2705 results about "Earth surface" patented technology

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

The invention provides a water and soil loss dynamic monitoring method and system based on a remote sensing image, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image data set of a target region, which comprises a plurality of time period remote sensing image subsets and land surface coverage information, and then carrying out the land surface feature extraction of the multi-temporal remote sensing image data set, after vegetation coverage, terrain gradient and soil exposure features are obtained, a water and soil loss prediction model based on time-space correlation is constructed, the features are input for prediction, a water and soil loss grade distribution map is generated, a target loss risk area is identified according to the water and soil loss grade distribution map, and a treatment priority sequence and a vegetation recovery strategy are generated. And finally, the information is fed back to a monitoring platform to trigger regional governance task allocation operation, so that dynamic monitoring of water and soil loss is realized, and governance tasks are effectively planned.

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 relates to the technical field of reservoir earth and rockfill dam leakage abnormity safety monitoring and early warning, in particular to an earth and rockfill dam leakage abnormity real-time monitoring and early warning system based on deep learning and a medium. The system comprises a data sensing transmission module, a data fusion processing and analysis module, an early warning evaluation module, a system management and maintenance module, a database management module and an emergency response command module. Through a well-ground collaborative full-dimensional electrical method and shallow earth surface and full-section distributed optical fiber sensing, the system collects and transmits multi-source data. And multi-mode fusion and a deep learning algorithm are adopted to realize multi-physical field feature extraction and three-dimensional modeling. The system generates graded early warning information based on dynamic threshold and multi-factor coupling, and realizes automatic real-time monitoring, intelligent early warning and efficient management of leakage abnormity of the earth and rockfill dam in combination with a database, management maintenance and emergency response functions. According to the invention, the accuracy of earth and rockfill dam leakage abnormity identification and the intelligent level of early warning are improved.

The invention relates to the field of hydrology and water resource management and karst hydrodynamics, and discloses a karst basin coupling hydrology model prediction method, which comprises the following steps: S1, collecting and standardizing multi-source heterogeneous monitoring data of a karst basin; s2, constructing a hydrological model for dynamic coupling of surface runoff, fracture seepage and a pipeline network; s3, performing global optimization on the multi-dimensional hydrological parameters by adopting a hybrid intelligent optimization method; s4, performing iterative correction on the model based on a multi-level verification system; and S5, outputting a hydrological prediction result and generating disaster early warning information. By adopting a dynamic adjusting technology of karst area underground river and surface water flow network joint modeling, multi-karst water-containing medium characteristic analysis and integration of underground river and surface water system response characteristics, the problems that a traditional method cannot accurately predict underground water flow and flow velocity, and is difficult to deal with complexity of a karst water system, lack of data and the like are solved.

The invention provides an earth surface anomaly monitoring data fusion system based on vibration signal processing, and relates to the technical field of earthquake precursor monitoring, and the system comprises a data collection module which is used for carrying out the real-time monitoring of the earth surface through a distributed multi-parameter sensor network, and obtaining the time sequence monitoring data related to the earthquake precursor; the signal processing module is used for processing the time sequence monitoring data by adopting a two-parameter joint optimization algorithm and outputting a separation signal component containing precursor information; the data fusion and state inversion module is used for carrying out fusion processing on the separated signal components by adopting a dual-time scale analysis framework and outputting a multi-scale precursor state estimation result and an uncertainty quantitative index; and the monitoring and early warning module is used for outputting earthquake precursor grading early warning information through a three-layer anomaly recognition architecture and probabilistic risk assessment. According to the invention, high-precision separation and identification of earthquake precursor related signals can be realized, and the false alarm phenomenon is reduced through an intelligent early warning mechanism.

The invention relates to a terrain real scene modeling method, device and equipment and a storage medium thereof. According to the method, multi-source heterogeneous data related to the terrain in a target area is integrated, a multi-modal data cube is generated through noise filtering and data fusion under a unified coordinate system, then a data cavity area is identified based on density clustering and elevation gradient analysis, and geophysical features are extracted. Innovatively constructing a neural implicit inference model fused with physical constraints to perform joint modeling on the earth surface and underground structures, and finally converting implicit terrain representation into an explicit three-dimensional terrain model; seamless fusion modeling of the earth surface and the underground space under the complex shielding environment is achieved, the physical rationality of the model and the hidden area reconstruction precision are remarkably improved, meanwhile, it is ensured that the generated model strictly conforms to the geological physical law, and the geological engineering safety and the planning reliability are effectively guaranteed.

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.

The invention relates to the technical field of map data acquisition, in particular to a surveying and mapping geographic information map data acquisition method and system, and the method comprises the steps: building a space-time reference network: generating an underground-earth surface space-time reference network for connecting the underground with the earth surface through acousto-optic joint calibration; generating a multi-source correction parameter: fusing the underground inertial navigation data, the surface atmospheric refraction parameter and the equipment temperature drift coefficient, and calculating a multi-source correction parameter set for coordinate system conversion and error compensation; space-time evolution element collection: calibrating a mobile collection device in real time by using the multi-source correction parameter set, synchronously obtaining underground pipeline point cloud, an earth surface building image and a construction machinery track, and generating a space-time evolution element body with a space-time version label; and dynamic topology evolution modeling: analyzing an element change sequence in the spatio-temporal evolution element body, and constructing a dynamic evolution topology model. According to the method, differential analysis and a conflict backtracking strategy are combined, so that the adaptive updating and decision support capability of the map data in a construction disturbance scene is improved.

The invention provides a tunnel excavation ground surface settlement prediction method and system based on a machine learning hybrid model, and relates to the technical field of tunnel engineering and machine learning crossing, and the method comprises the steps: obtaining the multi-source heterogeneous information of a target tunnel, and constructing a ground surface settlement data set; a Transform-BiLSTM hybrid model is constructed, the robustness of the algorithm in a noise environment is enhanced based on a VMD (variational mode decomposition) algorithm, hyper-parameters are adaptively adjusted and optimized by using a PSO (particle swarm optimization) algorithm based on a ground surface settlement data set, the model prediction precision is maximized, and a ground surface settlement prediction model is obtained; and analyzing decision logic of the ground surface settlement prediction model through the SHAP value, and outputting interpretable engineering guidance suggestions. By constructing a machine learning hybrid model, high-precision and real-time prediction of ground surface settlement in the whole process of tunnel excavation is realized. The precision and generalization ability of the model are improved, the characterization ability of complex spatial-temporal characteristics is enhanced, and overfitting is avoided; and the interpretability is optimized, and the influence of key parameters on a prediction result is quantified, so that construction parameter adjustment is guided.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention discloses a mine reclamation effect evaluation method based on a multi-temporal remote sensing image, and belongs to the field of mine reclamation effect evaluation, and the method comprises the steps: obtaining and preprocessing multi-source remote sensing data, and obtaining a standardized multi-temporal data set; performing image registration and radiation correction on the standardized multi-temporal data set to obtain a correction data set with consistent space and consistent radiation; constructing a multi-dimensional feature data set based on the correction data set, wherein the multi-dimensional feature data set comprises surface morphological features, vegetation health features, soil temperature features and underground water level change features; extracting a surface form feature vector according to the multi-dimensional feature data set, and generating a vegetation health distribution map, a soil temperature feature map and an underground water level change distribution map; and fusing the surface form feature vector, the vegetation health distribution map, the soil temperature feature map and the underground water level change distribution map, constructing a comprehensive index system, and outputting a mine reclamation effect evaluation result. According to the method, the accuracy and comprehensiveness of reclamation effect evaluation are remarkably improved.

The invention discloses an underground-ground-sky combined rock burst and earth surface deformation early warning method, which comprises the following steps of: S1, arranging an underground monitoring system, and acquiring underground monitoring data; s2, setting a ground monitoring system, and collecting ground monitoring data; s3, setting a sky monitoring system, and collecting sky monitoring data; s4, calibrating the time scale difference of the underground monitoring data, the ground monitoring data and the sky monitoring data through a multi-scale time alignment algorithm; and S5, constructing a multi-stage joint probability assessment framework to carry out mining area rock burst and earth surface deformation risk assessment. According to the method, multi-source monitoring data are integrated through a multi-scale space-time fusion framework, and a full-chain association model of'microcosmic fracture-mesoscopic deformation-macroscopic accumulation 'is constructed. And on the basis of cross-scale time sequence alignment, spatial heterogeneity is optimized and eliminated, a multi-stage joint probability assessment framework quantifies a cross-scale collaborative risk, full-period intelligent management and control of'monitoring-early warning-feedback-optimization 'is realized, and early warning accuracy and timeliness are improved.

The invention discloses a self-adaptive InSAR-GNSS high-precision three-dimensional deformation resolving method with regularization introduced twice, and the method comprises the steps: obtaining InSAR deformation data and GNSS three-dimensional deformation data of a to-be-monitored region, and building an InSAR error correction model considering spatial correlation based on the InSAR deformation data and the GNSS three-dimensional deformation data; performing system error correction on the InSAR deformation data by using the corrected error correction model to obtain preliminary correction data; grouping the preliminary correction data according to data precision characteristics and determining an initial weight; iteratively calculating until unit weight variances of each group of observation data are approximately equal to obtain a final weight; and obtaining a high-precision earth surface three-dimensional deformation rate based on the final weight. According to the method, Tikhonov regularization is introduced twice, the weight is dynamically adjusted in combination with the IGGIII equivalent weight function, and various error influences are effectively reduced.

The invention discloses an earthquake prediction method and system based on multi-physics coupling and quantum optimization, and relates to the technical field of earthquake prediction, and the method comprises the following steps: obtaining earth surface deformation observation data, geomagnetic observation data, gravity gradient data, and infrasonic wave monitoring data, and carrying out the preprocessing; performing multi-physics field coupling calculation on the preprocessed earth surface deformation observation data, geomagnetic observation data, gravity gradient data and infrasonic wave monitoring data; optimizing the fault network by adopting a D-Wave quantum annealing technology to find a lowest energy state to obtain a quantum optimization result; and generating an earthquake probability distribution diagram, an earthquake magnitude-time window table and a fault stress field three-dimensional model, thereby greatly expanding the breadth and depth of data, and remarkably improving the comprehensiveness of earthquake prediction.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

The invention relates to the technical field of image data processing, in particular to a difficult mountainous area slope three-dimensional modeling method and system based on a remote sensing technology. The method comprises the following steps: obtaining an original remote sensing data set of a target hard mountain area, and carrying out data preprocessing to obtain a standardized remote sensing data set; performing target hard mountainous area surface feature extraction on the standardized remote sensing data set to obtain hard mountainous area surface feature data; constructing a three-dimensional terrain model by using the earth surface feature data of the hard mountain area to obtain preliminary three-dimensional terrain model data; and performing time sequence analysis on the standardized remote sensing data set, and performing slope dynamic change analysis on the initial three-dimensional terrain model data by using a time sequence analysis result to obtain slope displacement change data. Through three-dimensional modeling, dynamic change analysis, stability evaluation and landslide early warning, a set of complete disaster prevention and reduction system for the hard mountainous area is constructed, and the prediction and prevention capability for landslide disasters is effectively improved.

The invention discloses a method for evaluating the influence of black land ecological barrier pattern evolution on agricultural climate and yield, and relates to the technical field of agricultural ecological environment monitoring and remote sensing model coupling. Comprising the following steps: S1, acquiring a high-resolution satellite image, microwave remote sensing earth surface humidity data, optical remote sensing vegetation coverage data and thermal infrared remote sensing earth surface temperature data of a target area; according to the method for evaluating the influence of the black land ecological barrier pattern evolution on the agricultural climate and the yield, the problem of lack of ecological-climate-agricultural system collaborative modeling in the traditional technology is solved. A high-resolution satellite image is used for analyzing a network topology structure of the protection forest, microwave and thermal infrared remote sensing are fused to invert'water, soil and gas generation 'multi-element parameters, and dynamic quantitative evaluation of ecological barrier spatial heterogeneity evolution on local climate and crop growth is realized in combination with a dynamic weight distribution and two-way nesting technology.

The invention belongs to the field of geotechnical engineering and engineering geological information modeling, and relates to a stratum-parameter coupling randomness three-dimensional random field modeling and shield construction ground surface settlement rapid prediction method and device based on multi-source data fusion. According to the method, a three-dimensional joint random field acting on stratum types and rock-soil key physical property parameters at the same time can be constructed under the joint constraint of multi-source data such as drilling, geophysical prospecting, geotechnical tests and terrains, and a calibrated, explainable and updatable geological section automatic generation and uncertainty quantification channel is formed; on the premise that geological rationality is guaranteed, section geometry and parameter distribution under the conditions of complex structures and lateral phase change are restored robustly, the accuracy, consistency and interpretability of the three-dimensional geological section are remarkably improved, and a combined uncertainty result which can be directly used for rapid prediction and risk assessment of shield construction settlement is output. The technical problems that a geological section generated by an existing method is not accurate enough, uncertainty is difficult to quantify, and the extrapolation capacity of a complex scene is weak are solved.

The invention provides a shield tunnel dynamic settlement compensation construction method based on an adaptive optimization algorithm, and the method comprises the steps: collecting the ground surface settlement, soil stress and underground water level data in real time through an Internet of Things sensor, achieving the data preprocessing and feature extraction in combination with an edge calculation node, constructing a three-dimensional geologic model, integrating the historical engineering data through transfer learning, and achieving the dynamic settlement compensation of a shield tunnel. The method comprises the following steps: identifying a high-risk area by using a clustering algorithm, designing a hybrid adaptive optimization framework with fusion of random forest and incremental learning, dynamically adjusting shield tunneling speed and soil bin pressure construction parameters, introducing an adaptive step length mechanism to cope with geological complexity change, and identifying a settlement abnormal mode through Fourier transform. Precise compensation is achieved in combination with a layered grouting strategy, the pressure of a soil bin is dynamically adjusted based on a hydraulic system, a closed-loop feedback mechanism is established, the predicted deviation rate is compared with an actual monitoring value, model parameters and the compensation strategy are continuously optimized, the settlement control precision is improved, and the construction risk is reduced.

The invention relates to the technical field of geological monitoring, and discloses a self-adaptive phase unwrapping foundation SAR deformation monitoring method and system. The method comprises the steps of collecting ground-based SAR echo data, extracting phase gradient features and noise distribution, constructing a model based on a compressed sensing theory to solve a phase unwrapping result, establishing a deformation field dynamic evolution model fusing multiple factors, and generating a deformation monitoring value through an adaptive weighted fusion algorithm. And an abnormal deformation detection model is also constructed to realize adaptive re-calibration of model parameters, and a multi-sensor collaborative calibration network is constructed to improve the monitoring precision. The problem of phase unwrapping is effectively solved, multiple influence factors are comprehensively considered, surface deformation can be accurately monitored in real time, abnormity can be timely found, the model can be calibrated, multi-sensor data can be fused, and the method has important application value in the fields of geological disaster early warning, engineering structure safety assessment and the like.

The invention discloses an urban inland inundation optimization transformation method and system based on a pipe network earth surface coupling hydrodynamic model, and the method comprises the steps: firstly generalizing a pipeline and a catch basin, constructing a pipe network confluence model according to a pipe network hydrodynamic method, then dividing a sub-catchment area according to a river network flood routing method, building an urban comprehensive drainage model through the sub-catchment area, and carrying out the optimization transformation of the urban inland inundation. Then establishing a surface runoff model by using a surface flood routing method and a Horton penetration method; according to the system, the function of accurately and efficiently simulating the urban inland inundation process is achieved, urban rainwater circulation is completely simulated, an urban drainage pipe network and a river channel model are integrated, and the interaction between an urban drainage system and a ground surface storage water body is truly simulated; and necessary support is provided for disaster situation rehearsal and rainwater engineering planning and layout, so that urban inland inundation points can be removed in advance, influences on production and life of people are avoided, and the method is suitable for being widely popularized and used.

The invention discloses an urban land subsidence analysis method and system based on multi-source data fusion, relates to the technical field of subsidence analysis, and constructs a multi-source three-dimensional monitoring network, performs air-space-ground multi-source data fusion analysis and atmospheric delay error correction, constructs a land subsidence prediction model, and provides an urban land subsidence analysis method and system based on multi-source data fusion. According to the method, a ground surface and underground three-dimensional monitoring network is constructed, the level, layered settlement and underground water data are integrated, minute-level global monitoring is achieved in combination with the Internet of Things, and the method is high in practicability and high in practicability. Through air-sky-ground data fusion and atmospheric correction, monitoring errors are reduced to a millimeter level, an evaluation system combining subjective and objective weights generates a five-level risk map, a visual platform supports dynamic simulation and factor traceability, a whole-process technical closed loop is formed, and the intelligent level of urban settlement prevention and control is remarkably improved.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention provides a reservoir bank slope deformation body instability volume prediction method, which comprises the following steps: respectively acquiring earth surface displacement and rock mass internal deformation data through a millimeter wave radar and a tilt angle sensor, and after processing through an adaptive noise decomposition algorithm, identifying a key deformation area and generating a data set. And performing space-time alignment on the data by using the engineering coordinate system and the topological relation to generate a fusion matrix. And reconstructing a potential slip crack surface geometric model in combination with slip crack surface features of historical cases, and calculating instability volume probability distribution by adopting Monte Carlo simulation. And finally, inputting the multi-dimensional data into the space-time prediction model, and outputting an instability volume prediction result with probability distribution. According to the invention, the accuracy and reliability of the prediction result can be improved, and scientific basis and technical support are provided for safety monitoring and disaster early warning of the reservoir bank slope.

The invention relates to the technical field of coal mining safety monitoring, and discloses a coal mining subsidence space-time evolution intelligent prediction method, which comprises the following steps: acquiring multi-dimensional geological dynamic data such as surface displacement, underground water pressure and rock stratum stress of a coal mining area, extracting a space-time characteristic sequence by using a GCLSTM algorithm, and calculating a space-time fluctuation coefficient; extracting a principal component vector through spectral decomposition, mapping to obtain a space-time correlation weight, dynamically correcting an original prediction value to generate a fusion prediction value, and judging a subsidence space-time evolution trend; constructing a three-dimensional subsidence curved surface model to identify a high-risk area and generate graded early warning; and establishing a joint optimization model to solve an optimal mining path, and generating a subsidence risk minimization scheme. According to the method, intelligent analysis of multi-dimensional data fusion and spatial-temporal characteristics is realized, the prediction accuracy is improved, an effective means is provided for coal mining subsidence prevention and control and safe mining, and the method has a remarkable engineering application value.

The invention relates to the technical field of geophysical exploration, in particular to an exploration surveying and mapping method based on remote sensing and a surveying and mapping instrument thereof. The exploration surveying and mapping method based on remote sensing comprises the following steps: step 1, multi-platform collaborative data dynamic acquisition; step 2, multi-modal data fusion and feature enhancement: vegetation noise filtering is performed on the laser radar point cloud data, and a surface terrain model under vegetation coverage is reconstructed; seismic wave speed data and ground penetrating radar dielectric constant data are combined to construct an underground medium physical property combined distribution model; associating the reflectivity characteristics of the multispectral image with the underground physical property parameters, and identifying a geological abnormal region; and step 3, hidden geologic body dynamic modeling and risk assessment. By means of the method, real-time monitoring and risk assessment of the complex geologic body can be achieved, and the geological disaster prediction and prevention and control capacity is greatly improved.

The invention relates to the technical field of territorial space planning, and discloses a monitoring and evaluation method based on multi-source remote sensing images and geographic information vector data. Comprising the following steps: acquiring a multi-source remote sensing image and geographic information vector data of a target area; performing preprocessing and fusion analysis on the multi-source remote sensing image, generating a dynamic earth surface change detection model with high time resolution, and constructing an initial monitoring baseline in combination with geographic information vector data; according to the method, a high-frequency change detection condition and a semantic segmentation condition are loaded on the basis of an initial monitoring baseline to form a comprehensive interpretation model, a real-time early warning evaluation index is output, and a dynamic earth surface change detection model is generated by preprocessing a remote sensing image. And generating an illegal behavior distribution result by using the key identification parameters, establishing a task distribution model to optimize a supervision scheduling scheme, and finally realizing accurate management and control. According to the invention, the efficiency and accuracy of territorial space planning monitoring are improved, and technical support is provided for intelligent supervision.

The invention relates to a steep slope stability prediction method and system based on a manifold graph convolutional network, and belongs to the technical field of geological disaster monitoring and prediction. The method comprises the following steps: data acquisition and preprocessing; constructing a graph structure fused with multivariate features; constructing a manifold graph convolutional neural network model; model training; predicting the surface displacement of the steep slope; and according to the predicted earth surface displacement result, steep slope stability evaluation is carried out. The real-time state of the slope is reflected by integrating multi-source data such as geology and terrain; through multi-dimensional data fusion, key factors are captured, and prediction comprehensiveness and accuracy are improved. The method comprises the following steps: constructing a graph structure fusing multivariate features, and deeply mining spatial correlation and time dynamic change by using a manifold graph convolutional network and a Shenchang differential equation to realize deep fusion of spatio-temporal features. The model has good adaptability to slight deformation and complex geological conditions, has high robustness, and can be suitable for monitoring different types of slopes.

The invention discloses a metro shield tunnel crack width refined numerical simulation method under surface loading, and the method comprises the following specific steps: describing the overall stress deformation characteristics of a soil body-shield tunnel structure based on a stratum-structure method, and simulating a macroscopic equivalent model by adopting a continuous medium model, and the equivalent stiffness reduction effect caused by the influence of longitudinal seams and circular seams is fully considered, the most unfavorable section position along the shield tunnel is determined, so that the key section is replaced with a refined model considering local hand hole weakening, multi-contact and built-in steel bars, and the shield tunnel crack evolution characteristics are described by adopting a total strain crack model. And obtaining the millimeter-scale crack width of the target tunnel structure based on a load-structure method. According to the analysis method, the millimeter-scale crack width of the shield tunnel under the action of ground surface loading can be accurately obtained, the structure safety state can be rapidly evaluated in combination with current specifications, and an important basis is provided for operation and maintenance of the subway shield tunnel.