ground remote sensing images, edge information of images is extracted frame by frame, pixel difference values ​​and spatial displacement information are identified, and the surface area where changes occur is marked to obtain basic anomaly detection results. In the present invention, by analyzing the ground remote sensing images frame by frame to extract edge information and compare the pixel differences between the previous and next frames, the slight changes in the surface can be monitored more finely, and by matching and analyzing historical data, potential anomaly types can be accurately identified, and targeted early warnings can be given for different types of geological disasters. By performing time series analysis on the trends of abnormal areas, the development trend and potential impact of disasters can be predicted, providing data support for disaster prevention and mitigation, improving the response speed and accuracy of the early warning system, and reducing the losses caused by untimely disaster response.

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 provides a shield adaptability adjusting method based on a stratum structure, which comprises the following steps: acquiring geological parameters of a construction area of a shield tunneling machine through drilling and geophysical prospecting, and arranging sensors at key positions of the shield tunneling machine to acquire construction parameters of the shield tunneling machine in real time; a convolutional neural network CNN is adopted to analyze the geological parameters to identify stratum features, and a geological model is constructed based on the identified stratum features; constructing a disaster risk index system, and predicting a disaster risk based on the geological model and the construction parameters by adopting Logistic regression and a random forest algorithm; according to the predicted disaster risk, the cutterhead configuration of the shield tunneling machine is dynamically adjusted, and tunneling parameters are optimized or a muck improvement scheme is adopted; by monitoring geological parameters, construction parameters and environmental changes in real time, a multistage early warning mechanism is adopted to trigger emergency response measures, and construction safety is guaranteed. According to the method, the safety and efficiency of shield construction can be improved, the geological disaster risk is reduced, and reliable technical guarantee is provided for underground space development.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method and system based on dynamic data monitoring, and the method comprises the following steps: obtaining geological displacement monitoring data, setting a time window range, respectively calculating a local extreme point and a local zero point of the monitoring data in a time window, and calculating the local extreme point and the local zero point of the monitoring data in the time window; obtaining a multi-scale reference value; and based on the multi-scale reference value, calculating a comprehensive statistical magnitude, and generating a data quality evaluation parameter. According to the method, a local extreme point and a zero point are dynamically calculated by setting a time window range, a multi-scale reference value is constructed to carry out comprehensive statistic generation and data evaluation, and collaborative discrimination of piecewise linear regression and threshold comparison are executed to form a critical index; and performing characteristic quantity recursive residual spectrum analysis and matrix decomposition operation by combining the power spectral density distribution and the spectral density matrix to establish an early warning characteristic sequence, and realizing multi-stage early warning indication output based on a grading rule.

The invention relates to the field of highway engineering safety monitoring, in particular to a highway slope stability image monitoring and risk early warning system which comprises an image acquisition module, a slope parameterization representation module, a multi-scale curvature flow analysis module, a Riemannian manifold learning module and a risk early warning module. Differential features are extracted by calculating a measurement tensor and a curvature tensor of the curved surface; constructing multi-scale representation of the slope curved surface by applying a curvature flow theory, and identifying deformation characteristics under different scales; according to the method, riemannian manifold learning and a geodesic convolutional network are utilized to extract advanced features, geological disaster precursors such as landslide, collapse and debris flow are accurately identified, the system has a self-adaptive monitoring frequency adjustment function, monitoring modes are automatically switched according to risk levels, the slope disaster risk is greatly reduced, and a powerful guarantee is provided for safe operation of roads.

The invention discloses a geological disaster monitoring and early warning method based on multi-factor dynamic coupling, belongs to the technical field of geological disaster monitoring and early warning, and aims to solve the problems that a traditional method cannot fuse multi-source factors in real time, is low in early warning precision, lags in response and the like. A geological environment static background factor is combined to construct a susceptibility evaluation model, a dynamic weight is analyzed and calculated by adopting a time sequence, a dynamic Bayesian network is utilized to carry out coupling analysis, and a geological disaster risk probability value is output in real time, so that a corresponding early warning level and an emergency response are triggered. The method is mainly used for real-time monitoring, accurate risk assessment and timely early warning of geological disasters.

The invention relates to the technical field of big data analysis, in particular to a geological disaster risk assessment platform and method based on big data, and the platform comprises a multi-source data collection module, a dynamic weight fusion module, a dual-channel time sequence modeling module, a risk probability dynamic deduction module, a multi-dimensional coupling assessment module and a three-dimensional visualization early warning module. Wherein the multi-source data acquisition module is used for acquiring multi-source data in real time; the dynamic weight fusion module is used for generating a fusion data set of time-space alignment; the dual-channel time sequence modeling module is used for outputting a coupling feature vector; and the risk probability dynamic deduction module is used for generating a regional geological risk probability distribution diagram. According to the geological disaster risk assessment system and method, accurate geological disaster risk prediction and assessment are provided by fusing multi-source data and adopting an advanced time sequence analysis and assessment technology, an interactive risk map can be generated in real time, graded early warning signals can be triggered, and data support is provided for disaster management and emergency response.

The invention discloses a geological disaster early warning method and system based on multi-source data fusion and electronic equipment, and the method comprises the steps: carrying out the alignment of remote sensing data, sensor data and meteorological data in a space dimension and a time dimension, and obtaining multi-source data after the time-space alignment; performing noise elimination and missing value filling on the multi-source data after space-time alignment to obtain processed multi-source data; extracting multi-source features based on the processed multi-source data, and performing feature fusion on the multi-source features to obtain a multi-source spatio-temporal data cube; constructing a geological disaster prediction large model comprising a spatial feature extraction layer, a time sequence feature aggregation layer and a disaster classification and regression branch; inputting the multi-source spatio-temporal data cube into a trained geological disaster prediction large model for prediction, and obtaining a risk level classification result and a displacement change value; and performing geological disaster early warning according to the risk level classification result and the displacement change value. The geological disaster early warning accuracy can be improved.