1462 results about "Geological disaster" patented technology

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 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 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 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.

The invention, which belongs to the technical field of image analysis and geological disaster early warning, discloses a high-precision topographic change monitoring and geological disaster early warning image analysis system comprising a deformation spatio-temporal feature sensing module, a geomechanics constraint optimization module, a multi-scale disaster evolution prediction module and a self-adaptive early warning decision module. Through deep coupling and closed-loop feedback between modules, dynamic matching of deformation confidence and mechanical constraint weight, physical enhancement of stress distribution and disaster prediction, and dual-path parameter optimization driven by early warning performance are realized, and the system adopts an InSAR technology and deep learning fusion to extract millimeter-level deformation. The physical embedded neural network is utilized to realize anomaly recognition under mechanical constraints, multi-scale disaster evolution is predicted through space-time convolution Transform, and the early warning accuracy rate can reach 95% or above after closed-loop iterative optimization.

The invention belongs to the technical field of underground engineering unfavorable geological disaster prediction and intelligent control, and provides a tunnel unfavorable geological physical field-hydrological field fusion holographic detection method and system. Detection information of various physical fields such as an induced electric field, a seismic electric field, a natural electric field and a hydrological field is used as a fusion data source; establishing a coupling objective function taking cross gradient inversion as a physical constraint condition; a CNN-GNN-Transform hybrid network is constructed, and multi-level feature fusion is carried out; through contribution of physical constraint conditions in a self-adaptive weight dynamic balance coupling objective function and deep learning data driving feature learning capability of a hybrid network, multi-field holographic interpretation with a hybrid deep learning model as a carrier is realized, and the fusion degree of multi-source heterogeneous data is improved. Three-dimensional holographic imaging and water gushing prediction of a water gushing disaster source can be achieved, and the accuracy of unfavorable geological disaster detection and the reliability of intelligent decision making are improved.

The invention relates to the technical field of remote sensing, discloses a geological environment monitoring method and system based on multi-source remote sensing, and aims to solve the monitoring problems of heterogeneous multi-source remote sensing data, difficulty in dynamic capture, disjunction of geological mechanism and interpretation and the like. According to the method and the system, multi-source remote sensing data is acquired and standardized, multi-modal geologic features are extracted, through cross-modal deep fusion and space-time modeling, abnormity is analyzed and identified, risks are evaluated, and visualization and decision support are realized. According to the technical scheme, comprehensive, accurate and high-timeliness monitoring of the geological environment can be realized, the premonition of the geological disaster can be effectively identified, the trend can be predicted, and support is provided for prevention and control of the geological disaster.

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 provides a rock-soil body multi-field coupling intelligent monitoring system comprising a multi-field sensing device configured to monitor parameters of a deformation field, a seepage field, a temperature field and a stress field of a rock-soil body; the edge calculation and transmission device is configured to perform preprocessing and real-time transmission on the multi-field sensing data; and the intelligent decision-making device is configured to analyze the preprocessed data based on a multi-field coupling model and generate disaster early warning information. According to the rock-soil body multi-field coupling monitoring system and method provided by the invention, synchronous monitoring of a deformation field, a seepage field, a temperature field and a stress field is realized through the multi-field sensing device; real-time data processing is performed in combination with an edge calculation and transmission device, and a dynamic early warning mechanism is established by using an intelligent decision-making device, so that the technical problems of multi-field data space-time correlation failure, insufficient multi-source data fusion precision and poor early warning mechanism adaptability are effectively solved, and the geological disaster early warning method has the remarkable advantages of improving the geological disaster early warning accuracy and timeliness.

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 geological disaster detection, in particular to an unmanned aerial vehicle multi-sensor image fusion slope geological disaster detection system which comprises a data acquisition module, a manifold registration module, a feature fusion module, a weight optimization module, a fusion execution module, a disaster detection module and the like. RGB images, thermal infrared images and laser point cloud data of a slope are collected through an unmanned aerial vehicle, the surface of the slope is modeled as a Riemannian manifold, and high-precision space registration of heterogeneous data is achieved; constructing a feature manifold based on a manifold learning method, and extracting and fusing multi-scale features; evaluating the information amount of different areas by adopting a differential entropy theory, and generating a self-adaptive weight distribution diagram; performing weighted fusion on the registered multi-source data to generate a fused image; geological disaster features such as cracks, abnormal vegetation and water seepage points on the surface of the slope are recognized based on the fused image, and high-precision recognition and early warning of the geological disaster of the slope are achieved.

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 a landslide risk assessment method based on an extreme rainfall and geology coupling model, and relates to the technical field of geological disasters. Comprising the following steps: S1, constructing a three-dimensional probability density field of a fracture network and a non-Gaussian random field model of a permeability coefficient tensor; s2, setting a physical kernel layer according to the non-Gaussian random field model, setting a data driving layer through space-time Transform coding, and constructing a graph attention network model; s3, generating an adversarial network through physical information, constructing extreme rainfall coupling data, and updating the non-Gaussian permeability coefficient random field model according to the graph attention network model; and S4, acquiring an entropy generation rate according to the mechanical field data, the seepage field data and the temperature field data, and determining a risk level. Physical interpretability grading early warning of landslide risks is realized, and meanwhile, risk space distribution can be visually displayed through a sliding surface probability cloud picture, so that accurate decision support is provided for disaster prevention and control.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses a geological disaster risk intelligent pre-judgment method based on deep learning, and relates to the technical field of geological disaster monitoring and early warning. According to the method, high-precision alignment and feature extraction can be automatically performed on monitoring data with different temporal-spatial resolutions and different physical meanings, such as optical remote sensing, radar measurement, laser point cloud and the like, uniform and information-rich representations are generated, a solid data foundation is laid for subsequent accurate prediction, and the limitation of data splitting application in a traditional method is overcome; a space-time diagram with slope units as nodes is constructed, an attention mechanism diagram convolutional network with hydrological directivity introduced is utilized, and the model can accurately describe the spatial propagation process that slope substances migrate and accumulate along with a confluence path and block a river channel under the rainfall condition; meanwhile, the time sequence module effectively learns the influence of the past hydrological state on the future evolution trend.

The invention discloses an open pit coal mine slope monitoring and early warning method and system based on multi-modal satellite fusion AI, and belongs to the technical field of geological disaster monitoring. The method comprises the following steps: S1, collecting multi-source data; s2, transmitting data in real time; s3, data preprocessing and space-time registration; s4, multi-band InSAR phase reconstruction and deformation field extraction are carried out; s5, deeply fusing the multi-source data; and S6, AI intelligent prediction and grading early warning. According to the open pit coal mine slope monitoring and early warning method and system based on multi-modal satellite fusion AI, slope global millimeter-level precision continuous deformation field monitoring is achieved, vegetation terrain shielding interference is reduced, the monitoring range is expanded, meanwhile, monitoring blind areas are eliminated, data processing efficiency is improved, a high-reliability deformation field is constructed, and the early warning error and missing report rate is reduced.

The invention discloses a slope deformation trend prediction method based on three-dimensional point cloud and deep learning, and relates to the technical field of geological disasters, and the method comprises the following steps: S1, obtaining multi-time sequence three-dimensional point cloud data of a target slope, S2, carrying out the preprocessing, obtaining a standardized time sequence point cloud data set, and carrying out the prediction of the deformation trend of the target slope. S3, extracting slope deformation characteristic parameters from the standardized time sequence point cloud data set, S4, constructing a prediction model, S5, integrating the data into a model training sample, and training and optimizing the deep learning prediction model, and S6, inputting the data into the trained deep learning prediction model, and outputting a deformation trend prediction result of a target slope. And S7, carrying out reliability evaluation on the deformation trend prediction result, and generating a final prediction report. According to the method, through the deep learning model fusing the CNN and the attention mechanism LSTM, the spatial relevance and the time dynamics of slope deformation can be mined at the same time, compared with a traditional statistical model, the prediction precision is improved, and the method is especially suitable for long-term deformation trend prediction.

The invention relates to the technical field of geological disaster monitoring, and discloses a large-gradient deformation area phase optimization method and device, a medium and equipment, and the method comprises the steps: obtaining SAR data of a to-be-monitored area, and processing the SAR data to generate a differential interference image set; phase gradients of the differential interference image set in multiple directions are calculated to obtain a direction gradient image set, time sequence stacking and multi-direction fusion are carried out to obtain a phase gradient rate diagram, and a binary boundary diagram is determined through threshold segmentation; traversing the differential interferogram by using a sliding window, and performing structural consistency screening on neighborhood pixels in the window according to the binary boundary diagram to obtain a candidate pixel set; and performing amplitude consistency check, determining a homogeneous pixel set homogeneous with the central pixel, constructing a complex covariance matrix, and decomposing characteristic values to obtain an optimized phase of the central pixel. According to the method, the monitoring stability and precision are enhanced, the reliability and interpretability of a point selection result are improved, and an accurate phase reconstruction method is provided for a large-gradient deformation region.

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.

The invention provides a landslide classification method and system based on a visual language model and a cross attention mechanism. The method and the system specifically comprise the following steps: data preprocessing: carrying out Canny edge detection on an RGB image, and calculating terrain attributes such as a gradient and a slope direction for a DEM (Digital Elevation Model); feature extraction: capturing local features by adopting a reflection filling convolution layer and multi-scale residual connection; a visual language model is introduced, wherein semantic enhancement features are extracted through image-text alignment by means of the visual language model; cross self-attention fusion: capturing a global context through self-attention, and focusing heterogenous data complementary information by cross attention; and classifying and outputting: outputting a result by using global average pooling and a linear classifier. Through the visual language model and the cross self-attention mechanism, the landslide recognition capability under the complex terrain is effectively improved, an efficient and reliable technical means is provided for geological disaster monitoring, and the method can be widely applied to the fields of landslide recognition, risk assessment and the like.

The invention provides a landslide risk dynamic early warning method and system based on a Bayesian network, and belongs to the field of geological disaster intelligent prediction, and the method comprises the steps: constructing a disaster-inducing factor data set, and screening key factors based on a Pearson's correlation coefficient and an information gain method; an FP-Growth algorithm is further utilized to extract association rules among high-confidence factors, a Bayesian network structure is guided to be optimized, and a Bayesian network model with a causal relationship is constructed; the model supports an incremental learning mechanism based on a newly added landslide sample, can dynamically update a conditional probability table, and realizes landslide probability prediction and risk grade division in combination with a Bayesian forward reasoning result. The method has the advantages of being high in causal reasoning ability, high in model structure expression ability, excellent in prediction precision and capable of supporting real-time updating and risk partition, and is suitable for an intelligent risk assessment and early warning system for landslide disasters.

The invention provides a geological disaster intelligent early warning method based on fusion of a physical information neural network and space-air-ground monitoring, and the method comprises the following steps: S10, obtaining geological structure features of different depths under the ground of a city, and recognizing the spatial distribution and thickness of an underground abnormal body; acquiring a surface deformation time sequence, crack and landform information, a three-dimensional point cloud and a continuous vibration signal of a surface-shallow stratum; s20, constructing a three-dimensional twin substrate, performing space-time alignment and resampling on multi-source heterogeneous data, mapping the data to a three-dimensional grid of a unified coordinate system, and extracting and fusing geological disaster precursor features; s30, taking the generated fusion feature field as an input training neural network model, carrying out geological disaster forward prediction and parameter inversion, and outputting future stability probability distribution, a potential slip plane and key parameter evolution; and S40, adaptively adjusting a risk threshold and an early warning rule, constructing an incremental data set, and carrying out periodic incremental training and parameter optimization on the physical information neural network in the step S30.

The invention discloses a high geological disaster chain risk quantitative assessment method adapted to a high-cold and high-altitude environment, and relates to the technical field of geological disaster monitoring and prevention and control, and the method comprises the following steps: S100, building an irradiation and reflection combined observation layer, collecting the dynamic intensity of direct light and reflected light, generating an exposure time sequence fingerprint baseline, and obtaining an exposure time sequence fingerprint baseline; and determining a critical threshold value of the direct light intensity according to the base line. The method comprises the following steps: constructing an irradiation and reflection combined observation layer to extract light intensity characteristics, and generating an exposure time sequence fingerprint baseline; a trigger point boundary is extracted by adopting coherent phase decomposition and anti-fact playback, and a continuous track is reconstructed; the overexposure risk is predicted based on the time-frequency score, and spectrum gating and polarization rotation are linked to regulate and control the exposure rhythm; and reverse phase traction and phase conjugate intervention are implemented in a steady-state window, so that boundary information is maintained and the risk identification precision is improved.

The invention provides a monitoring and early warning method and system suitable for road slope geological disasters, and belongs to the field of image processing, and the method comprises the steps: obtaining the spatial coherence of each pixel point in a slope region according to the distribution condition of the structure texture change values of the pixel points in the slope region; clustering pixel points in the side slope area based on spatial cohesion, and screening out a suspected slope body displacement area in the side slope area; the motion consistency credibility of each suspected slope body displacement area is obtained by analyzing the difference condition of motion vectors between pixel points in the suspected slope body displacement areas; according to the motion consistency credibility of the slope body displacement area in the slope area, the slope geological disaster degree of the target monitoring road is obtained; disaster monitoring and early warning are carried out on the target monitoring road based on the slope geological disaster degree; according to the invention, the accuracy of monitoring and early warning of road slope geological disasters can be improved.

The invention relates to the technical field of computational mechanics and geological disaster simulation, and discloses a full-process numerical simulation method suitable for simulating continuous medium damage to discontinuous medium movement. The invention provides a novel chain-type disaster simulation method for solving the problems that calculation efficiency and large deformation precision are difficult to consider at the same time and the evolution process of a slope from a continuum to a fragmented body cannot be dynamically reflected in the prior art. The core of the method is that a dynamic criterion is set based on a unit real-time damage variable, a failed finite element (FEM) unit is adaptively converted into smoothed particle hydrodynamics (SPH) particles, and the physical state of the particles is accurately mapped; and a virtual particle coupling algorithm is adopted to process a two-domain interface, so that bidirectional transmission of mechanical parameters is realized. The method is executed circularly, the whole process of'continuous deformation-fragmentation flow 'of the side slope is simulated dynamically, high-precision integrated simulation of a'seismic source-propagation-response-movement' disaster chain in the same frame is achieved, and an efficient tool is provided for disaster risk assessment and prevention and control.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention belongs to the field of computer application technology and geological disaster prediction and early warning, and particularly discloses a landslide real-time prediction method and early warning method based on multi-modal information fusion, and the prediction method comprises the steps: constructing a landslide event text data set; a BERT-BiLSTM-CRF model is trained after time-space element labeling, and a structured historical landslide knowledge graph is constructed; using the landslide boundary training data set to train a U-Net + + image segmentation model to identify a landslide space boundary; taking time and space information as space-time anchor points, combining with a U-Net + + image segmentation model identification result, and identifying a landslide occurrence range and time on a remote sensing cloud platform; and extracting a multi-dimensional dynamic environment factor by combining a landslide occurrence range and time, constructing a time sequence feature sample set, and training a landslide susceptibility prediction model to realize dynamic prediction of landslide risks. According to the invention, timeliness and space precision are taken into consideration, and prediction accuracy and response capability of landslide disasters can be effectively improved.

The invention discloses a flood type landslide disaster monitoring and early warning method, and belongs to the technical field of geological disaster prediction. The method comprises the following steps: step 1, acquiring a flood type landslide disaster case in which a rainstorm event and a landslide event coincide in time and space, collecting landslide factor data, environment data, monitoring data and historical landslide data, and constructing a historical database; step 2, constructing and training a Bayesian network model based on a historical database; step 3, acquiring landslide monitoring data and preprocessing the landslide monitoring data; step 4, training an LSTM time sequence prediction model; 5, inputting the real-time monitoring data into the LSTM model, and predicting to obtain future landslide monitoring data; and step 6, inputting future monitoring data into the Bayesian network to obtain a slope instability probability based on a future trend. According to the invention, through organic fusion of the Bayesian network and the LSTM, dynamic prediction and real-time response of the landslide instability probability are realized, and timeliness, accuracy and robustness of early warning are significantly improved.

The invention belongs to the technical field of geological disaster risk assessment, and particularly relates to a slope stability grade identification method and system based on multi-modal deep learning. Comprising the following steps: acquiring a remote sensing image and corresponding parameter data, and respectively preprocessing the remote sensing image and the corresponding parameter data to obtain a high-dimensional image feature vector and a parameter feature vector; performing dynamic fusion of multi-modal information on the high-dimensional image feature vector and the parameter feature vector through a modal-level gating and fine-grained weighting mechanism to obtain a fusion feature; and inputting the fusion features into a preset classifier to obtain probability distribution of slope stability levels, and outputting the class with the maximum probability as a stability level identification result. According to the method, through combined modeling of the image modality and the parameter modality and introduction of an improved gating attention mechanism in a feature fusion stage, a reliable explanatory basis can be provided while high-precision prediction is ensured, so that the engineering applicability and the popularization value of the model are enhanced.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.