253 results about "Geological disaster" patented technology

The present application relates to the technical field of geological disaster risk assessment, and discloses a landslide-prone prediction method based on multi-scale geographic weighted regression and spatial heterogeneity partitioning, which solves the problems of low local accuracy, poor area efficiency and insufficient prediction stability caused by single spatial scale, uniform modeling and single evaluation system in traditional landslide prediction models. The present application organically combines multi-scale geographic weighted regression with spatial heterogeneity partitioning, first adaptively assigns a dedicated optimal bandwidth to each disaster-inducing factor through a multi-scale regression model to accurately depict the spatial non-stationary characteristics of landslide disaster-inducing relationships, then reconstructs the discrete regression coefficients into a spatial continuous surface with the help of Kriging interpolation, dynamically extracts the dominant factors with the optimal discrete and natural discontinuity method to realize the heterogeneity partitioning of the study area, takes each partition as an independent unit to carry out landslide-prone deduction, and constructs a multi-dimensional comprehensive evaluation system from the dimensions of prediction accuracy, area rationality and uncertainty robustness.

This invention relates to the field of geological disaster monitoring and distance sensor technology, specifically disclosing a real-time monitoring and early warning method for slope deformation based on interferometric radar images. The method includes: acquiring multi-time-series interferometric radar images and extracting high-precision surface deformation field sequences; constructing a physical information neural network model integrating the constitutive relations of rock and soil mechanics; using the deformation field as input to invert the internal stress, pore water pressure, and potential sliding surface of the slope in real time; determining the instability risk level based on preset thresholds and triggering a three-level early warning response mechanism to generate a visualized early warning report. This invention achieves highly timely and interpretable early warnings at the minute or even second level by deeply coupling millimeter-level deformation observation with physical constraints, thus improving the scientific rigor and reliability of slope disaster monitoring.

This utility model discloses a surveying drone with a rockfall protection structure, relating to the field of drone technology. It includes a main cabin for housing the drone's electronic components; a measurement host located at the top center of the main cabin for performing measurement functions and data acquisition; a protective frame located on the top outer side of the main cabin for protecting the measurement host; several extension arms located around the main cabin for welding to form the drone's frame; a propeller for providing flight propulsion; a rockfall protection assembly located on the outer side of one end of each extension arm for preventing damage to the propeller from falling rocks; a landing support located in the middle of each extension arm for supporting the drone's landing; and a control mechanism located inside the main cabin for controlling the drone's flight and signal transmission. This utility model effectively solves the problem of surveying drones being easily damaged by falling rocks when operating in areas prone to geological disasters.

This invention discloses a geological disaster rainfall monitoring and early warning device, belonging to the field of geological disaster monitoring technology. It includes a liquid level sensor, a water pump, a motor, and a base plate. The base plate is slidably fitted onto the lower end of a square rod, and multiple first anchor rods are inserted and installed on the base plate. A cylinder is fixed to the top of the square rod, and an upper rod body is fixed to the upper end of the cylinder. A rotating wheel and a bevel gear ring are rotatably mounted on the cylinder, and the bevel gear ring is fixedly installed to the bottom surface of the rotating wheel. Two lead screws are symmetrically rotatably mounted on the side of the square rod near the top. The top ends of the lead screws mesh with the bevel gear ring for transmission. This invention enables convenient rainfall monitoring and allows for adjustment of its installation height as needed. After installation, it can better cope with severe weather such as strong winds and heavy rains, ensuring that the device can safely and stably monitor rainfall. Furthermore, it allows for convenient cleaning of photovoltaic panels, ensuring their high power generation efficiency.

This invention belongs to the technical field of coal mine power supply safety and geological disaster assessment. It discloses a dynamic analysis method for the weighting of geological disaster susceptibility evaluation factors in coal mine power supply lines, incorporating an attention mechanism. The method includes: constructing an evaluation factor system comprising static factors, dynamic factors, and mining-related influence factors; constructing a weighted directed factor network based on the temporal causal co-occurrence relationship between dynamic factors in historical geological disaster cases; acquiring real-time monitoring data for each dynamic factor, calculating the self-state contribution, network propagation contribution, and global context contribution of each dynamic factor, and obtaining the dynamic attention weight after weighted summation and normalization; calculating the geological disaster susceptibility index based on the dynamic attention weight using a weighted information content model; and achieving dynamic adaptive adjustment of the weights through time triggering, event triggering, and network structure updates. This invention can integrate real-time monitoring data, significantly improving the timeliness and accuracy of geological disaster susceptibility assessment.

The application discloses a two-dimensional distributed optical fiber sensing full waveform simulation method based on a discrete elastic lattice, and belongs to the technical field of seismic wave field simulation and distributed optical fiber sensing. The application comprises the following steps: receiving seismic wave data of a target geological medium layer; setting a two-dimensional calculation region according to the spatial distribution of the longitudinal wave velocity, the transverse wave velocity and the density parameters of the seismic wave, and taking the two-dimensional calculation region as a numerical simulation carrier of seismic wave propagation; discretizing the two-dimensional calculation region into a plurality of particle nodes, and connecting adjacent particle nodes through elasticity to form a discrete elastic lattice, so as to represent the mechanical properties of the stratum medium; and according to the density parameters, the longitudinal wave velocity, the transverse wave velocity or the equivalent elastic parameters of the stratum medium, the elastic connection between the particle nodes is endowed with corresponding elastic coefficients. The application provides a reliable numerical tool for DAS deployment design, data interpretation and seismological inversion, and can be widely applied to the fields of earthquake monitoring, oil and gas exploration, geological disaster warning and the like.

This invention provides a geological disaster monitoring and early warning method and system based on BeiDou signals, belonging to the field of data monitoring technology. It includes: extracting spectral, texture, and shape features from monitored real-time images for analysis to identify abnormal areas and corresponding geological disaster types in the monitored region; analyzing surface deformation data of the identified abnormal areas based on BeiDou satellite data to obtain deformation data features of the abnormal areas, assessing the degree of deformation, and obtaining a deformation degree assessment value; determining the geological disaster index of the monitored region based on the deformation degree assessment value and the geological disaster type, and generating a corresponding early warning strategy. This invention achieves early automatic delineation of potential hazards by analyzing image features from BeiDou satellite imagery, and combines BeiDou deformation data to quantitatively analyze the activity status of disaster bodies, generating a tiered early warning strategy, thus improving the spatiotemporal coverage, accuracy, and timeliness of geological disaster monitoring and early warning.

The utility model discloses a mountain landslide early warning device based on waveguide deformation relates to geological disaster monitoring field, including microwave source, circulator, waveguide, lossy medium, waveguide load, power detection module and signal processing module, the output of microwave source is connected with circulator's Port1 port through radio frequency cable, and Port2 port of circulator is connected with the input of waveguide through coaxial waveguide adapter, and Port3 port of circulator is connected with power detection module through radio frequency cable, the inside chamber end of waveguide is filled with lossy medium, and the outer port of waveguide is fixedly connected with waveguide load, the input of waveguide load is in close contact with lossy medium, and the output of power detection module is connected with the input of signal processing module through AD acquisition circuit. The utility model overcomes the deficiency of prior art, can realize the sensitive capture and accurate evaluation to mountain tiny slip. Thus effectively prevent the occurrence of landslide disaster.

This invention discloses a rural geological disaster prediction method and system based on edge computing, which relates to the field of geological disaster prediction technology. By comprehensively calculating the disaster prediction probability coefficient and the dynamic confidence coefficient obtained by dynamic confidence assessment based on the disaster prediction probability coefficient, the probability prediction magnitude is determined and the reliability of the prediction is assessed. This achieves second-level accurate early warning of rural geological disasters and improves the reliability of the early warning.

The utility model discloses a kind of transverse underground monitors, including inductor, the number of inductor is 4, 4 inductors are connected by end cap, form a cylindrical structure, inductor inside is provided with cavity, one side is provided with the liquid inlet that is linked with cavity, pressure transmission pipeline is set on liquid inlet, liquid is injected into the cavity of inductor by pressure transmission pipeline and keeps basic pressure, pressure sensor is set on pressure transmission pipeline, pressure sensor is used to monitor the liquid pressure in inductor, temperature sensor and moisture sensor are also set on inductor, temperature sensor and moisture sensor are used to monitor the temperature and moisture information of stratum, the stratum temperature monitored can correct the pressure change caused by temperature change, the utility model is monitored by inductor the longitude and latitude direction stress change in deep place below the position where monitor is located, real-time monitoring is carried out to the environment such as coal mine, roadway, tunnel, seismic fracture zone prone to geological disasters.

The application discloses a geological disaster multi-scale environment control factor identification system based on machine learning and relates to the technical field of geological disaster identification.The system comprises a multi-scale environment factor collection module, which acquires historical geological disaster data of a target region, extracts multi-scale environment characteristics of geological disasters and constructs a geological disaster-environment factor correlation characteristic set; a quantitative evaluation module, which establishes a geological disaster risk probability prediction model, quantitatively evaluates the marginal contribution degree of each environment factor at different spatial scales, identifies environment factors that dominate the development of geological disasters in the multi-scale of regions, river basins and slope surfaces and establishes a target region geological disaster risk probability surface and an environment factor contribution spatial distribution map; and a prevention and control module, which introduces a target region human activity intensity index and an ecological system state index, establishes an environment factor dynamic coupling risk propagation model, realizes real-time multi-scale risk transmission paths and generates a dynamic risk classification early warning atlas. The application improves the precision of geological disaster risk assessment.

The application provides a kind of underground cavern microstructure surface quantum sensing detection device, including nanoscale piezoelectric sensor array, data acquisition unit, signal processing unit and structure surface identification unit;Nanoscale piezoelectric sensor array is deployed in surrounding rock surface or borehole, and microseismic signal is detected;Data acquisition unit collects and converts signal into digital signal;Signal processing unit pre-processes and extracts feature data;Structure surface identification unit identifies the position, dip angle and length of microstructure surface based on feature data.The application can detect underground cavern microstructure surface with high sensitivity and high precision, provide important basis for rock mass stability evaluation and engineering design, reduce geological disaster risk and ensure the safety of underground engineering.

The application discloses a kind of based on spatial intelligence's active fault three-dimensional model automatic construction method, belong to geological exploration and earthquake engineering technical field;Method includes: collection data, analysis and extract minimum complete subdirectory;Through adaptive threshold hierarchical clustering combined with improved RANSAC algorithm, the exclusive small earthquake cluster of each fault is automatically identified;Based on local weighted regression, three-dimensional automatic slice is made to small earthquake cluster, and each profile fault interpretation line is fitted by moving least square method;Finally, active fault three-dimensional fine model is constructed;Test model rationality and output model file;The application realizes the automation, quantification of fault modeling whole process, can be fused multi-source data and realize multi-element constraint modeling, adapt complex fault zone modeling, and the fine three-dimensional model constructed can provide key data support for fault present-day deformation inversion, three-dimensional potential source research, earthquake geological disaster assessment, with higher engineering application value.

This invention provides a method and related equipment for assessing earthquake landslide susceptibility. Multiple landslide influencing factors are selected from multi-source data of the study area, and multicollinearity analysis and Pearson correlation coefficient analysis are performed on all landslide influencing factors to obtain basic environmental factors. Newmark displacement is calculated based on the physical and mechanical parameters in the multi-source data and the basic environmental factors. The Newmark displacement is stacked with the basic environmental factors to obtain multi-channel image data, which is then input into an earthquake landslide susceptibility assessment model for evaluation, resulting in a coseismic landslide susceptibility zoning map of the study area. Compared with existing technologies, this invention uses Newmark displacement as an independent feature input, compensating for the lack of physical mechanism support in traditional pure data-driven models. It achieves the complementary advantages of geological disaster dynamics mechanisms and deep learning feature extraction capabilities, improving the accuracy and reliability of earthquake landslide susceptibility assessment.

This utility model relates to the field of protective device technology, and discloses a safety protection device for geological disaster control engineering construction. It includes a support frame, with a support plate fixedly connected inside the support frame. Multiple fixing plates are fixedly connected to the front side of the support plate. Fixing rings are fixedly connected to the four corners of the front side of each fixing plate. Limiting posts are fixedly connected inside two pairs of fixing rings in the same vertical direction. Two connecting plates are rotatably connected to the outside of each of the two limiting posts. Moving posts are rotatably connected inside two pairs of connecting plates in the same vertical direction. Two transmission plates are rotatably connected to the outside of each of the two moving posts. In this utility model, the impact force acts on the shock-absorbing mesh, pushing the extrusion plate backward. Position correction is achieved by restoring the air pressure inside the sealed chamber and then extruding the extrusion plate again, thus achieving a buffering and shock-absorbing effect on falling objects and preventing injury to workers operating at the bottom of the work area.

This invention belongs to the field of geotechnical engineering safety monitoring technology, and discloses a distributed flexible intelligent monitoring device, method, and system for geological disasters in soil and rock. It includes a monitoring unit and a coupling unit outside the monitoring unit. The monitoring unit comprises, from bottom to top, a base layer, a flexible conductive layer, a brittle conductive layer, and a circuit layer. The flexible conductive layer includes a first flexible substrate, a one-dimensional conductive material, and a two-dimensional conductive filler. The brittle conductive layer uses a conductive material with high intrinsic stiffness. The circuit layer includes a second flexible substrate on which multiple measuring electrodes are arranged. Some measuring electrodes are connected to the flexible conductive layer, and some are connected to the brittle conductive layer. The brittle conductive layer and the flexible conductive layer are connected in parallel. This invention employs a rigid-flexible coupled double-layer conductive structure. The brittle conductive layer can capture the initiation of micro-cracks in soil and rock, thus improving the sensitivity of the monitoring unit. The coupling unit enhances the coupling between the monitoring device and the soil and rock, thereby improving monitoring accuracy.

A method, system, device, and readable storage medium for predicting leading-edge bulging cracks are disclosed, relating to the field of geological disaster monitoring and early warning. Specifically, the method includes determining the active thrust of the landslide on the leading edge based on the landslide mass weight, sliding surface dip angle, seepage hydrodynamics, and additional thrust, wherein the additional thrust is generated by external dynamic disturbance. When the detected active thrust is not less than the passive earth pressure limit, the target vertical bulging displacement is determined based on horizontal shear displacement, maximum dilatation angle, geometric correction factor for shear zone thickness, hydraulic correction factor, initial effective stress, real-time effective stress, and dilatation angle stress sensitivity factor. The predicted leading-edge bulging crack result is determined based on the target vertical bulging displacement and the predicted vertical bulging displacement. This application can improve the accuracy and applicability of leading-edge bulging crack prediction.

The application discloses a landslide early warning device and method based on image recognition and computer vision, relates to the technical field of geological disaster monitoring, and comprises an anchor rod, an energy collection intelligent sensing component and a visual verification active protection component. The top of the anchor rod is respectively provided with the energy collection intelligent sensing component and the visual verification active protection component. The energy collection intelligent sensing component comprises a flexible tail wing, a blunt body, two powerful magnets, two electromagnetic induction coils, a piezoelectric cantilever beam and a counterweight magnet. The energy collection intelligent sensing component and the visual verification active protection component are in deep cooperation, realizing full-process intelligent operation of slope monitoring from energy autonomy to intelligent diagnosis. The application is different from a traditional extensive scheme which relies on external power supply, a single sensor and fixed angle monitoring, and makes the landslide early warning process more reliable, more accurate, more adaptive and more durable. The application completely breaks through the inherent contradiction in the traditional scheme that continuous power supply, reliable sensing and accurate early warning cannot be achieved simultaneously.

The present application relates to the field of geological disaster monitoring, and discloses an intelligent monitoring and analysis system for deep stress of geological disasters based on AI big data, comprising: obtaining a multi-source long-term big data set of a deep geological disaster monitoring area; calculating a viscoelastic constitutive parameter matrix based on core rheological test parameters to generate a rheological stress evolution baseline sequence of each monitoring point; subtracting the rheological baseline from the deep stress time series data and performing STL decomposition and system drift correction to generate a net anomaly trend sequence; extracting multi-scale trend change features and rheological deviation degree features to generate a composite long-period feature vector; inputting the feature vector into a hierarchical time series deep learning model to output long-period precursor intelligent recognition results; and generating deep stress intelligent monitoring and analysis results in association with historical geological disaster event records. The present application effectively improves the accuracy of precursor recognition of geological disasters in deep soft rock areas by explicitly separating rheological background effects and using a multi-time scale hierarchical deep learning model.

The utility model relates to the technical field of geological disaster monitoring discloses the automatic monitoring structure of stratum collapse is monitored with soil pressure gauge, including monitoring hole, sleeve tube, multiple soil pressure gauge, data acquisition appearance, power supply equipment and monitoring early warning platform, monitoring hole sets in stratum collapse hidden danger point area, sleeve tube fixed mounting is in monitoring hole, multiple soil pressure gauge is arranged in the internal cavity along the length direction of sleeve tube layer, and the internal wall interval of soil pressure and internal cavity forms and has filled area, and the backfill layer is filled in filled area, data acquisition appearance is connected with every soil pressure gauge through data transmission wire, power supply equipment and data acquisition appearance electricity are connected, through layering setting multiple soil pressure gauge in sleeve tube, can layering capture the soil pressure change of different depth stratum, solved the problem that traditional monitoring point is single, the cooperation of data acquisition appearance and monitoring early warning platform realized the real -time transmission and analysis early warning of data, improved the timeliness and accuracy of monitoring.

This invention discloses a geological disaster early warning method based on remote sensing technology, belonging to the field of geological disaster early warning technology. The method includes: acquiring remote sensing image data with at least two different imaging principles within the same geographic unit and the same observation time window; performing geometric correction on the remote sensing image data under a unified coordinate system; and converting the remote sensing image data into corresponding physical quantity data and completing scale consistency processing through sensor response conversion and radiometric calibration; using the remote sensing image data and digital elevation data under the unified coordinate system, calculating the slope and aspect of each pixel, and constructing the light propagation path based on the solar altitude angle and solar azimuth angle. This invention models the influence range of environmental factors from the perspective of propagation structure by constructing light propagation paths and heat diffusion propagation paths under a unified coordinate system and calculating the direction vector field formed by the gravity direction of the slope. This allows anomaly identification to be based not only on numerical changes but also on the analysis of propagation mechanisms.

The present invention discloses an intelligent monitoring and maintenance system and method for water conservancy dams, which relates to the technical field of water conservancy project monitoring, obtains the historical water level data and seepage data of the water conservancy dam; obtains the water level relationship when no geological disaster occurs according to the historical water level data of the water conservancy dam; based on the historical water level data of the water conservancy dam when a geological disaster occurs and the water level relationship when no geological disaster occurs, obtains the water level relationship when a geological disaster occurs; obtains the current geological disaster information and water level information, corrects the water level information according to the geological disaster information, and analyzes the danger situation of the water conservancy dam according to the corrected water level information; corrects the water level data of the water conservancy dam, uncovers the true danger situation information of the water conservancy dam covered by the geological disaster, and by establishing a mathematical model, separates the influences of the geological disaster interference and the true danger situation of the dam body respectively, achieving the effect of more accurately identifying the true danger situation.

The present application relates to the technical field of geological disaster prevention engineering, and particularly relates to an adjustable buffer energy-absorbing structure of a rock retaining wall, which comprises an anchoring and guiding system, a buffer energy-absorbing assembly, a driving execution mechanism and a control unit. The present application combines the innovative adjustable inverted V-shaped buffer energy-absorbing assembly with the regulation and control strategy based on the impact dynamics model to construct an efficient, self-adaptive and easy-to-maintain dangerous rock prevention and enhancement system. Through the optimal angle adjustment verified quantitatively, the method realizes the maximum dissipation of impact energy and the active control of rockfall trajectory, improves the protection capability of the traditional rock retaining wall for the key structures behind, and has important engineering application value and popularization prospect.

The application discloses a landslide displacement prediction method based on time sequence decomposition and shape constraint, and relates to the cross technical field of early monitoring and early warning of geological disasters and artificial intelligence time sequence data processing. The method comprises the following steps: firstly, decoupling a landslide displacement sequence into a trend component, a seasonal component and a residual component; secondly, inputting the trend component and the seasonal component into corresponding TimesNet models for prediction to obtain a trend prediction value and a seasonal prediction value, and inputting the residual component into an iTransformer model for prediction to obtain a residual prediction value; in the model training stage, a MixLoss mixed loss function is introduced, which fuses mean square error and cosine similarity, and the geometric shape and evolution direction of the displacement sequence are forced to be learned by the network model through trend similarity constraint; finally, the trend prediction value, the seasonal prediction value and the residual prediction value are added to obtain a landslide displacement prediction value sequence of continuous time steps from the current time. The method improves the accuracy of landslide displacement prediction.

The application discloses a geological disaster susceptibility evaluation method based on a machine learning model and particularly relates to the field of geological disaster risk assessment, which is used to solve the problem that the existing geological disaster susceptibility evaluation method cannot effectively reflect the continuous disturbance effect of engineering activities and is disconnected with the actual engineering promotion stage. The application obtains historical engineering activity information and historical geological disaster information, calculates the spatial kernel density of different engineering activity types under the scale of a geological environment unit, constructs a geological sensitivity coefficient matrix in combination with the historical geological disaster occurrence frequency, corrects the engineering activity intensity to obtain an engineering activity influence intensity data sequence, further extracts a triggering feature index reflecting the disturbance accumulation and change characteristics of engineering activities, evaluates the geological disaster triggering potential by using a machine learning model and outputs a risk partition result, so that the dynamic evaluation of geological disaster susceptibility facing the engineering activity scene is realized.

This application discloses a geological disaster forecasting method, system, equipment, and medium based on big data analysis, relating to the field of geological disasters. The method includes: acquiring geological monitoring data of a target geological area; preprocessing the acquired geological monitoring data to obtain corrected geological monitoring data; performing at least one pre-simulation operation until the pre-simulation indicates that a target geological disaster will occur in the target geological area; wherein, the pre-simulation operation includes: inputting the corrected geological monitoring data into a constructed disaster pre-simulation model, and using the disaster pre-simulation model to predict whether a target geological disaster will occur in the target geological area in a future target period based on the corrected geological monitoring data; if the pre-simulation indicates that a target geological disaster will occur in the target geological area in a future target period, issuing an early warning information. This application can provide timely early warning of possible target geological disasters, enabling target personnel to promptly carry out management and prevention of possible target geological disasters.

This invention provides a method and system for early warning of debris flow initiation based on dynamic weighting of multiple influencing parameters, belonging to the field of geological disaster prevention and early warning technology. The method includes: constructing a set of multiple influencing parameters by acquiring source conditions, topographic features, and real-time rainfall parameters; constructing an intrinsic weighting function based on the initiation physical mechanism to calculate the intrinsic weights of the source and topographic parameters; adjusting the intrinsic weights using a dynamic correction function with rainfall intensity or cumulative rainfall as input to obtain dynamic weights; establishing a critical function coupling the dynamic weights and critical conditions to calculate the debris flow initiation critical index, and issuing an early warning when the threshold is exceeded. This solves the problem that existing debris flow early warning methods are unable to simultaneously integrate physical mechanisms and dynamic weight adjustment, and accurately reflect the critical state of debris flow initiation under the coupling effect of multiple factors.

The application relates to the field of shield tunnel construction and geological disaster prevention and control, and specifically discloses a water surface and tunnel integrated efficient targeted treatment method for full-cover water dense disaster sources, which comprises the following steps: a three-level detection system is combined with a visual technology to construct a three-dimensional model of the disaster sources, and core process parameters such as intelligent matching grouting are determined; a first grouting curtain is formed through a mobile water surface grouting platform, a drilling and grouting system carried by a shield is used to realize tunnel internal collaborative grouting, and a third impermeable layer is formed through targeted supplementary grouting after segment assembly; and parameters are dynamically regulated and controlled during construction, and treatment effects are verified through multiple modes. The water surface and tunnel integrated efficient targeted treatment method for full-cover water dense disaster sources effectively solves the problems in traditional treatment methods, such as disconnection between detection and treatment, insufficient targeting, weak adaptability to complex hydrological conditions, easy flow of grout, high ecological risk and the like, and significantly improves the treatment accuracy and efficiency of full-cover water dense disaster sources.