3039 results about "Geological disaster" patented technology

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

This application relates to the field of geological disaster early warning technology, and discloses a method for monitoring, predicting and warning of geological disasters based on artificial intelligence, including the following steps: data collection: collecting geological disaster monitoring data; data preprocessing: time alignment and missing completion of the collected data; spatiotemporal correlation matrix construction: analyzing the preprocessed monitoring data; feature extraction: extracting the nonlinear spatiotemporal characteristics of geological disaster monitoring data; time series prediction: outputting the geological disaster risk level in the future period; feedback optimization: based on the error between the prediction result and the actual observation data; warning release: generating geological disaster warning information based on the predicted risk level. Through data collection, preprocessing, feature extraction, prediction optimization and warning release, a complete geological disaster monitoring and prediction method is constructed, which integrates multi-source data, extracts key features and dynamically predicts risks, so as to ensure the accuracy of the model and provide a scientific basis for disaster prevention and control.

The invention discloses an optimal design method and system for foundation pit supporting, and aims to improve the design efficiency and safety of a supporting structure, collect on-site landform and soil physical and mechanical parameters, evaluate the influence of foundation pit excavation on the surrounding environment, analyze geological disaster risks, formulate preventive measures and select a proper supporting structure type. Finite element analysis software is used for simulating the performance of different supporting schemes, monitoring equipment is installed to monitor the state of a foundation pit in real time, a machine learning algorithm is used for establishing a prediction model, a design scheme is adjusted according to monitoring data, the final performance of a supporting system is evaluated, abnormal conditions are recorded, and the design process and the construction method are continuously improved according to project feedback. According to the method, a data acquisition and processing module, a design decision support module, a numerical simulation and optimization module, a monitoring and feedback control module, a construction management module and a later evaluation and feedback module are integrated, a closed-loop optimization design system is formed, key problems in support design are solved, and engineering efficiency and safety are remarkably improved.

The invention relates to a geological disaster automatic identification method and system based on multi-source remote sensing data, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: collecting the multi-source remote sensing data of a to-be-detected region, carrying out the cross-modal registration, and generating a registered multi-source data set; carrying out multi-modal feature extraction based on the multi-source data set and carrying out space-time correlation analysis to obtain a multi-modal feature map; performing dynamic weight distribution on the multi-modal feature map, generating a fusion feature vector, inputting the fusion feature vector into a pre-trained geological disaster prediction model, and outputting a geological disaster probability map; performing binarization segmentation on the geological disaster probability graph to obtain a potential disaster area mask; and according to the geological disaster probability map and the potential disaster area mask, based on a preset joint determination rule of the surface deformation rate and the gradient characteristics, carrying out risk grade division on the potential disaster area to obtain a risk grade distribution map of the to-be-detected area. The disaster prediction precision can be improved, and the emergency response capability is enhanced.

The invention relates to the technical field of geological monitoring, in particular to a method for intelligently monitoring geological disasters based on satellite remote sensing, which comprises the following steps of: obtaining corresponding earth surface coverage information through image registration and ortho-rectification processing; identifying terrain boundaries, geological structure features and geological attribute information through the land surface coverage information; recognizing surrounding hidden danger points through a topographic feature extraction method; an image fusion enhancement method is matched with a permanent scatterer interference method, a stable scatterer is extracted, a deformation monitoring network is constructed, and a phase difference value in a high-risk area is obtained through calculation; carrying out unwrapping processing through the phase difference value, and carrying out inversion to obtain earth surface three-dimensional deformation field information in the monitoring area; establishing a geological disaster risk assessment model through space-time clustering analysis; by establishing a risk assessment model, the geological disaster risk in the monitoring area is quantitatively assessed. The beneficial effect of the invention is that comprehensive monitoring and accurate early warning of geological hazard hidden dangers around the transformer substation are realized.

The invention discloses a slope geological disaster multi-mode early warning method and system, and relates to the technical field of slope monitoring, and the method comprises the steps: laying a distributed sensor network, and collecting slope multi-source monitoring data; the multi-source monitoring data comprises displacement data, stress data and vibration frequency data; feature extraction is performed on the multi-source monitoring data by using a graph neural network, and the feature extraction comprises capturing spatial correlation among sensor nodes and identifying an abnormal mode, identifying a potential instability area of the slope according to the abnormal mode, and outputting features of the potential instability area; and generating a slope stability risk grade assessment report based on the characteristics of the potential instability region and a disaster evolution graph constructed by combining historical disaster data. According to the invention, the comprehensive monitoring of the slope from the outside to the inside and from the static state to the dynamic state can be realized, the abnormal mode and the potential instability area can be accurately identified, and the accurate assessment and timely early warning of the slope risk can be realized based on the historical data.

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 geological disaster early warning method and a precise early warning system based on multi-source data fusion, and relates to the technical field of geological disaster early warning. According to the method, multi-source heterogeneous data such as remote sensing, meteorological and geological monitoring are fused, a standardized protocol is utilized to unify a data format and temporal-spatial resolution, a standardized data set is formed, key features are extracted by adopting principal component analysis and a recursive feature elimination algorithm, and a long-short-term memory network and a convolutional neural network model are combined, so that the real-time performance of the system is improved. According to the method, the disaster risk is accurately predicted, the space risk distribution diagram is generated, in addition, through application of the real-time stream processing framework and the self-adaptive learning algorithm, rapid distribution of early warning signals and dynamic optimization of model parameters are achieved, the accuracy and timeliness of an early warning system are remarkably improved, and the geological disaster risk is effectively reduced.

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 relates to the technical field of geological disaster intelligent monitoring, in particular to a geological disaster intelligent monitoring system and method based on multiple sensors. The method comprises the following steps: covering a monitoring area through multiple sensors, and collecting and summarizing environment data; separating effective signals and noise by adopting a space-time adaptive decomposition method, completing data denoising by combining a signal-to-noise ratio dynamic threshold and a local weighted regression smoothing trend residual error, then aligning time and dimensions through linear interpolation and standardization, and integrating multi-sensor data based on an adaptive weighted fusion method; the fused data is subjected to generation and review of a to-be-reviewed mark, so that the data integrity is ensured; key features are extracted, a mixed model based on LSTM and a random forest is constructed to evaluate disaster risks, and a risk grade division rule is set; and finally, outputting fusion data and an analysis result, and generating an early warning signal and a decision suggestion for potential geological disasters. According to the invention, accurate monitoring and risk assessment of geological environment changes are realized.

The invention relates to the technical field of geological disaster monitoring and early warning, in particular to a side slope slippage monitoring and early warning method based on an image recognition technology, which comprises the following steps: arranging a monitoring target and a reference target in a to-be-monitored area of a side slope, arranging two image displacement monitoring devices at opposite stable positions at equal height to acquire two-dimensional displacement data of the targets; after error correction, an equipment monitoring coordinate system included angle is calculated based on parameters such as equipment spacing, slope inclination displacement is calculated through vector synthesis, and three-dimensional slippage deformation is calculated in combination with vertical displacement; meanwhile, a multi-level early warning mechanism based on the slip rate and the accumulated slip amount is established, a Delaunay triangulation network is constructed, and an improved adaptive Kriging interpolation algorithm is adopted to realize deformation trend prediction. According to the method, binocular vision and multi-algorithm fusion are utilized to realize high-precision three-dimensional monitoring, the evaluation accuracy is improved through the dynamic weighting model and intelligent early warning, the method has the advantages of high monitoring precision, timely early warning, high adaptability and the like, and the safety of slope engineering can be effectively guaranteed.

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 discloses a reservoir bank slope geological disaster risk identification method based on an unmanned aerial vehicle laser radar, and relates to the technical field of reservoir bank slope prevention and control, an unmanned aerial vehicle is used for carrying laser radar equipment, flight scanning is carried out on a reservoir bank slope area, and three-dimensional point cloud data is obtained. According to the reservoir bank slope identification method based on the unmanned aerial vehicle laser radar detection data, high-precision terrain and geological information of the reservoir bank slope is rapidly acquired in a non-contact manner by using the three-dimensional imaging capability of the unmanned aerial vehicle laser radar technology, so that the data acquisition efficiency and safety are greatly improved; the method also remarkably improves the precision and integrity of data, accurately captures key topographic factors such as topographic relief, gradient, slope direction and the like through laser radar scanning, and provides a solid foundation for the accurate recognition of bank slope types in combination with geological factors such as geological structures, lithology and the like, so that the prediction and evaluation of bank slope disasters are more accurate. Preventive measures can be taken in advance, and disaster risks are reduced.

The invention relates to the technical field of geological disaster prevention and control, in particular to a rock engineering slope stability classification method. According to the technical scheme, the method comprises the following steps: obtaining spatial distribution characteristics of a slope structural plane through combined detection of three-dimensional laser scanning and a geological radar, and establishing a quantitative index system containing occurrence, density and connectivity of the structural plane; the method comprises the following steps: acquiring a fracture signal in a slope in real time by adopting a micro-seismic monitoring system, and extracting a micro-seismic event energy release rate, a dominant frequency offset and a seismic source mechanism parameter through time-frequency analysis; and constructing a geologic structure evolution inversion model, reconstructing a geologic structure evolution process based on the regional geologic database and the field drilling data, and calculating the distribution characteristics of the residual stress field of the structure. According to the method, comprehensive, accurate and real-time monitoring and classified evaluation of the rock engineering slope stability are realized, potential risks can be effectively predicted, a scientific and accurate decision basis is provided for slope support and treatment, and the safety and stability of the rock engineering slope are greatly guaranteed.

The invention discloses a multi-modal remote sensing intelligent identification method for hidden geological disasters. The method comprises the following steps: carrying out normalization processing on multi-modal remote sensing data; constructing a pixel-level cross-modal visual converter; constructing a pixel-level cross-modal attention module, wherein the pixel-level cross-modal attention module comprises a pixel self-attention sub-module, a pixel mutual-attention sub-module and a layer adaptation mixing module; a layer adaptive noise mechanism is introduced, and the weight ratio of intra-modal self-attention and inter-modal mutual attention in different levels is adjusted in a self-adaptive mode; constructing a relation discriminator, and modulating the attention calculation process by evaluating the difference between modals of corresponding points in the space; training the model, adopting a multi-task loss function including semantic segmentation loss and boundary perception loss to perform joint optimization, and generating a concealment geological disaster segmentation result. According to the method, complementary information of an RGB optical image, an InSAR deformation rate image and DEM elevation data is fully utilized, and accurate recognition of the hidden geological disaster is realized through an efficient pixel-level cross-modal fusion mechanism.

The invention relates to the technical field of data processing, and discloses a geological disaster meteorological risk early warning method and system based on machine learning. The method comprises the following steps: acquiring rainfall intensity, soil saturation, underground water level change and slope runoff coefficient by a multi-source sensor, and constructing a geological disaster meteorological data set; performing sensitivity weight distribution on the meteorological factors according to geological conditions to obtain a weight matrix; carrying out weighted fusion on the weight matrix and meteorological time series data, and extracting features through a geological constraint long-short-term memory network to obtain a risk probability vector; dynamically adjusting an early warning threshold value based on the safety coefficient change rate; and carrying out Bayesian fusion on the risk probability vector and an adaptive early warning threshold to obtain a graded early warning result. The technical problem that an existing geological disaster early warning technology lacks a multivariate meteorological factor intelligent weight distribution and geological condition adaptive threshold adjustment mechanism is solved.

The invention relates to the technical field of geological disaster monitoring, and discloses an intelligent early warning method and system for geological disasters in geotechnical engineering, and the system comprises a data collection module, a data processing module, a feature extraction module, an early warning model module, a response execution module and an optimization feedback module. Static geological parameters, dynamic environment parameters and historical disaster data are integrated, a standardized space-time correlation data set is constructed, the limitation of a single data source is broken through, multi-dimensional dynamic response characteristics of a rock-soil body are captured, a reliable data basis is provided for accurate early warning, the rigidity defect of a traditional fixed threshold value is avoided, and the early warning accuracy is improved. The method achieves the self-adaption of the risk early warning sensitivity, reduces the misjudgment and missing judgment caused by environment interference, intercepts a dynamic data stream in real time through a sliding window, calculates the risk mean value and variance, quickly responds to sudden environmental changes such as rainfall sudden change and vibration abnormality, and generates a graded early warning signal.

The invention discloses a collapse geological disaster vulnerability model and risk assessment method and system, and relates to the technical field of geological disaster risk assessment, and the method comprises the steps: collecting and preprocessing target rock mass information; collecting monitoring data and long-term rainfall data of the three-axis acceleration sensor; the acceleration fluctuation characteristics are analyzed, and the risk of rock mass instability is recognized in combination with rainfall data; the instability risk of the rock mass is quantified based on a Monte Carlo method; predicting a motion path and a stacking range of the collapsed rockfall by utilizing numerical simulation, and evaluating threats of the collapsed rockfall to surrounding electric power facilities; and performing level-to-level management on the surrounding area of the transformer substation through the risk level model. Real-time monitoring, probability calculation and numerical simulation technologies are adopted, and the problem that in the prior art, power facilities lack dynamic monitoring, accurate evaluation and graded protection is solved. According to the overall scheme, real-time and automatic monitoring and risk grading of collapse geological disasters are achieved, and the safety and reliability of electric power facilities under the disaster condition are effectively improved.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a railway tunnel portal geological disaster deformation early warning system based on an SAR radar, and the system comprises a digital twinborn body construction module which constructs a digital twinborn body with initial parameters based on basic data; the SAR deformation monitoring module is used for acquiring SAR deformation observation data; the digital twinborn body dynamic optimization module is used for inverting and updating parameters by using an optimization algorithm based on the SAR data, and generating an optimized twinborn body; a risk prediction and key area identification module which deduces a disaster scene based on the optimized twinborn body, predicts the risk and identifies a key risk area; and the intelligent early warning module is used for generating early warning information based on the prediction risk and the key risk area. According to the method, the geomechanical digital twins are dynamically optimized by adopting the SAR data, accurate prediction, key area identification and intelligent grading early warning of the geological disaster of the railway tunnel portal are realized, and the initiative and accuracy of risk cognition and early warning are remarkably improved.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster activity target monitoring method based on dynamic visual tracking, which comprises the following steps of: adjusting multiple shooting parameters of a camera according to illumination intensity difference in an environment based on surface image acquisition equipment, and recording time points and geographical location information of image shooting in real time; a sequence of geo-tagged images is generated. According to the method, camera parameters are adjusted according to real-time illumination conditions, the quality of surface images is optimized, the monitoring and recognition capability of surface cracks and deformation is improved, and data support is provided for prediction and type recognition of geological disasters through multi-time-point image analysis and calculation of displacement speed and sedimentation rate, so that early warning is more accurate and targeted, and the early warning efficiency is improved. According to the method, the monitoring data and the topographic features of the target area are combined, the disaster type is accurately recognized, the development trend is predicted, dynamic monitoring and pre-judgment of geological disaster activities are achieved, the effectiveness of prevention measures is enhanced, and potential threats to human life and property are reduced.

The invention relates to the technical field of remote sensing monitoring, in particular to an SAR satellite data driven freeze-thaw landslide deformation monitoring system which comprises a multi-band SAR data acquisition module, a data preprocessing module, a deformation detection module, a dynamic modeling module, a risk assessment early warning module and a report comprehensive display module. Wherein the multi-band SAR data acquisition module is used for acquiring multi-band radar image data; the data preprocessing module is used for carrying out denoising, correction and image registration processing; the deformation detection module is used for generating a high-precision earth surface deformation graph; the dynamic modeling module is used for constructing a dynamic geologic model of the freeze-thaw landslide; and the risk assessment early warning module is used for carrying out landslide risk assessment. According to the invention, through integration of SAR satellite data and dynamic geological modeling, the precision and real-time performance of freeze-thaw landslide monitoring are significantly improved, the accuracy and timeliness of disaster early warning are enhanced, and efficient scientific and technological support is provided for geological disaster management.

The invention relates to a landslide detection method based on an improved YOLOv10n model, and belongs to the technical field of computer vision and geological disaster monitoring. The method comprises the following steps: firstly, collecting high-resolution remote sensing image data, and preprocessing by using a data enhancement technology; then, dividing the preprocessed remote sensing image data into a training set, a verification set and a test set, and carrying out real frame marking and category marking; then, an improved YOLOv10n model is constructed and trained on the basis of a Swin Transform backbone network, a depth separable convolution module and a context aggregation attention mechanism; and finally, outputting a landslide detection result by using the trained improved YOLOv10n model. According to the method, high efficiency and real-time performance are kept when high-resolution remote sensing images are processed, the method is particularly suitable for landslide real-time monitoring in a resource limited environment, the accuracy and efficiency of landslide detection are improved, the method can better adapt to complex terrains and background conditions, and efficient and reliable technical support is provided for landslide disaster monitoring.

The invention discloses a mine geological environment characteristic monitoring and recovery treatment method, and relates to the field of geological environment monitoring, and the method comprises the steps: collecting mine geological data; adopting a pre-trained LSTM neural network model to predict membership information of geological disaster evolution in a period of time in the future; adjusting a state transition probability matrix in the Markov chain model by using the membership information; predicting the evolution state of the geological disaster in a future period of time by using the adjusted state transition probability matrix; the method comprises the following steps: mapping mine geological data into nodes of a Bayesian network, and constructing a directed acyclic graph containing environmental factors; based on a Bayesian network inference algorithm, calculating the occurrence probability of geological disasters under different environmental factor combinations; fusing the predicted evolution state of the geological disaster in a future period of time with the geological disaster occurrence probability deduced by the Bayesian network to obtain a geological disaster prediction evaluation result; in view of low mine geological disaster time sequence evaluation precision in the prior art, the evaluation precision is improved.

The invention discloses a coal mining subsidence area surface crack intelligent extraction method based on an improved Transform model, and belongs to the technical field of remote sensing image processing. Firstly, an unmanned aerial vehicle carrying a high-resolution optical camera is used for collecting images, the image overlapping rate of 70%-80% is guaranteed, and a training data set is constructed through professional labeling, cutting screening and data enhancement. The encoder of the innovative model is very distinctive, and the adaptive multi-scale patch mapping layer can dynamically adjust the patch size according to the local complexity of the image and efficiently extract features; double-attention fusion is combined with optimization position coding, and long-distance dependency capture is enhanced; and the calculation amount and the overfitting are reduced by the dynamic sparse connection full-connection layer. Residual attention enhancement pyramid pooling and a space-channel attention bottleneck mechanism are adopted, key features are highlighted, and noise is suppressed; and a breakpoint detection and connection rule determination module is utilized to realize complete restoration of the ground fracture. After the data set is used for training a model, deployment is carried out, and through preprocessing, encoding and decoding and post-processing, ground fracture information can be accurately obtained, and a data foundation is built for mining area safety management and geological disaster prevention and control.

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 relates to the field of geological disaster prediction, and discloses a geological disaster real-time monitoring and early warning system based on Internet of Things and artificial intelligence, and the system comprises a sensing module which is used for collecting multi-source data of geological disasters in real time through all Internet of Things sensors, and visually displaying potential geological disaster hidden danger signs on the ground surface; the transmission module is used for transmitting various data and guaranteeing the data transmission security by adopting an encryption technology; the processing module is used for sorting and processing the multi-source data and converting the multi-source data into processing data in a standardized data format; and the model module is used for processing data and applying a model fusing a physical principle and a machine learning algorithm. An advanced early warning model is constructed by applying an artificial intelligence technology and fusing a physical principle and a machine learning algorithm, a deep learning optimization unit customizes a strategy for geological disaster images and time sequence data characteristics, and a knowledge graph unit introduces geological domain knowledge to assist modeling.

The invention discloses a coal mine geological disaster and environment early warning method based on multi-source monitoring data fusion. The method comprises the following steps: determining a mine water disaster early warning evaluation index, a mine pressure disaster early warning evaluation index and a geological environment evolution early warning index; a corresponding first early warning threshold value is set; data are acquired and preprocessed, and a mine water disaster early warning model, a mine pressure disaster early warning model and a geological environment evolution early warning model are established; collecting new data to carry out iterative optimization on the model, and correcting an early warning threshold value; and integrating the iteratively optimized models into an early warning index system library, collecting and fusing various monitoring system and production dynamic data of the mine, and inputting the data into the early warning index system library for coal mine geological disaster and environment early warning analysis. The method solves the problems that in the prior art, multi-source monitoring data cannot be fully fused, an early warning model lacks self-learning and optimization capabilities, and an early warning threshold cannot be dynamically adjusted along with the change of a mining environment.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster detection method and monitoring system based on unmanned aerial vehicle scanning. Comprising the following steps: S1, configuring an unmanned aerial vehicle-mounted tilt camera, a multispectral laser radar and a high-precision positioning module, planning a route, carrying out multi-angle scanning on a target area, and obtaining earth surface three-dimensional point cloud data, a multispectral image and terrain elevation information; s2, preprocessing the collected original image data, including point cloud denoising, image distortion correction and multi-source data registration, and generating a high-resolution live-action model fused with three-dimensional geographic information data; s3, extracting various data based on the live-action three-dimensional model to construct a geological disaster hidden danger analysis model, calculating a risk index through multi-factor weighted fusion, training a transfer learning model in combination with historical disaster data, and outputting a hidden danger type and probability; according to the invention, the geological disaster type can be analyzed based on the geological condition and the targeted processing strategy can be specified.