817 results about "Landslide" patented technology

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 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 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 relates to the technical field of geotechnical engineering disaster monitoring and early warning, and discloses a stratum rainfall seepage deformation coupling numerical simulation method and system and a storage medium, and the method comprises the steps: building a three-dimensional geologic model reflecting soft and hard rock interbed characteristics; converting the on-site rainfall data into hydraulic boundary conditions in real time; calculating a rock mass shear expansion volume change rate, determining a chemical damage acceleration factor, and updating a total damage variable; reconstructing a permeation tensor representing the dominant flow channel according to the space gradient of the damage variable; solving a fluid-solid coupling equation based on the permeability tensor, obtaining pore water pressure and calculating a displacement field; building a likelihood function by using field monitoring data to perform inversion correction on model parameters; and evaluating the stability based on the corrected calculation result and outputting an early warning signal. By constructing a mechanical coupling mechanism and a dominant flow channel model, precise simulation of a landslide evolution process in a complex geological environment is realized.

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 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 relates to a landslide identification method based on a channel and space mixed attention mechanism. The method comprises the following steps: acquiring remote sensing image data, and constructing a landslide identification model comprising an encoder, an improved jump connection and feature fusion module, a mixed attention module, a decoder and an output layer. The encoder performs layer-by-layer convolution down-sampling on the remote sensing image and outputs features of each layer; the improved module carries out convolution normalization, splicing and weighted fusion on the features to obtain fusion features; in the fusion stage, a mixed attention module is embedded, EMA generates channel weights and enhances features, PPA generates space weights and enhances features, and the EMA and the PPA are spliced and subjected to convolution activation to obtain final enhanced features. After receiving, the decoder performs up-sampling fusion, and an output layer obtains a landslide segmentation result through an activation function; and finally, the model is trained by using a preset loss function, landslide identification is realized, a landslide area can be accurately extracted, and the problem of class imbalance is relieved.

The invention discloses a landslide crack displacement self-adaptive monitoring system, and relates to the technical field of geological disaster monitoring, and the system comprises a multi-source data collection module which is responsible for collecting multi-dimensional data such as crack displacement; the data processing module carries out smoothing, normalization and other processing on the data; the intelligent early warning module calculates the landslide probability and divides four levels of early warning; the disaster deduction module calculates a landslide speed and an impact range; the linkage control and management module triggers an alarm and implements traffic control; according to the invention, comprehensive and accurate data is guaranteed through multi-source data acquisition and special equipment, data quality is optimized through data processing, and support is provided for early warning; the intelligent early warning module dynamically adjusts the reference, reduces false alarm and missing alarm, and adapts to different geological conditions; meanwhile, the disaster deduction and linkage control module cooperatively and quantitatively calculates the risk, automatically triggers alarm and is in butt joint with traffic control, a complete closed loop is formed, and the early warning efficiency and the disaster emergency response capability are improved.

The invention discloses a geological disaster real-time monitoring system based on the Internet of Things, and relates to the technical field of geological disaster intelligent early warning. Original data such as earth surface displacement, rainfall and earth sound signals are acquired through a multi-source sensing acquisition module; the edge intelligent processing module extracts disturbance characteristics and calculates disturbance entropy and an energy index; constructing a regional topological graph by using a graph neural network, and predicting a propagation path and a risk probability of an induction factor; fusing meteorological prediction and topographic factors to construct a multi-dimensional mapping model, and identifying a disaster development trend; judging whether a critical state is entered or not based on a causal reasoning map and triggering early warning; the sampling frequency and the communication priority of the sensor are dynamically adjusted in combination with the historical anomaly similarity, and resource optimization configuration is achieved; the system has the advantages of high recognition precision, high response speed, low energy consumption and high deployment adaptability, and is suitable for intelligent monitoring and early warning of geological disasters such as landslide and debris flow.

The invention provides a landslide probability prediction method and system, computer equipment and a storage medium, and belongs to the field of geological disaster early warning, and the method comprises the steps: obtaining the soil cohesion, internal friction angle and permeability coefficient of rock soil in a landslide region to be predicted; random field generation is carried out on the soil body cohesion, the internal friction angle and the permeability coefficient of the rock soil in the landslide area to be predicted, and soil body cohesion random field data, internal friction angle random field data and permeability coefficient random field data are obtained; carrying out discrete processing on the soil mass cohesion random field data, the internal friction angle random field data and the permeability coefficient random field data to obtain random variables of rock soil in the landslide area to be predicted; inputting the random variables of the rock and soil of the landslide area to be predicted into a pre-trained machine learning model, and calculating and outputting a plurality of slope safety coefficients; and determining the failure probability of the rock soil of the to-be-predicted landslide area according to the plurality of slope safety factor sets. According to the method, soil parameter spatial variability is considered, and the landslide probability prediction accuracy is improved.

The invention relates to the technical field of synthetic aperture radar interferometry (InSAR) and deep learning, and discloses a CNN-BiGRU landslide displacement prediction method based on InSAR deformation spatial-temporal feature fusion. The method comprises the following steps: firstly, acquiring landslide area time sequence deformation displacement by utilizing an SBAS-InSAR technology, decomposing the displacement into a trend term, a season term and a noise component by adopting variational mode decomposition (VMD), fitting and predicting the trend term and a segmented polynomial through RNN, converting a one-dimensional time sequence into a time-frequency image by the season term through a Gramb angle field (GADF), extracting spatial features by combining the CNN and extracting time sequence features by a BiGRU, and calculating the landslide area time sequence deformation displacement according to the time-frequency image. Multi-modal fusion prediction is realized; and finally combining the trend term and the season term to obtain total displacement prediction. Experiments prove that RMSE, MAE and Rindexes of the method in seasonal item and total displacement prediction are all superior to those of comparison models such as BiGRU and BiLSTM, and prediction precision and reliability are remarkably improved.

The invention discloses an intelligent landslide risk trend analysis method and device based on a trend prediction model, and relates to the technical field of landslide risk data analysis. According to the invention, the micro-core pile and the auxiliary sensor are adopted to collect multi-source time sequence monitoring data, intelligent data analysis processing is carried out based on built-in workflow of the micro-core pile, interference data optimization and data quality analysis are completed, and high reliability of data is ensured; intelligent analysis is carried out based on the trend prediction model, future landslide risks are predicted, and model prediction stability judgment and feedback adjustment are carried out; in combination with real-time monitoring data and known environmental factors, such as rainfall, risk judgment and early warning execution are performed. Through multi-level data optimization, stability feedback adjustment and comprehensive analysis of environmental factors, the precision and real-time performance of landslide risk prediction are effectively improved, the early warning capability is enhanced, and particularly, a more stable and reliable risk analysis result can be provided in a low electric quantity and external interference environment.

The invention discloses a ground surface settlement and ground fracture monitoring device and method based on distributed fiber sensing (DSS) and fiber bragg grating (FBG). The device comprises a GFRP composite material substrate which stretches across a ground fracture and is fixed on a stable stratum through an anchoring structure, a distributed sensing and temperature measuring optical cable which is pre-embedded in a groove of the substrate, an FBG displacement meter which is connected through a plug-and-play interface, a static force level gauge, an osmometer and other point type sensors, and the data processing unit has the functions of multi-source data acquisition, temperature compensation, data fusion and multistage early warning. According to the method, multi-parameter, distributed and high-precision monitoring of the ground fracture development state is achieved through the steps of field investigation, substrate installation, sensor deployment, system calibration, real-time monitoring and the like. The method has the advantages of high measurement precision, strong anti-interference capability, convenient installation, low cost, capability of predicting the crack propagation trend and the like, and is suitable for long-period safety monitoring of geological disaster frequent areas such as mining areas and landslide areas.

The invention discloses a rainstorm disaster early warning and alarming method, which comprises the following steps: carrying out multi-source data acquisition, and carrying out cleaning, standardization and fusion processing on the acquired multi-source data; based on the basic geographic information data and the historical disaster data, constructing a regional disaster bearing capability evaluation model, and respectively setting differentiated early warning thresholds for regions of different risk levels; based on the preprocessed multi-source data, constructing a rainstorm trend prediction model, and predicting rainfall intensity, cumulative rainfall and rainfall duration in a future preset time period; starting a corresponding multi-channel collaborative alarm mechanism according to the characteristics of the early warning level and the early warning area; in the early warning period, real-time monitoring data are continuously collected, and the rainstorm trend prediction result is updated. According to the method, the problems of inconsistency of multi-source data and low fusion precision are effectively solved, meanwhile, the situation that the prediction advance of secondary disasters such as excessive release of emergency resources and landslide is increased by 1-2 hours is avoided through the temporary high-risk labels, and comprehensive early warning of the secondary disasters is achieved.

The invention discloses a discrete element simulation method for slope unstable seepage, effectively solves the problem of nonlinear flow simulation distortion caused by excessive simplification of a traditional seepage model, and improves the calculation precision of transient processes such as rainstorm infiltration. The dynamic porosity feedback mechanism can reflect the influence of the internal structure change of the slope on the seepage field in real time, and more reliable pore water pressure prediction data is provided for landslide early warning. The establishment of a multi-scale coupling framework provides a new technical approach for unstable seepage analysis under complex geological conditions, a physical information neural network effectively avoids the overfitting risk of a pure data driven model, and an improved multi-objective optimization algorithm can quickly position an optimal parameter combination under complex constraint conditions. A dynamic updating mechanism can automatically adjust model parameters according to slope state changes, and the timeliness and accuracy of landslide early warning under the unstable seepage condition are remarkably improved.

The invention discloses a soft soil foundation embankment heightening and widening structure and a construction method, and belongs to the field of water conservancy projects. In order to solve the problems of uneven settlement, landslide instability and overlarge occupied area easily occurring when the soft soil foundation embankment is heightened and widened, the structure comprises precast concrete piles (top beams and ripraps are arranged on the tops of the piles) densely arranged at the slope toe outside the embankment, an inner side cantilever retaining wall (with drainage holes and a gravel inverted filter), geogrids laid in backfill soil in a layered mode, and CFG piles arranged on the lower portion in a plum blossom shape. The guardrail base and the flower bed are integrally poured with the retaining wall. The construction method comprises the steps of slide-resistant pile construction, earthwork excavation, CFG pile construction, retaining wall pouring, embankment body backfilling and embankment top road construction. Bidirectional control over settlement and horizontal displacement is achieved through cooperation of multiple structures, variable-stiffness leveling can be achieved by adjusting parameters of the CFG piles, and the variable-stiffness leveling structure has the advantages of skid resistance, stability, landscape optimization and land occupation saving and is suitable for soft soil foundation embankment upgrading and reinforcing projects.

The invention provides an intelligent landslide rainfall simulation system and method based on AI historical rainfall data driving, and belongs to the technical field of geological disaster monitoring and early warning. Dynamic coupling analysis and high-precision simulation of the rainfall process and slope stability are realized through automatic fusion of multi-source monitoring data and spatial-temporal feature extraction in combination with deep learning and physical mechanism modeling. The system can sense various types of data such as rainfall, soil moisture content and slope deformation in real time, intelligently identify landslide induced risks, output graded early warning information and support digital twinborn and adaptive risk optimization decisions. The landslide disaster risk assessment and early warning accuracy and response timeliness are remarkably improved, and the method has good intelligence, expansibility and practical application value and is widely applied to the fields of geological disaster prevention and control and engineering safety monitoring.

The invention relates to the technical field of slope disaster monitoring, in particular to a high and steep slope geological disaster monitoring method and system based on multi-source data fusion. The method comprises the following steps: tracking surface deformation through PS-InSAR time sequence analysis and atmospheric phase correction to obtain surface deformation characteristics; identifying the apparent diseases of the slope, and extracting spatial and temporal distribution characteristics of the apparent diseases of the slope; analyzing spatio-temporal evolution characteristics under geologic structure constraints by utilizing slope rock mass structure characteristics and the earth surface deformation characteristics; according to underground water level dynamic and potential sliding surface weakening features and the slope apparent disease spatial and temporal distribution features, extracting disaster-causing key factor features after cooperative correction; and combining the disaster-causing key factor characteristics, the true three-dimensional geological environment background characteristics and the spatio-temporal evolution characteristics to analyze the comprehensive early warning grade and the potential instability mode of the landslide. Scientific support is provided for high and steep slope geological disaster prevention and control, and engineering construction and operation safety can be guaranteed.

The invention belongs to the field of geological disaster early warning, and provides a landslide danger prediction system and method, and the method comprises the steps: carrying out the fusion and normalization processing of multi-source disaster-inducing factors, obtaining an impact factor matrix, and carrying out the weighted correction of the impact factor matrix; determining spatial association strength and semantic association degree between the nodes according to the association edges; adopting feature mapping, association weight calculation and information aggregation adaptive learning to obtain association strength and association features of the nodes; according to the association strength and the association features of the nodes, learning by adopting an association graph model to obtain a global prediction model, and optimizing the global prediction model; and predicting the target landslide area through the optimized global prediction model to obtain a prediction result, and carrying out danger grade division on the prediction result according to a preset probability threshold. The beneficial effect of the invention is that the precision of landslide risk prediction is improved.

The invention discloses a landslide modeling method and system based on unmanned aerial vehicle image and multi-modal feature fusion, and belongs to the technical field of landslide monitoring. The landslide modeling method comprises the following steps: acquiring an original image; detecting a bounding box of a landslide in the image, and extracting a feature vector of a landslide area in the bounding box and a central point of the bounding box; a sliding window is used for sliding on the image sequence, and center points contained in the window at each position form a point pool; collinear points in the point pool are detected, and center points corresponding to straight lines containing the most collinear points are determined as a point set; forming a candidate class by the center points with the similarity meeting a first preset condition; combining the candidate classes of which the similarity meets a second preset condition to form a final class; and drawing a mask for each final class and performing three-dimensional modeling. The problem that in the prior art, the landslide identification process needs to depend on manual intervention to define the specific landslide object of the geographic space corresponding to the landslide body in the image is solved.

The invention discloses a step-type landslide-oriented multi-source fusion deep learning deformation intelligent prediction method, and belongs to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, constructing and preprocessing a landslide data set of a research region; s2, decomposing displacement data based on a CEEMDAN algorithm; s3, screening key influence factors through grey correlation analysis; s4, constructing a TCN-Attention model to respectively predict a trend term and a period term; and S5, superposing prediction results and carrying out precision evaluation. According to the multi-source fusion deep learning deformation intelligent prediction method for the step-type landslide, the problems of modal aliasing and energy loss of a traditional method are reduced, and the signal decomposition effect is optimized; and the long-term deformation trend is accurately captured, the displacement abrupt change point is identified, and the prediction precision is improved.

The invention discloses a landslide maneuverability confirmation method and system based on an adaptive difference model, and relates to the technical field of data processing, and the method comprises the steps: obtaining a sub-slope region, and obtaining a vegetation coverage data sequence; obtaining a difference index, obtaining a first to-be-processed data sequence, obtaining a second to-be-processed data sequence, obtaining a difference sequence of the first to-be-processed data sequence, obtaining a first fluctuation index, and if the first fluctuation index exceeds a third preset threshold value, taking a sub-slope region corresponding to the first to-be-processed data sequence as a sliding source region boundary region; acquiring a difference sequence of the second to-be-processed data sequence, acquiring a second fluctuation index, and if the second fluctuation index exceeds a fourth preset threshold value, taking a sub-slope region corresponding to the second to-be-processed data sequence as a landslide region boundary region; and obtaining a target sliding source area, a target landslide area and an area proportion. The method has the advantages of self-adaptive difference distinguishing, stable and accurate boundary and dynamic quantization.

The invention discloses a remote sensing image landslide disaster detection and segmentation method and system, and a medium, and belongs to the technical field of disaster detection. Comprising the following steps that a remote sensing image is obtained and input into an improved YOLOv11n detection model, the model replaces a channel splicing and down-sampling module of a Neck part with a BiFPN module and an SCDown module respectively, and a landslide candidate box is output; taking the candidate frame as a prompt, inputting the candidate frame and an image into an improved SAM2 segmentation model, adding a refined convolution layer behind a mask decoder of the model, performing convolution and residual connection processing on an original mask logic value output by the decoder to realize boundary enhancement, and finally outputting an accurate landslide boundary mask; according to the invention, high-precision automatic detection and pixel-level segmentation of the landslide are realized.

The invention belongs to the field of natural disaster monitoring and early warning information processing, and discloses a landslide monitoring system and method based on multi-source data fusion. A data preprocessing module; a data processing and analyzing module; a dynamic early warning module and a remote visualization module; the method comprises the following steps: S1, installing equipment; s2, collecting data; s3, data preprocessing; s4, data fusion analysis; s5, risk judgment and early warning; s6, displaying a result; by adopting the technical scheme, the environmental factors can be monitored in real time, the environmental factors are fused into the basis of early warning judgment, the influence of the induction factors on the stability of the mountain can be sensed in advance, the comprehensiveness of landslide judgment is improved, and the accuracy and sensitivity of mountain early warning are improved; meanwhile, according to the technical scheme, the threshold value is optimized, so that the system judges whether the threshold value is a fixed threshold value or not, the threshold value can be dynamically adjusted according to the monitored environmental factors, and the early warning of the system on the landslide is more accurate.

The invention discloses a landslide early warning method based on multi-source fusion and an intelligent algorithm. The landslide early warning method comprises the following steps: acquiring meteorological and hydrological monitoring data, remote sensing image data and landslide displacement monitoring data of a target landslide area; performing data decomposition on the landslide displacement monitoring data to obtain a trend term and a periodic term; extracting ground monitoring data composed of a plurality of factors and response time of the plurality of factors from the meteorological and hydrological monitoring data; performing fusion processing on the remote sensing image data and the ground monitoring data to obtain key disaster-causing factors; constructing a prediction model used for predicting the landslide displacement rate; triggering an early warning signal based on the predicted value of the landslide displacement rate; according to the method, the landslide multi-scale response rule and the lag effect can be identified more accurately, and the dynamic prediction and early warning capability of the landslide is improved.

The invention discloses a geological feature-based regional landslide hidden danger automatic identification system and method, and relates to the technical field of intelligent sensors. The geological feature-based regional landslide hidden danger automatic identification method comprises the steps of S1, acquiring and preprocessing multi-source monitoring data and geological constraint data, and constructing a landslide monitoring database; s2, based on the weighted sensitivity and residual accumulation of the multi-source monitoring data, analyzing the credibility of the physical property parameters of the slip band; s3, spatial coupling analysis is carried out through multi-source deformation and geometric parameter data, and sliding band form recognition and connectivity adjustment operation are dynamically optimized; s4, dynamic instability analysis is carried out through deformation rate and sliding surface geometric fusion data, and the slip band instability trend is quantified; and S5, performing comprehensive judgment on the stable state of the monitoring unit by fusing the three-dimensional sliding surface probability body and the spatio-temporal evolution index. The problems of inaccurate slip zone parameter inversion, low slip surface judgment precision and early warning lag in regional landslide hidden danger identification are solved.

The invention discloses a photoelectric integrated landslide slip monitoring system and method. The system comprises a photoelectric integrated cable used for being lowered into a monitoring drill hole drilled in a landslide mass, and a heavy hammer facilitating the photoelectric integrated cable to be kept vertical in the lowering process is hung at the bottom of the photoelectric integrated cable. The photoelectric integrated cable comprises a copper inner conductor, an insulating layer, an aluminum tube and an outer protective layer which are coaxially arranged from inside to outside and form a coaxial cable, a strain optical fiber and a temperature measuring optical fiber are arranged in the insulating layer in parallel and separated from the inner conductor, the inner conductor, the strain optical fiber and the temperature measuring optical fiber are fixed through insulating filler filled in the insulating layer, the aluminum tube is wrapped outside the insulating layer, and the outer protective layer is wrapped outside the aluminum tube. The aluminum tube is wrapped by an outer protective layer, and cables respectively connected with the inner conductor, the strain optical fiber and the temperature measurement optical fiber are led out from the top of the photoelectric integrated cable and are respectively connected with a TDR channel, an optical fiber strain channel and an optical fiber temperature measurement channel of the multi-channel demodulator. According to the invention, the position and displacement angle information of slippage deformation of the landslide can be accurately obtained, and the method is rapid and accurate.

The invention discloses a multi-parameter fusion slope early warning method and system, and belongs to the technical field of slope safety monitoring and early warning. The method comprises the following steps: S1, risk analysis: collecting multi-source monitoring data, establishing a slope engineering safety risk evaluation system based on a fuzzy comprehensive evaluation-analytic hierarchy process, and performing slope safety risk evaluation; s2, risk management and control: analyzing a landslide form and an identification basis, selecting a landslide criterion, designing a slope early warning standard, constructing a comprehensive early warning system of a multivariate information fusion criterion, and performing slope safety risk management and control; and S3, safety monitoring: developing a high-risk slope engineering automation safety monitoring management platform based on the Internet of Things technology, realizing digital management of slope engineering monitoring and early warning, and providing technical support for highway operation management.

The invention discloses a multi-source monitoring and early warning method for slope stability of a strip mine dump. The method comprises the steps that multi-source monitoring data of a target slope are acquired, and the multi-source monitoring data comprise radar monitoring data, GNSS monitoring data and deep displacement sensor data; a two-dimensional model of the central axis of the maximum deformation area of the target slope is established based on the numerical simulation technology, the model is generated according to slope design parameters and rock stratum physical and mechanical parameters, and the deformation rate and the accumulated displacement early warning threshold value are obtained by simulating the landslide process; and according to the slope surface deformation rate and the deep accumulated displacement, landslide risk early-warning grades are divided, and the surface early-warning grade and the deep early-warning grade are fused to output a final landslide risk early-warning result. The method is comprehensive and accurate in monitoring and early warning of the slope stability of the strip mine dumping site, and is suitable for monitoring and evolution of the slope stability of the strip mine dumping site.

The invention discloses a side slope three-dimensional deformation monitoring and early warning method and system based on a cascade single-line multipoint crack meter. The method and system are suitable for deformation monitoring and landslide state forecasting of a side slope area without macroscopic cracks. The method comprises the steps that a monitoring main line is planned in a potential slip area in the main slip direction, intelligent fusion nodes are arranged on the main line, multi-direction monitoring branch lines are led out, and a tree-shaped monitoring network is formed; two ends of the main line are anchored in a stable area, and tail ends of the branch lines are fixed on a slope; the node devices synchronously collect relative displacement and three-dimensional attitude data in the main line direction and the branch line direction, a three-dimensional deformation field is built in combination with an early warning model, and landslide state study and judgment and threat range prediction are achieved. The system achieves multi-point and multi-direction displacement monitoring through a tree-shaped structure, has the advantages of being wide in coverage, low in cost, flexible in arrangement and the like, and supports early-stage automatic early warning of slope disasters.