330 results about "Mudflow" patented technology

The invention discloses a grating dam safety evaluation and design method and structure based on debris flow blocking degree. Wherein the safety assessment method comprises the following steps: S100, calculating the safety of the debris flow on the basis of the solid matter concentration of a debris flow susceptible area, the particle size value corresponding to the accumulated content of 95% in a full particle size grading curve, the channel average gradient, the channel average width and the primary debris flow scale in the section range of the grating dam, the minimum particle size value of the maximum boulder in the drainage basin and the opening width of the grating dam; calculating a critical blocking judgment coefficient according to the correlation value of the height of the grating dam, the angle of the wedge-shaped structure and 60 degrees and the correlation value of the total amount of debris flow solid substances and the reservoir capacity of the wedge-shaped beam type grating dam; and S200, evaluating the safety of the grating dam based on the critical blocking discrimination coefficient. The evaluation and design method and structure incorporate the relative relation between the debris flow scale and the dam body storage capacity, meanwhile, the influence on active structure parameters such as wedge-shaped flow guide is quantified, and the blocking state prediction accuracy of the grating dam in actual operation is improved to a certain degree.

A method for reducing the danger of a dammed lake formed by a debris flow blocking a river, comprising: first predicting the accumulation range of a river-blocking debris dam during occurrence of a debris flow, dividing a river boundary into multiple areas on the basis of the elevation, and then performing roughness reduction treatment on the areas of the river boundary during a dry season. The present method reduces the roughness of a river boundary within the accumulation range of a river-blocking debris dam, so as to reduce the opposite bank dam height of the river-blocking debris dam and the storage capacity of the dammed lake, thereby achieving the purpose of reducing the danger of dammed lakes formed by debris flows and reducing the impact of debris flow disasters.

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 technical field of geological disaster monitoring and early warning, and relates to a rainfall type landslide-debris flow chain disaster intelligent dynamic monitoring and early warning method, which comprises the following steps: data acquisition: generating multi-source data including an optical image, an SAR image, an unmanned aerial vehicle image and ground data; preprocessing the multi-source data; performing space-time registration, data format and organization on the multi-source data after data processing; key characteristic parameters are calculated, and data fusion is carried out; constructing a multi-dimensional feature vector, and generating a multi-dimensional feature image; constructing a risk assessment model by using a deep learning model, and performing real-time prediction; dynamically determining the total risk level of each area according to a preset comprehensive research and judgment rule by integrating the real-time prediction result; and when the total risk level reaches a'high risk 'or'extremely high risk' threshold value, generating early warning information, and performing early warning release. According to the invention, comprehensive, whole-process three-dimensional perception, dynamic and self-adaptive intelligent evaluation and disaster prediction are realized.

The invention relates to a geological disaster change detection method and system based on an improved twin U-Net and a central surrounding double-flow network. According to the method, an improved twin U-Net network is constructed as a feature extraction trunk, and a deformable convolution module is integrated in an encoder to adaptively adjust a receptive field; a central surrounding double-flow network is embedded in a decoding path, detail features are extracted through a central flow path, and a global context is obtained through a surrounding flow path; a feature fusion module is designed to realize double-path feature deep fusion, and a gating attention unit is adopted to calibrate feature response; introducing a contrast feature learning mechanism to reinforce feature space clustering characteristics; multi-modal degradation enhancement training is implemented to improve the robustness of the model; and an objective function containing difference perception loss and feature comparison loss is minimized through end-to-end joint optimization. The method can accurately identify geological disaster change areas such as landslide, debris flow and land subsidence, and has the advantages of high detection precision and strong anti-interference capability.

The invention discloses a data processing method for geological disasters, and particularly relates to the technical field of geological disaster monitoring and data processing. Collecting and normalizing multi-source monitoring data, and constructing a unified time sequence fusion vector; identifying a weak abnormal signal based on the disturbance change rate and a sensitivity model; high-disturbance feature points are extracted through sliding window clustering, and a dynamic risk factor matrix is constructed in combination with a remote sensing image; extracting dominant risk factor feature vectors by using principal component analysis, inputting the dominant risk factor feature vectors into a disaster evolution simulation model, and predicting a future high-risk area in combination with a historical path library; and finally, potential micro-disaster trigger points and risk levels are output, and graded early warning processing is realized. The system has the advantages of high disturbance identification precision, strong simulation prediction capability, automatic early warning response and the like, and is suitable for an intelligent early warning system for sudden geological disasters such as landslide and debris flow.

The invention discloses a geological risk identification method based on multi-source data fusion, belongs to the technical field of geological risk monitoring and analysis, and can solve the problems that the existing address risk identification mode is single, the identification precision is poor, and the identification efficiency is low due to the lack of dynamic monitoring means. The method comprises the following steps: S1, collecting geological data of a target area, and constructing a three-dimensional geological risk dynamic model according to the geological data; s2, determining risk index calculation data of the target area according to debris flow source data and rainfall data of the target area and the three-dimensional geological risk dynamic model; s3, determining a risk evaluation coefficient of the target area according to the risk index calculation data and risk index reference data of the target area, and generating a dynamic risk partition map according to the risk evaluation coefficient; and S4, generating early warning information of the target area according to the dynamic risk partition map, and performing geological risk identification according to the early warning information. The method is used for geological risk identification.

The invention discloses a landslide-debris flow disaster chain assessment method based on a graph neural network, and the method comprises the steps: generating a composite ground feature unit through the watershed segmentation of a composite curvature field and hydrological analysis, abstracting the composite ground feature unit as a geographic node, optimizing a multi-source environment factor through mutual information screening, constructing a directed weighted graph in combination with spatial similarity, and carrying out the reconstruction of a landslide-debris flow disaster chain. An improved GraphSAGE model which introduces neighbor weight, self-loop and residual error information and cancels random sampling is adopted, a sample set adaptive to a less-data area is constructed through a unit splitting-feature matching-terrain fitting mechanism, and model training and risk prediction are completed. The method solves the problems of single disaster assessment, strong sample dependence and high missed judgment rate in the traditional technology, has excellent performance indexes, effectively reduces the missed judgment risk of a high-risk area, and provides accurate and efficient technical support for disaster chain risk assessment of a small watershed in a small-data mountainous area.

The invention discloses a debris flow source identification method and system, and the method comprises the steps: obtaining at least one second source attribute feature of to-be-identified debris flow source information, building the feature description of the to-be-identified debris flow source information on a plurality of source attribute data volumes, and carrying out the recognition of the to-be-identified debris flow source information according to the at least one second source attribute feature. And determining directory information of each byte in the debris flow source information to be identified. According to the embodiment of the invention, the cleaning unit adapted to different object source attribute data volumes is dynamically generated according to the to-be-identified debris flow object source information, and the weight of the cleaning unit is dynamically generated according to the to-be-identified debris flow object source information, so that the adaptability is better; therefore, the feature description of the established debris flow source information to be identified on a plurality of source attribute data volumes is more accurate, and the accuracy of the directory information of each byte in the determined debris flow source information to be identified is higher.

The invention relates to the technical field of natural disaster risk assessment, in particular to a multi-disaster disaster-inducing factor internal correlation analysis method, which comprises four steps of data preprocessing, Bayesian network correlation model construction, space-time dynamic evolution analysis and risk early warning threshold model construction. And deep coupling analysis of disaster-inducing factors of multiple disasters such as mountain torrent-debris flow and the like is realized. The method comprises the following steps: firstly, collecting 17 disaster-inducing factor data such as terrain and rainfall, performing dimensionality reduction through kernel principal component analysis, and calculating a dynamic weight by adopting an entropy evaluation method; then constructing a Bayesian network model to quantify conditional probability association; introducing a space-time attention mechanism to optimize a multi-scale coupling weight; and finally, establishing a coupling risk early warning threshold value and outputting a relevance intensity matrix. According to the method, the problem that a traditional single-disaster analysis method cannot capture the space-time linkage effect of disaster-inducing factors is solved, the risk early warning accuracy of multiple disasters is improved, and scientific decision support is provided for regional disaster prevention and reduction.

The invention discloses an extreme rainfall induced geological disaster chain risk evaluation method, and relates to the technical field of geological disaster risk evaluation. Comprising the steps of defining extreme rainfall induced geological disaster chain danger, selecting susceptibility evaluation indexes and grading, constructing a geological disaster susceptibility evaluation model based on a multi-element machine learning model, constructing a geological disaster susceptibility evaluation model based on a multi-element machine learning integrated model, and evaluating the susceptibility of an extreme rainfall induced geological disaster chain. And assessing the risk of the geological disaster chain induced by the extreme rainfall. According to the method, based on an optimal model, the comprehensive susceptibility and danger of extreme rainfall-collapse, extreme rainfall-landslide, extreme rainfall-debris flow and extreme rainfall induced geological disaster chains are evaluated respectively; the comprehensive risk assessment method for the extreme rainfall-collapse chain, the extreme rainfall-landslide chain, the extreme rainfall-debris flow chain and the extreme rainfall induced geological disaster chain is constructed, and a new thought is provided for risk assessment research of the extreme rainfall induced geological disaster chain.

The invention provides a debris flow risk assessment method and system based on debris flow parameter probability inversion calibration, and relates to the technical field of geological disaster risk assessment. According to the technical scheme provided by the embodiment of the invention, an effective means is provided for scientifically identifying debris flow parameters, and the method is particularly suitable for setting single gullies which are limited in detailed observation and cannot directly measure the parameters. A traditional method depends on statistical parameters or deterministic back analysis, the former introduces errors due to poor regional adaptability, and the latter has the problem of non-uniqueness of parameters; according to the scheme, the parameter posteriori distribution is updated by combining the Bayesian framework with historical observation data (debris flow influence areas), so that the standard deviation of key parameters is generally reduced. In the embodiment of the invention, the influence area is taken as core observation information, the practical limitation that the flow depth and the flow velocity are difficult to observe in real time in the debris flow event is avoided, the observation information can be obtained through post-event field investigation or satellite images, and the scheme is high in practicability.

The invention provides a geological disaster identification method based on seismic wave dynamics and kinematics characteristics, the geological disaster is mountain torrent or debris flow, and the method comprises the following steps: collecting seismic signals in a geological disaster potential risk area; the seismic signals are preprocessed; extracting early warning parameters from the pre-processed seismic signal, wherein the early warning parameters comprise a long and short time window energy ratio, a time-frequency spectrum parameter, a power spectrum density and a seismic signal duration; establishing an earthquake prediction model of the single-measuring-point geological disaster according to the extracted early warning parameters; fusing the earthquake prediction models of the geological disasters at multiple measuring points, and judging whether the earthquake signals are earthquake signals triggered by the geological disasters or not; and when it is judged that the seismic signal is triggered by the geological disaster flow, real-time early warning information issuing is carried out on the downstream risk area. The method can be applied to monitoring and early warning of geological disasters caused by artificial or natural activities such as mountain torrents, dam break, landslides, collapse and the like, and has wide engineering technology application and scientific research prospects.

The invention relates to a debris flow gully slope protection structure, and relates to the technical field of water conservancy projects. Multi-stage counter-slope terraces are arranged on the slope surface of the debris flow gully slope from the bottom end to the top end, each terrace structure comprises a terrace surface, ridges and a terrace ditch which are arranged upwards at intervals along a contour line, each layer of counter-slope terrace is separated by using a mortar rubble retaining wall as the ridge, the terrace surface is covered with soil by using a slope native soil layer, and the terrace ditch is connected with the ridge of the next stage of terrace by adopting a mortar rubble material. The field surface has a reverse slope inclination angle of 5-10 degrees, and crops can be planted on the field surface. The field ditches are communicated with main drainage ditches on the two sides of the terrace along the contour line downwards, and metal gates are arranged at the joints to control water flow, are closed when water storage is needed and are opened when water drainage is needed. The structure can effectively reduce the material, construction and maintenance cost, has high debris flow resistance, can stabilize the debris flow channel slope, reduces the water and soil loss of the slope, plays a role in terrace water storage and drainage, and further protects and promotes the growth of crops.

The invention discloses a plateau structure active area debris flow development characteristic and disaster-causing mode identification method and system, and relates to the technical field of debris flow prevention and control engineering, and the method comprises the steps: S10, determining the development characteristic parameters of the plateau structure active area debris flow based on remote sensing and field investigation; s20, determining a debris flow formation mechanism, and calculating a debris flow influence range; and S30, analyzing the relationship between the debris flow influence range and the position of the potential influence area so as to judge the debris flow disaster-causing mode. According to the method, on the basis of obtaining the basic parameters of the debris flow, the development characteristics are identified, then the scale of the debris flow is calculated, the debris flow disaster-causing modes are determined, and the selectable debris flow disaster-causing modes comprise impact disaster-causing modes, silting disaster-causing modes and erosion disaster-causing modes. The method has a good effect of judging the debris flow disaster-causing mode of the plateau structure active area, can identify the disaster-causing mode of the glacier and glacier lake debris flow in the plateau area, and fills the blank in the debris flow disaster-causing mode identification technology.

The invention discloses a flexible blocking solid source early warning structure suitable for narrow and steep debris flow prevention and control, and relates to the technical field of debris flow prevention and control. The structure is selectively arranged in a circulation area formed by debris flow, is reasonably combined and arranged from upstream to downstream according to the channel length and the longitudinal slope, and at least comprises four stages of flexible carbon fiber grating net structures and a counterfort type rubber dam. According to the flexible blocking and source fixing early warning structure suitable for narrow and steep debris flow prevention and control, the blocking and source fixing effects are achieved, early warning can be conducted in advance, the structure can bear high-strength impact of narrow and steep debris flow, impact loads are dispersed, damage of debris flow impact force to the blocking structure is greatly reduced, and the flexible blocking and source fixing early warning structure is light, green and convenient to use. According to the transportation and installation scheme, the construction period is short, the difficulty is low, debris flow disasters are accurately predicted, the problem that a steep debris flow prevention and control structure is prone to failure is solved, and the investment benefits of prevention and control measures are exerted to the maximum extent.

The invention relates to the field of debris flow disaster prevention and control, in particular to a method for predicting the flow of large-particle blocking and collapsing debris flow at the sudden shrinkage position of a channel, which comprises the following steps: determining the stress state information of large-particle materials according to the movement process of the large-particle materials in the channel along with debris flow; according to the stress state information and the stress balance condition, a flow prediction model of the large-particle blocking and collapsing debris flow at the channel sudden shrinkage position is constructed; building an experimental device, and placing a simulated large-particle material in the experimental device; obtaining debris flow characteristic parameters and large particle material motion characteristic parameters through an experimental device; and measuring the real channel to obtain a large particle material form parameter and a channel form parameter, so as to obtain the flow of the large particle blocking and collapsing debris flow at the channel sudden shrinkage part of the real channel. The experiment device is built to simulate the debris flow process for multiple times to obtain corresponding parameters, the debris flow is calculated in combination with the debris flow prediction model, and the debris flow prediction reliability is improved.

The invention discloses a mine underground debris flow risk quantitative analysis and evaluation method, and belongs to the technical field of metal mine disaster prevention and control. Comprising the following steps: calculating 24-hour rainfall under different rainstorm occurrence frequencies; determining a sand production critical hydraulic gradient and a sand inrush critical hydraulic gradient, and calculating a sand production water level height and a sand inrush water level height; the rainstorm infiltration amount of the upper boundary of the quaternary gravel bed under the most unfavorable working condition is calculated, and the sand production rainstorm leakage amount and the sand inrush rainstorm leakage amount of the lower boundary of the quaternary gravel bed are calculated; the leakage amount and the imbibition amount are compared, the sand production or sand inrush risk is preliminarily judged, the time needed for lifting the water level to the sand production water level height or the sand inrush water level height is calculated, and finally the sand production or sand inrush risk is judged; and analyzing the sand production and sand inrush risks under the condition of short-time heavy rainfall by adopting the same method. According to the method, underground debris flow occurrence risks under different rainfall intensities are quantitatively analyzed, a disaster triggering mechanism can be determined, critical criteria for formation of mine underground debris flow are given, and a prevention and treatment decision basis is provided.

The invention provides a debris flow disaster early-warning method based on image recognition, and the method comprises the steps: firstly obtaining two groups of comparison video stream data of a multi-view camera in different time periods, carrying out the mountain feature registration of the two groups of comparison video stream data to guarantee the consistency of space coordinates, filtering and removing invalid interference information through an interference region mask, and obtaining a pre-warning result; and then determining a mountain sliding association difference pixel point set according to the filtered video stream data, and finally constructing a three-dimensional mountain sliding monitoring model in combination with the set and the registered video stream data, thereby determining a target monitoring area of the detection equipment and carrying out monitoring and early warning on the target monitoring area. Spatial position deviation of video stream data in different time periods is eliminated through mountain feature registration, various interference factors in the natural environment are effectively eliminated in combination with interference area mask filtering, mountain sliding can be accurately recognized, the debris flow disaster early warning precision is remarkably improved, the early warning signal false alarm condition is reduced, and the early warning efficiency is improved. And potential safety hazards caused by false alarms are reduced.

The invention discloses a debris flow prediction method based on multi-source remote sensing, and the method comprises the following steps: 1, data acquisition and preprocessing: obtaining target region data through satellite remote sensing, and eliminating terrain shadow interference; step 2, feature extraction: extracting vegetation coverage and surface temperature from the optical image, extracting deformation magnitude and time sequence from InSAR data, and extracting gradient, slope direction, curvature and terrain humidity index from DEM terrain data; 3, performing multi-dimensional feature fusion, and constructing a multi-dimensional feature vector; 4, model construction: establishing a debris flow susceptible area identification model, and fusing the vegetation-terrain coupling characteristics of the optical image, the deformation dynamic characteristics of the InSAR and the terrain stability characteristics of the terrain data; 5, carrying out susceptibility partitioning and early warning, dividing risk grades based on probability values output by the model, and triggering graded early warning responses; according to the method, the multi-dimensional feature space is constructed through the collaborative interaction of the multi-source remote sensing data and the fused data.

The invention relates to the technical field of geological disaster monitoring, in particular to a rock collapse accumulation body debris flow tendency intelligent evaluation system and method based on multi-parameter coupling. The method comprises the following steps: acquiring geological structure data of a rock collapse accumulation body; performing potential unconsolidated formation analysis according to the geological structure data to obtain potential unconsolidated formation data; performing rainfall infiltration analysis according to the potential unconsolidated formation data to obtain infiltration rate data; determining the water content state of the accumulation body according to the infiltration rate data; performing stability analysis according to the water-containing state of the accumulation body to obtain accumulation body stability data; identifying a potential sliding section based on the accumulation body stability data; detecting flow energy according to the potential sliding section to obtain sliding power data; and performing hydrological confluence analysis according to the geological structure data to obtain hydrological confluence data. According to the method, the accuracy of identifying and evaluating the debris flow tendency of the rock collapse accumulation body is improved based on a geological disaster monitoring technology.

The invention discloses an elasticity evaluation and improvement method for a power transmission system under the action of debris flow, is used for solving the technical problem of single fault rate consideration factor in the prior art, and belongs to the technical field of evaluation of anti-disaster capability and recovery capability of the power transmission system under extreme natural disasters (such as debris flow). The method comprises the following specific steps: step 1, constructing a debris flow model considering an impact force attenuation effect; s11, describing the influence area of the debris flow by using the three parameters; s12, carrying out stress analysis on the tower impacted by the debris flow; 2, constructing a fault model considering the accumulated impact process of debris flow on the power transmission element; s21, calculating a tower failure probability; s22, calculating the failure probability of the power transmission line; s23, calculating the overall failure probability of the power transmission line; 3, sampling at a constant time interval delta t based on the solved failure probability of the power transmission line within the debris flow duration delta T; 4, drawing an expected power supply power curve of the power transmission system; and 5, calculating an elastic index.

The invention provides a semi-analytical MPM-DEM coupling numerical simulation method for interaction of debris flow and a structure, and relates to the field of geotechnical engineering disaster simulation. Taking the fine-grain slurry as a continuous medium, and discretizing by adopting MPM; meanwhile, spherical particles and clustered particles serve as solid phases, and DEM modeling is adopted; structures and boundaries are embedded into the computational domain in the form of rigid walls. Through grid-particle information exchange and two-way transmission of particle-grid coupling force, synchronous solution of fluid-solid multiphase coupling is realized. The fluid-solid contact force adopts a semi-analysis mode; the DEM particles and the DEM particle-wall contact are accurately calculated on the basis of a Hertz-Mindlin model, and the DEM particle-wall contact is accurately calculated on the basis of a Hertz-Mindlin model. According to the method, collaborative collision, blockage and impact mechanisms of block stones and floating woods in debris flow are truly represented, and the three-dimensional large-scale situation of million-level fluid mass points and million-level particles is simulated. The simulation result can output key indexes such as the stress time history, the impact peak value and the blockage rate of the structure, and a reliable numerical basis is provided for debris flow protection.

A debris flow discharge channel, comprising: a main channel through which the debris flow passes; a separation screen detachably connected to the main channel, the separation screen separating the main channel into an upper discharge channel and a lower discharge channel; a first accumulation area in communication with the upper discharge channel; a buffer channel in communication with the lower discharge channel, the buffer channel being provided with transverse energy dissipation ridges and longitudinal flow guide ridges; and a second accumulation area provided behind the buffer channel. The separation screen separates coarse and fine particles, which, in terms of service life, prevents the downstream discharge channel from being damaged by the huge impact force of coarse particles or being blocked by the accumulation, effectively prolongs the service life of the discharge channel, and, in terms of later maintenance, the separation of coarse and fine particles facilitates the maintenance and repair of the downstream main channel, thereby effectively reducing the maintenance cost in the later period.

This invention belongs to the field of flash flood and debris flow disaster prevention and mitigation, and provides a method for simulating flash flood and debris flow processes based on rheological evolution characteristics. The method includes the following steps: establishing a generalized numerical model of the target channel; using a CFD-DEM coupled model to simulate the transport process of sediment particles in the generalized numerical model under the action of water flow or debris flow slurry; and, based on the semi-empirical relationship between the drag coefficient and Reynolds number of sediment particles carried by flash floods and debris flows, derived in this invention, realizing real-time adjustment of rheological parameters during the simulated flash flood and debris flow transport process through the bidirectional coupling mechanism of the CFD-DEM coupled model. This invention's method can more realistically simulate the spatial distribution of sediment particles during flash flood and debris flow transport, increasing the accuracy of flash flood and debris flow process simulation, and solving the problem that existing technologies struggle to simulate the rheological evolution phenomenon of flash floods and debris flows caused by changes in volumetric sediment content.

This invention provides a multi-model fusion method, device, electronic device, and system for debris flow early warning. The method includes: collecting geographical and geological data, real-time rainfall, runoff, and auxiliary monitoring data; fusing a shallow flow numerical model, a moving least squares machine learning model, and a generalized regression neural network model, and obtaining a comprehensive runoff prediction through weighted least squares calculation; constructing critical runoff indicators based on the safety requirements of disaster-bearing bodies such as roadbeds, retaining walls, and culverts, and inversely calculating critical rainfall thresholds, then making differentiated adjustments based on the average gradient of the gully; generating a debris flow early warning report by comparing real-time predictions with thresholds, achieving final graded and zoned early warning for the warning area. This invention improves the accuracy and engineering relevance of early warnings by integrating physical mechanisms and data patterns, achieving refined early warning from "whether it will occur" to "when, where, and what the impact will be," while reducing the error of a single model and adapting to different terrains and disaster-bearing bodies along highways.

The invention provides a wide valley U-shaped channel mountain torrent debris flow prevention and control method based on a mountain torrent sediment transport rule, and the method comprises the steps: determining a formation region, a circulation region and an accumulation region of a large watershed debris flow channel, and determining debris flow channel parameters; determining the characteristics of the mud mark section; calculating debris flow characteristic parameters; a target bedrock section is selected, and the design height of a target dam body is calculated according to section characteristics of the target bedrock section, debris flow channel parameters, mud mark section characteristics and debris flow characteristic parameters; according to the method, an open type grating dam is arranged at a section where flood is often encountered, rolling type earth and rockfill dams are arranged at other positions of the section, and design construction is carried out according to a target design specification and the design height of a target dam body, so that the problem that when debris flow occurs, an existing prevention and treatment project loses efficacy due to deposition of silt carried by long-term erosion of mountain torrents is solved; and the service life of the prevention and treatment project in the drainage basin is greatly prolonged, and the expected effect can be achieved when debris flow occurs.

The present application relates to the field of highway engineering safety monitoring, in particular to a highway slope stability image monitoring and risk early warning system, the system comprises an image acquisition module, a slope parameterization representation module, a multi-scale curvature flow analysis module, a Riemann manifold learning module and a risk early warning module, the system regards the slope as a Riemann manifold, extracts differential features by calculating the metric tensor and the curvature tensor of the surface; the multi-scale representation of the slope surface is constructed by applying the curvature flow theory, and the deformation characteristics under different scales are identified; senior features are extracted by using Riemann manifold learning and geodesic convolution network, and precursors of geological disasters such as landslides, collapses and debris flows are accurately identified, the system has an adaptive monitoring frequency adjustment function, automatically switches the monitoring mode according to the risk level, greatly reduces the slope disaster risk, and provides a strong guarantee for highway safety operation.

The present application relates to a kind of mudslide dam based on the quick design method of motion energy analysis, comprising the following steps: step S1: according to the situation of geological exploration, preliminary selection n dam arrangement position, corresponding n kinds of working conditions, subsequent analysis is carried out according to each working condition;Step S2: the motion process of mudslide is simulated using SPH method, flexible disaster mitigation measures are applied, and the retarding effect of dam on mudslide is simulated using virtual wall particle;Step S3: calculate the impact characteristic parameter of mudslide, including the impact time Δ t Of leading head, impact average speed, the length L Of leading head, the volume Vol head Of leading head, impact load size Load impact ;Step S4: calculate the design characteristic parameter of dam, obtain the cross section design characteristic parameter of dam under different downstream slope, including dam height H , dam top length L T , dam top width W T , dam bottom length L B And dam bottom width W B This method can realize the quick design of dam characteristic size.

The utility model provides a monitoring and early warning device for a debris flow gully in a canyon fall section, and relates to the technical field of natural disaster early warning. The device comprises a supporting column, a connecting frame, a sliding rail, a debris flow monitoring module and a rainfall monitoring module. Wherein the supporting columns are embedded and fixed on the two sides of a debris flow gully; the connecting frame is fixedly connected to the upper end of the supporting column. The sliding rail is fixed to the lower end of the middle of the connecting frame and extends in the same direction as the debris flow gully. The debris flow monitoring module is slidably connected to the sliding rail and can follow the debris flow state in the debris flow gully when the debris flow disaster occurs. And the rainfall monitoring module is fixed at the upstream position of the debris flow gully. According to the scheme, the debris flow monitoring module can move along the channel for monitoring through the sliding track, dynamic data collection is achieved in combination with the rainfall monitoring module, the problem that traditional fixed-point monitoring cannot adapt to complex canyon terrains is solved, and the accuracy and timeliness of early warning are improved.