2713 results about "Landslide" patented technology

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention discloses a wide-area landslide rapid identification method based on an interpretable intelligent algorithm, and relates to the field of remote sensing science and technology, and the method comprises the steps: building a dual-channel feature extraction architecture through multi-source spatio-temporal data fusion and knowledge graph dynamic weighting: capturing image local textures through a lightweight CNN, modeling geological spatial correlation through a graph convolutional network, and carrying out the recognition of the landslide. Combining the SHAP value and causal reasoning to generate an interpretable contribution degree thermodynamic diagram and a rule chain; a knowledge graph bidirectional verification system is introduced, spatial logic contradictions are verified by using prior rules, and co-evolution of a model and a rule base is triggered based on misjudgment samples; outputting a multi-dimensional credibility report, quantifying uncertainty by Monte Carlo Dropout, and customizing interpretation granularity according to roles; a terrain-adaptive block-stream processing architecture is adopted, and edge lightweight deployment and federated learning are combined, so that wide-area real-time early warning and model dynamic updating are realized. According to the scheme, the limitation of a traditional black box model is broken through, and a disaster prevention closed loop with physical driving, transparent decision and second-level response is formed.

The invention provides a ground-based radar landslide monitoring and early warning method and system, and relates to the technical field of landslide monitoring, and the method comprises the steps: collecting a monitoring signal, and carrying out the filtering processing; extracting an instantaneous phase and performing wavelet packet decomposition; enhanced features are obtained through morphological filtering; constructing a monitoring feature vector; carrying out nonlinear dimension reduction on the characteristic time sequence matrix; obtaining an evolution trajectory by using a fractional order adaptive neural network; constructing a hierarchical risk reasoning framework; a risk metric value is obtained through multi-source information fusion; and carrying out dynamic evaluation by adopting a fuzzy evolution algorithm and determining an early warning level. The method can improve the accuracy and real-time performance of landslide monitoring and early warning.

The invention discloses a slope deformation monitoring and dynamic early warning method and system based on multi-sensor data, and relates to the technical field of slope monitoring, and the method comprises the steps: collecting multi-source sensor data by using pre-deployed multi-class sensors, constructing graph structure data according to the sensor distribution and the pre-processed multi-source sensor data, and carrying out the graph structure data; a graph convolutional network is used for modeling, and a slope deformation monitoring model is constructed; introducing a clustering federation learning strategy to carry out joint training on the slope deformation monitoring models of the plurality of sites, and carrying out risk grade division by using the trained slope deformation monitoring models; key influence factors of landslide disasters are extracted, an improved firefly algorithm is introduced to dynamically optimize an early warning threshold value, the optimized early warning threshold value and the current risk level are used for judgment, and early warning information is generated. According to the invention, the reliability of monitoring and the timeliness of early warning are improved through multi-source data fusion and intelligent analysis, and the crossing of slope deformation monitoring from single-point static state to networked intelligence is realized.

The invention relates to the technical field of geological disaster monitoring, in particular to a landslide geological disaster monitoring and early warning system based on multi-means fusion, which comprises a data acquisition module, a data fusion module, a landslide prediction module, a landslide disaster intelligent partition and response module and an early warning transmitting module, the data acquisition module acquires landslide related data in real time; the data fusion module is used for fusing landslide related data from different sources; the landslide prediction module is used for predicting the occurrence probability of the landslide; the landslide disaster intelligent partition and response module combines landslide related data and geologic structures monitored in real time, dynamically adjusts post-disaster response measures, and generates early warning information; and the early warning signal sending module automatically sends an early warning signal based on the generated early warning information. According to the invention, potential risks can be found in time in the early stage of landslide occurrence, and the accuracy and timeliness of disaster early warning are greatly improved.

The invention discloses a geological disaster deformation monitoring method based on remote sensing data, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, data source selection and preprocessing; s2, region feature extraction; s3, differentiating the multi-temporal remote sensing images; s4, analyzing a time sequence; s5, performing three-dimensional reconstruction on the deformation area; s6, settlement and landslide monitoring; s7, performing fusion analysis on environment and meteorological data; s8, performing data anomaly detection and automatic early warning; and S9, risk assessment and influence analysis. According to the geological disaster deformation monitoring method based on the remote sensing data, SAR, optical images and LiDAR data are combined, the defect of dependence of a single data source in the prior art is overcome, dynamic data fusion and time sequence analysis are used, multi-dimensional real-time monitoring of earth surface deformation is achieved, the monitoring precision and timeliness are improved, and the geological disaster deformation monitoring method based on the remote sensing data has good application prospects. More comprehensive geological disaster early warning can be provided, and the effectiveness of disaster prevention and control is greatly enhanced.

The invention discloses a regional risk monitoring and early warning system based on space-air-ground multi-source data fusion, and relates to the technical field of disaster monitoring and early warning. According to the invention, comprehensive monitoring of regional risks is realized by fusing various data sources such as satellite remote sensing, unmanned aerial vehicle aerial survey and ground-based radar. The system can automatically process data, construct a high-precision three-dimensional real scene model, and identify a potential risk area based on a time sequence InSAR technology and a multi-source data fusion technology. And meanwhile, non-contact high-precision measurement is carried out on key facilities. An interactive monitoring and early warning platform is constructed, functions of deformation graph generation, target detection and risk division, data fusion processing, potential risk surface determination, stress-strain analysis and early warning information release are realized, and support is provided for regional risk prevention. The system has the characteristics of high automation, intelligence and visualization, and is suitable for various application scenes such as earthquakes, floods, landslides and the like.

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

The invention discloses a landslide hidden danger point displacement monitoring system applying multi-source data fusion, and the system comprises a multi-source sensing unit which comprises a spaceborne radar sensing module which is used for obtaining large-range ground surface deformation data through a synthetic aperture radar; the earth surface deformation sensing module is used for monitoring earth surface displacement in real time through a double-frequency GNSS receiver and an optical fiber grating sensor; the underground stress sensing module is used for acquiring the stress change of an underground rock-soil body through a micro-seismic monitoring array and a distributed optical fiber sensor; and the edge calculation unit comprises a dynamic weight fusion module which is used for dynamically distributing the weight of the multi-source sensor according to the geological state and generating high-precision fusion displacement field data. According to the invention, through multi-source data fusion, model parameter real-time updating and a closed-loop optimization mechanism, bottlenecks of a traditional monitoring system in aspects of data quality, model precision and response efficiency are broken through, and data support is provided for accurate early warning and intelligent emergency of landslide disasters.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

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

The invention discloses a rock slope risk assessment method and system based on artificial intelligence, and belongs to the technical field of geological disaster risk assessment.The rock slope risk assessment method comprises the steps that a slope three-dimensional digital model is constructed, and geological-environment-monitoring data are integrated; extracting spatio-temporal characteristics, and predicting a landslide risk probability; quantifying a risk space diffusion path, and identifying a high-risk area; the early warning threshold value is dynamically adjusted, and accurate early warning is achieved; the problems that in the prior art, how to integrate multi-modal data (geological data, environment data and monitoring data) to achieve real-time sensing of the slope state and how to construct a deep learning model with high generalization ability to deal with risk prediction under the complex geological condition are solved. The problem of how to establish a risk transfer model considering spatial heterogeneity to improve high-risk area identification precision and how to realize dynamic adaptive adjustment of an early warning threshold to match personalized risk features of different slopes is solved.

The invention relates to the technical field of landslide motion simulation, in particular to a landslide motion simulation method based on a motion model. The method comprises the following steps: acquiring data of a target landslide area; generating complete topographic feature data through landform identification and difference complementation; performing topographic analysis on the data; identifying a landslide-prone area, constructing a three-dimensional model, judging a critical state, performing risk weight calculation, and generating a probability distribution set of the landslide-prone area; performing path analysis to obtain an initial motion track and a probability prediction model thereof, constructing a three-dimensional dynamic simulation scene, performing iterative analysis, finally generating a landslide motion simulation result, and establishing a dynamic risk early warning and emergency response model to improve the prevention and control capability of landslide disasters. According to the invention, a more accurate and more reliable landslide motion simulation method is realized.

The invention discloses a landslide monitoring method based on remote sensing data, and the method comprises the steps: obtaining a high-resolution optical remote sensing image and a synthetic aperture radar image of a research region, carrying out the registration processing of the optical and radar remote sensing images, and obtaining a remote sensing image with an accurate spatial position and an accurate geometric shape; generating a landslide area fusion image with high spectral resolution; the matched high-resolution DEM data is obtained; based on the matched high-resolution DEM data, gradient parameters of the research area are calculated, and a gradient threshold value is determined according to gradient distribution characteristics and prior knowledge; extracting slope direction parameters of the research area by adopting a slope direction algorithm based on grids and vectors, and screening out a landslide-prone area in combination with the slope threshold value; obtaining a landslide risk distribution map; and obtaining the occurrence possibility and danger level of the landslide, and obtaining the landslide condition according to the occurrence possibility and danger level of the landslide. According to the invention, precise identification and dynamic monitoring of landslide disasters are realized.

The invention belongs to the technical field of risk prediction, and discloses a landslide risk prediction method and system based on data intelligent analysis, and the method comprises the steps: obtaining corresponding multi-source heterogeneous information, carrying out the feature extraction and data fusion of the information, and obtaining a mountain monitoring basic data set; constructing a mountain twinborn feature model based on the mountain monitoring basic data set, and performing landslide scene simulation based on the mountain twinborn feature model to obtain corresponding risk feature information; constructing a trigger factor association network based on the risk feature information, and performing zoning evaluation and grading on the target landslide risk in combination with the mountain monitoring basic data set; constructing a corresponding grading risk zoning map; obtaining a landslide risk dynamic prediction result based on the grading risk zoning map; a corresponding grading early warning rule base is constructed; and the target mountain area is monitored in real time based on the grading early warning rule base, and the monitoring result is fed back. According to the invention, a comprehensive and efficient solution is provided for prevention and treatment of landslide disasters.

The invention relates to the technical field of geological landslide monitoring, in particular to a tower footing geological landslide monitoring and early warning system and method. The method comprises the following steps: arranging an inclination angle sensor, a Beidou-GNSS module, a soil moisture content probe and a micro-seismic accelerometer on a tower footing and the periphery of the tower footing, and collecting the inclination amount, three-dimensional displacement, soil moisture content and vibration response information of the tower footing to form a monitoring original data set; timestamp unification, WGS-84 projection coordinate system conversion and multi-frequency vibration elimination are carried out on the monitored original data set, and an air-ground integrated displacement profile is constructed; the whole process realizes high integration and automation in the aspects of data acquisition, processing, fusion and transmission, so that the timeliness, accuracy and data integrity of landslide risk monitoring are ensured, and scientific decision support is provided for disaster prevention and reduction.

The invention discloses an air-ground cooperative monitoring and early warning method and system for a buried gas pipeline in a landslide hidden danger area, and the method comprises the steps: carrying out the ground monitoring through employing distributed optical fiber sensing, and enabling an optical fiber sensor to be embedded in the outer wall of the buried gas pipeline or to be laid with an accompanying optical fiber pipeline; collecting multiple physical quantity data of strain, temperature and vibration of the pipeline; in-air monitoring, a multi-sensor platform carried by an unmanned aerial vehicle is used for carrying out in-air inspection; the air monitoring data and the ground monitoring data are fused, and a landslide hidden danger area pipeline safety state evaluation model is constructed; performing real-time intelligent analysis on the fusion data by using an advanced mixed attention convolutional neural network, and identifying a potential catastrophe hidden danger area of the pipeline; and according to a risk assessment result, early warning grading is carried out, and a corresponding emergency plan is formulated. According to the method, real-time and accurate monitoring of the pipeline state can be realized, the landslide hidden danger can be found in time, accurate early warning can be realized, and safe operation of the buried gas pipeline in the landslide hidden danger area can be effectively guaranteed.

The present disclosure relates to the technical field of landslide hazard model tests, and in particular to a landslide model test method and device for realizing multi-variable synchronous monitoring. In the test device, a soil slope simulation unit is configured to pile up a soil slope and simulate different inclinations of the slope. A rainfall simulation unit is configured to simulate rainfall of different intensities and induce a landslide. A runoff-seepage separation unit is configured to realize separation of runoff from seepage of the slope under the effect of rainfall. A monitoring module is configured to monitor runoff and seepage flow rates of the slope, changes in water content and pore water pressure within the slope, and a morphological change of the slope under the effect of rainfall, to obtain a mechanism of rainfall-induced landslide deformation and damage based on changes in the flow rate.

The invention provides a landslide displacement space-time prediction method, device and system and a storage medium, and relates to the technical field of engineering geology, and the landslide displacement space-time prediction method comprises the steps: obtaining the historical landslide multi-field time sequence monitoring data of each monitoring point of a to-be-measured region, constructing a monitoring point bitmap based on the historical space related data, constructing a time sequence characteristic matrix according to the historical displacement time sequence, the historical landslide external disturbance factor time sequence and the monitoring point bitmap; inputting the time sequence characteristic matrix and the monitoring point bitmap into a trained landslide displacement space-time prediction model to obtain a landslide displacement space-time prediction result; the landslide displacement space-time prediction model is constructed based on an improved GCN network and an improved BiLSTM network; according to the method, the space-time prediction precision of landslide displacement can be remarkably improved by combining the space correlation data and the external disturbance factors and utilizing the capability of the deep learning model.

The invention discloses a landslide disaster early warning system and method, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: carrying out the omnibearing monitoring of a target region, and collecting monitoring data; the method comprises the following steps: preprocessing collected monitoring data to obtain monitoring processing data; analyzing the monitoring processing data, and performing feature extraction to obtain a target feature vector; constructing a landslide disaster risk assessment model by adopting logistic regression and a decision tree; acquiring historical monitoring data according to a large amount of historical data and landslide cases, and training the model by using the historical monitoring data; real-time monitoring data are collected and input into the landslide disaster risk assessment model, the landslide risk level of the target area at the current moment is judged, and a corresponding early warning prompt is given; the method comprehensively considers the linear and non-linear relationship of the data, can accurately evaluate the landslide risk grade of the target area, and gives out graded early warning prompts. Compared with an existing scheme, the method has the advantage that the monitoring data source, the data processing precision and the model prediction capability are remarkably improved.

The invention, which relates to the technical field of landslide monitoring, discloses an optical fiber sensing landslide dynamic response monitoring system comprising an event monitoring module, a sampling optimization module, a landslide risk analysis module and an early warning level adjustment module. By comparing the offset of adjacent data points in real time, tiny displacement abnormity can be quickly identified, instant marks are provided for early geological activities, and the sampling frequency is automatically adjusted according to real-time monitoring data, so that data acquisition is finer, meanwhile, storage and processing of irrelevant data are reduced, the operation efficiency of the system is effectively improved, and the working efficiency of the system is improved. Real-time data and a historical landslide mode are finely compared, comprehensive analysis is carried out in combination with key indexes such as earth surface displacement and soil humidity, the system can accurately calculate the probability of occurrence of the landslide, the accuracy and timeliness of early warning are greatly improved, and casualties and property losses caused by landslide disasters are effectively reduced.

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 provides a deformation rate difference-based time sequence InSAR landslide deformation monitoring fusion method, which comprises the following steps of: acquiring and preprocessing a synthetic aperture radar image of a monitoring area to obtain processed SAR image set data, then acquiring a linear deformation rate by using Stacking-InSAR, extracting PS point high-precision deformation speed by using PS-InSAR, and acquiring a nonlinear deformation rate by using SBAS-InSAR; coordinates are generated according to the deformation rate of each technical data point in combination with the orbit and geometric information, and creep, acceleration and temporary sliding deformation areas are divided by using ArcGIS and K-Means clustering; then, SBAS-InSAR processing is carried out on the newly-added image in the regional boundary, and dynamic deformation information is obtained; and finally, carrying out weighted averaging on the deformation rates of the three monitoring technologies. The landslide deformation monitoring precision under complex terrains and environments is effectively improved, errors caused by vector monitoring point density and deformation rate problems in single interference measurement are overcome, and powerful scientific support is provided by accurately monitoring and analyzing landslide evolution, determining the range, predicting the trend and early warning.

Disclosed in the present invention is a landslide identification method based on physical constraint machine learning, comprising: first, collecting slope data information and storing same for later use; then, utilizing the collected data, and embedding physical constraints into a neural network for learning to obtain a prediction model, the physical constraints comprising an erosive landslide mobility dynamic equation, a boundary condition and an initial condition; and collecting slope data information of a slope to be predicted, and then using the prediction model obtained in step S2 to perform predictive analysis on the motion process of said slope, so as to implement the prediction of the landslide dynamic process of said slope. According to the present invention, more physical information, such as a mathematical physical equation, a boundary condition, an initial condition and the like related to landslide, are added into a neural network of machine learning, such that the neural network can better "understand" the physical significance of the landslide, and the neural network can predict the landslide more accurately in the absence of much observational data.

The invention discloses a slope stability evaluation and landslide early warning system and method based on strain monitoring, and relates to the technical field of geotechnical engineering safety monitoring, in the slope stability evaluation system, strain monitoring optical cables are arranged in a plurality of drill holes formed in a slope in a penetrating mode and are fully coupled with slope soil; laser is emitted to the strain monitoring optical cable through a Brillouin optical time domain reflectometer, and a Brillouin scattering light signal is received from the strain monitoring optical cable; brillouin frequency shift is determined according to the Brillouin scattered light signals, then transverse shear force is determined according to the Brillouin frequency shift, the slope stability coefficient of the slope is calculated according to the change of the transverse shear force, and whether landslide is likely to happen or not is further judged in combination with soil humidity measured by the humidity monitoring optical cable, and early warning is conducted in time. Compared with an existing slope monitoring technology, the slope monitoring system has the advantages of being high in precision, wide in monitoring range, resistant to electromagnetic interference, resistant to corrosion and the like, and real-time effective monitoring of different deformation stages of a slope can be achieved.

The invention discloses a landslide displacement prediction method for reservoir bank inspection, and relates to the technical field of disaster prevention, and the method comprises the following steps: collecting original data of geological conditions, meteorological monitoring and bank landslide mass monitoring of reservoir bank inspection, and carrying out the preprocessing of the original data; extracting features related to the landslide deformation mode from the preprocessed original data, and integrating the features to obtain a feature set; and training the extracted features by adopting a support vector machine algorithm based on the related data of the feature set. According to the landslide displacement prediction method for reservoir bank inspection, by combining geological conditions, meteorological monitoring and reservoir bank landslide mass monitoring data, landslide deformation inducements can be analyzed more comprehensively, the influence of rainfall and earthquakes on landslides is considered, the possibility of landslide occurrence can be predicted more accurately, and the prediction accuracy is improved. And by setting a risk assessment threshold and assessing different landslide risk levels, early warning can be performed before the occurrence of the landslide, so that early recognition of the landslide risk is realized.

The invention discloses an intelligent visual monitoring method and system for a small-scale deformation body of a side slope, and relates to the technical field of side slope monitoring, and the method comprises the steps: searching targets in all view fields of a side slope surface, carrying out the initial deployment of the targets, carrying out the target detection of the targets, and determining whether the targets exist or not; after the existence of the target is confirmed, pixel-level positioning is firstly carried out on the edge of the target, then sub-pixel-level positioning is carried out on the edge of the target, the ellipse circle center coordinate of the target ring is obtained, and the displacement of the target is calculated in combination with the historical position of the target; and judging the structural deformation of the slope according to the target displacement, and judging the landslide risk of the slope by using the structural deformation. According to the invention, high-precision displacement monitoring is carried out on a plurality of point positions of the whole slope surface through vision, planar coverage monitoring of the monitored slope surface is realized, the construction deployment difficulty and the monitoring cost of monitoring equipment are greatly reduced, and the monitoring efficiency of a small-scale deformation body of the slope is improved.

The invention provides a dynamic monitoring method for a soil mass seepage field of a power transmission tower slope. According to the method, related data of the slope are acquired through multi-source data acquisition and technologies of pressure sensing and static pressure measurement, displacement monitoring, multi-sensor fusion, Beidou / GPS positioning, resistivity method exploration, soil moisture content monitoring and the like; fusing the collected multi-source data, analyzing the dynamic change of the seepage field by using a risk assessment model and a machine learning algorithm, and predicting the evolution trend of the seepage field; a lightweight model is deployed at a monitoring terminal to realize local rapid processing of data, alarm information is immediately sent out once an abnormal state is monitored, and scientific and reasonable decision support is provided for protection and treatment of a transmission tower slope according to early warning information in combination with a pre-made decision scheme; according to the invention, the accuracy and timeliness of transmission tower slope soil seepage field monitoring can be effectively improved, and the threat of landslide and other disasters to a transmission line is reduced.

The invention relates to the technical field of mine potential safety hazard identification, in particular to a mine potential safety hazard identification method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a global image of a mining area by using an unmanned aerial vehicle camera, and constructing a three-dimensional model; thirdly, performing rock-soil landslide probability estimation through weathering erosion mechanism reduction in combination with the three-dimensional model and the global image, and calculating landslide coercing force potential energy increment; then, according to the landslide probability and the coercing force incremental data, carrying out risk grade endowing on the local area of the mining area; and finally, constructing a mine potential safety hazard identification optimization model based on the risk level, and sending the model to a terminal for potential hazard identification. According to the invention, the mine potential safety hazard identification technology is optimized, so that the mine potential safety hazard identification technology is more perfect.