485 results about "Slope stability" patented technology

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

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 relates to the technical field of geotechnical engineering safety monitoring, in particular to a river valley area bedding slope stability monitoring method and related products, and the method comprises the steps: building a three-dimensional geology-mechanics numerical model of a slope; acquiring monitoring data of the slope state in real time; dynamically correcting mechanical parameters in the three-dimensional geology-mechanical numerical model through inversion calculation; evaluating the stability of the slope and outputting early warning information; according to the method, multi-source monitoring data are fused into the numerical model in real time, dynamic inversion and tracking of internal mechanical parameters of the slope are achieved, comprehensive indexes such as the real-time safety coefficient, the main sliding direction deviation angle and the damage evolution rate are calculated, graded early warning is triggered according to the indexes, and the perspectiveness and reliability of early warning are improved.

The invention relates to the field of highway engineering safety monitoring, in particular to a highway slope stability image monitoring and risk early warning system which comprises an image acquisition module, a slope parameterization representation module, a multi-scale curvature flow analysis module, a Riemannian manifold learning module and a risk early warning module. Differential features are extracted by calculating a measurement tensor and a curvature tensor of the curved surface; constructing multi-scale representation of the slope curved surface by applying a curvature flow theory, and identifying deformation characteristics under different scales; according to the method, riemannian manifold learning and a geodesic convolutional network are utilized to extract advanced features, geological disaster precursors such as landslide, collapse and debris flow are accurately identified, the system has a self-adaptive monitoring frequency adjustment function, monitoring modes are automatically switched according to risk levels, the slope disaster risk is greatly reduced, and a powerful guarantee is provided for safe operation of roads.

The invention discloses a loess slope stability intelligent monitoring method and system, and the method comprises the steps: carrying out the regional differentiation modeling of multi-source data of a target region, and outputting slope change data; according to the period-by-period displacement time sequence data, a slope deformation trend is obtained through prediction based on an LSTM algorithm; and inputting the slope change data, the slope deformation trend and the meteorological rainfall data into a slope stability evaluation model to predict a safety coefficient, and if the safety coefficient is lower than a preset threshold value, issuing early warning information. According to the loess slope stability intelligent monitoring method and system provided by the invention, data such as gradient change and deformation trend are accurately extracted, multi-source data and meteorological rainfall data are fused by means of cross-modal attention, a random forest model is used for learning a multi-factor coupling rule, and finally multi-factor data prediction is integrated to obtain a safety coefficient for early warning. The problem of low early warning reliability caused by difficulty in accurately evaluating the slope stability by fusing multiple factors such as gradient, deformation and meteorological rainfall can be solved.

The invention provides a slope displacement monitoring data processing system based on an unmanned aerial vehicle laser radar, and relates to the technical field of data processing, and the system comprises the steps: carrying out the spatial interpolation processing of a topographic feature data set, and constructing a digital topographic surface model; the displacement field calculation module is used for performing iterative optimization based on a digital terrain surface model through spatial similarity analysis and fusion with a gradient descent algorithm, calculating a slope surface displacement vector field, identifying a potential sliding surface and a deformation abnormal region, and generating a displacement field calculation result; and the evaluation module is used for inputting a displacement field calculation result into a risk evaluation model and carrying out slope stability quantitative evaluation through a multi-source data fusion analysis platform. According to the invention, the practicability and operability of the monitoring result are improved.

The invention relates to the technical field of rainfall slope evaluation, in particular to a rainfall slope stability evaluation method based on a physical certainty coupling model. The method comprises the following steps: acquiring digital elevation model data, engineering geological survey data and historical rainfall data of a target area; extracting topographic features from the digital elevation model data to obtain basic topographic parameters; the method comprises the following steps: carrying out longitudinal layering division on a rock-soil body based on engineering geological survey data, and carrying out physical and mechanical parameter setting on each layered rock-soil body to obtain a rock-soil body layering parameter set; and performing frequency analysis on the historical rainfall data to obtain an extreme rainfall intensity sequence. According to the method, by fusing the multi-source data and the three-dimensional physical coupling model, dynamic and accurate rainfall induced slope stability evaluation and risk early warning are realized, and the prediction precision is effectively improved.

The invention belongs to the technical field of civil engineering, and discloses and relates to an open pit coal mine slope multi-physical field action large-scale simulation platform which is used for simulating slope stability tests under various working conditions. Comprising a base, a plurality of box bodies are arranged in the middle of the base from left to right, and a first foundation pit and a second foundation pit are arranged at the bottom of each box body; the base is provided with a box rotating shaft located between the first foundation pit and the second foundation pit. A plurality of lateral overturning oil cylinders are arranged in the first foundation pit; the bottom of the front end of each box body is hinged to the tops of at least two lateral overturning oil cylinders, and the middle of each box body is connected with a rotating shaft fixed to the base. Wherein one box body comprises a forward overturning oil cylinder, a forward overturning base and a forward overturning box body; and the forward overturning oil cylinder is used for driving the forward overturning box body to overturn towards the adjacent box body. The device can realize loading of the open pit coal mine slope under various working conditions, and is reasonable in structure, convenient to operate and high in test precision.

The invention provides a GNSS-based mountain area highway loess slope stability evaluation method and system, and relates to the technical field of loess slope stability evaluation.The method comprises the steps that firstly, a slope area is divided into grids, geologic feature data of each grid are obtained, super nodes are divided according to the geologic feature data, and the super nodes are divided into super nodes; monitoring points are determined, and the soil moisture content is monitored, so that the moisture content of the soil at different depths is analyzed; secondly, the original displacement is monitored through a GNSS, displacement correction is carried out based on the Fredld unsaturated soil shear strength theory, and the actual displacement is obtained; the influence of the accumulated rainfall on the soil body strength parameters is analyzed, and the soil body strength parameters are corrected in real time; coupling the corrected soil body strength parameter with the actual displacement, calculating a safety coefficient, and judging an early warning state according to the rainfall sensitivity coefficient; and finally, comprehensively judging the stability of the slope according to the early warning state. According to the method, through multi-parameter comprehensive analysis, the accuracy of slope stability evaluation is improved.

The embodiment of the invention discloses a hydrology-landslide coupling forecast and parameter optimization method, and the method comprises the steps: constructing a coupling model, the coupling model comprises a hydrology model and a landslide model, the hydrology model is used for simulating the spatial and temporal changes of water volume and energy flux in a watershed, and the landslide model is used for representing the stability of a slope; establishing an intelligent forecasting model of the coupling model through a machine learning algorithm; performing parameter optimization on the intelligent forecasting model by using an intelligent substitution model based on Gaussian process regression, and outputting an optimized parameter sample and a target function value thereof; the objective of simultaneously forecasting flood and landslide disasters in a research area can be achieved, and the precision and efficiency of model calculation are improved.

The invention provides a slope stability double reverse parameter inversion early warning method and system, and relates to the technical field of slope early warning, and the method comprises the steps: obtaining slope multi-source data, and carrying out the preprocessing of the slope multi-source data, and obtaining a slope parameter database; performing side slope potential sliding surface screening and numerical modeling based on the side slope parameter database to obtain a multi-dimensional data set of the side slope potential sliding surface; constructing a double-reverse neural network model based on the multi-dimensional data set of the potential sliding surfaces of the side slope, and obtaining a double-reverse neural network model of multi-sliding-surface risk early warning; and processing slope displacement data monitored in real time based on the double-reverse neural network model for multi-sliding-surface risk early warning to obtain a prediction result of slope stability, and performing real-time early warning based on the prediction result. According to the invention, the efficiency and precision of landslide early warning are improved, and an intelligent solution is provided for slope engineering under complex geological conditions.

The invention belongs to the technical field of geological disaster risk assessment, and particularly relates to a slope stability grade identification method and system based on multi-modal deep learning. Comprising the following steps: acquiring a remote sensing image and corresponding parameter data, and respectively preprocessing the remote sensing image and the corresponding parameter data to obtain a high-dimensional image feature vector and a parameter feature vector; performing dynamic fusion of multi-modal information on the high-dimensional image feature vector and the parameter feature vector through a modal-level gating and fine-grained weighting mechanism to obtain a fusion feature; and inputting the fusion features into a preset classifier to obtain probability distribution of slope stability levels, and outputting the class with the maximum probability as a stability level identification result. According to the method, through combined modeling of the image modality and the parameter modality and introduction of an improved gating attention mechanism in a feature fusion stage, a reliable explanatory basis can be provided while high-precision prediction is ensured, so that the engineering applicability and the popularization value of the model are enhanced.

The invention relates to the technical field of monitoring and intelligent decision analysis, and discloses a monitoring and intelligent decision system for the influence of holographic blasting vibration on slope stability, and the system comprises a data collection module, a data processing module, an intelligent decision module, and a feedback control module. The method comprises the following steps: dynamically deploying a three-dimensional vibration sensor array based on topographic features; collecting and preprocessing blasting vibration signals and geological data; fusing the physical model and the data driving model to predict vibration propagation; calculating a dynamic safety coefficient and generating a multi-objective optimization scheme; and outputting an early warning instruction and feeding back and adjusting the sensor network configuration. Through a three-dimensional dynamic monitoring network, real-time data processing, a hybrid drive prediction model, multi-target quantitative evaluation and hierarchical early warning regulation and control, the problems of incomplete sensor coverage, early warning delay, prediction deviation, evaluation deficiency and poor scene adaptability are solved, and accurate monitoring and intelligent decision making of blasting vibration influence are realized.

The invention discloses a geotechnical engineering slope stability real-time monitoring method and a geotechnical engineering slope stability real-time monitoring system, which are characterized in that a blind source separation technology combining independent component analysis and physical constraint is introduced, an original displacement time sequence and an environment temperature time sequence of a plurality of GNSS (Global Navigation Satellite System) measuring points are regarded as multi-channel mixed signals, and statistical independence among signal sources is utilized to monitor the stability of a slope in real time. Periodic environment noise, instrument random noise and drift and real slope deformation signals are effectively separated from the mixed observation signals; and then, through correlation verification with physical quantities such as the field environment temperature and the like, automatic calibration is performed on the separated source signals, and temperature effect source signals and long-term creep source signals with physical labels are accurately identified and extracted, so that accurate elimination of noise components and high-fidelity reconstruction of pure deformation signals are realized. Therefore, the accuracy of real-time monitoring of the slope stability of geotechnical engineering can be effectively improved, and a solid data basis and a decision basis are provided for early warning and prevention of slope disasters.

This application discloses a slope detection device and method based on distributed optical fiber sensing, comprising: an optical fiber measurement unit deployed inside the slope to measure the acceleration time series of multiple state parameters of the slope; a demodulator connected to the optical fiber measurement unit to receive the acceleration time series measured by the slope; and a terminal device connected to the demodulator to perform multi-domain coupled analysis on the acceleration time series, generate a feature cloud map of the slope, and perform sub-item detection of slope stability based on the feature cloud map and preset multi-domain feature parameter indicators to obtain the sub-item detection results of the slope state, thereby generating detection results of the slope's dynamic characteristics and deformation characteristics. This allows for a comprehensive, accurate, and efficient identification of the slope's health status by integrating the multi-domain analysis results, improving the reliability of slope stability health detection and diagnosis, as well as the accuracy and efficiency of distributed optical fiber measurement.

The invention discloses a slope monitoring data calibration and three-dimensional point cloud matching method, belongs to the field of monitoring data calibration, and aims to improve orientation errors caused by the fact that global satellite positioning and navigation system measurement endpoint errors and equipment length problems are not fully considered in existing slope radar measurement. The method solves the problem of position deviation of a monitoring result caused by the fact that the position of a deformation area is monitored, breaks through the limitation of insufficient precision when a traditional radar image is converted into geographic space coordinates, improves the accuracy and reliability of data, remarkably improves the position precision of slope radar monitoring data, and enables the position monitoring of the deformation area to be more accurate. The high-precision requirement of slope stability monitoring is met, reliable data support is provided for fine monitoring of slope stability, potential safety hazards can be found in time, and corresponding measures can be taken.

The invention discloses a geotechnical engineering catastrophe dynamic real-time intelligent early warning method and system based on displacement monitoring, and the method comprises the steps: collecting the real-time displacement data and related parameters of a geotechnical engineering region through displacement monitoring equipment, and transmitting the real-time displacement data and related parameters to a bank slope stability evolution analysis platform to establish a multi-dimensional database; calling a fractured rock mass effective stress analysis model to calculate rock mass effective stress distribution characteristics, and inputting a multi-grid sliding surface stress calculation algorithm to obtain a potential sliding surface stress state and a critical threshold value; inputting the sliding surface stress parameters and the displacement data into a multi-field coupling analysis learning prediction model to obtain a displacement evolution trend and a catastrophe risk coefficient; and finally, real-time early warning information is generated and fed back through the catastrophe dynamic real-time intelligent early warning module. The method improves the real-time performance and accuracy of early warning, can effectively guarantee the safety of geotechnical engineering, and is suitable for catastrophe early warning scenes of geotechnical engineering such as bank slopes and foundation pits.

The invention discloses a post-raining side slope seepage characteristic testing method based on waveform inversion of nano-iron concentration distribution, and belongs to the field of seepage characteristic determination in foundation pit side slope stability monitoring, the method comprises the following steps: spraying a mixture of nano-iron particles and a surfactant into cumulonimbus to make nano-iron permeate into a side slope soil body along with rainfall; a radio frequency coil is arranged in the orthogonal direction of the selected section of the slope, an electromagnetic field is applied to excite nano iron resonance, and electromagnetic wave signals are collected; dividing the selected section of the slope into a plurality of grid units, and inverting the concentration distribution of the nano-iron in each grid unit by using an iterative optimization algorithm based on the electromagnetic wave signal to obtain an inversion result; and generating a seepage cloud picture based on the inversion result and calculating the seepage speed. According to the method, economic loss caused by large-area laying of nano-iron and interference to follow-up monitoring are avoided, the seepage position and size are determined, and complete and clear information is provided for monitoring seepage changes in slope engineering.

The invention discloses a backwater side seepage drainage and pressure reduction anti-seepage method based on a built sandy soil embankment foundation, and relates to the field of hydraulic engineering embankment seepage control. The technical problems that in existing sandy soil embankment foundation seepage drainage design, due to dynamic change of soil layer permeability, the drainage effect is poor, and slope stability is difficult to guarantee are solved. The method comprises the following steps: establishing a seepage field model through a three-stage hydraulic fracture test, and measuring a permeability coefficient, critical pressure and a failure criterion; the groove is dynamically excavated in three stages, the excavation depth and the slope gradient are corrected in real time according to actually measured data, and the shape of the groove is verified through a model; and finally, laying an inverted filter layer, a drainage body and an anti-scouring layer, wherein the permeability coefficient of the inverted filter layer and the aperture ratio of the drainage body are dynamically adjusted according to actually measured data. According to the method, accurate control over seepage drainage and pressure reduction of the sandy soil embankment foundation is achieved through dynamic coupling of testing, construction and a numerical model, and the method is mainly used for seepage treatment of the backwater side of a built embankment and slope stabilization and reinforcement.

The invention discloses an accurate monitoring system for linear deformation of a loess tunnel portal side upward slope, and relates to the technical field of geotechnical engineering and surveying and mapping. The system comprises a sensing network layer, a data acquisition and transmission layer, a cloud platform and data analysis layer and an application display layer, displacement data of discrete monitoring points are reconstructed into continuous full-field displacement and strain distribution through Kriging interpolation, then a displacement vector field is regarded as a whole to be analyzed, the maximum shear strain field of the displacement vector field is calculated, an abnormal area with the deformation gradient abrupt change is accurately positioned, and the deformation gradient abrupt change accuracy is improved. The abnormal point sets are merged into independent clusters according to spatial proximity and deformation consistency, and the geometric trajectory of a potential slip crack surface or crack is accurately outlined through curve fitting, so that the technical path fundamentally changes the limitation of point-mode monitoring, and can automatically identify a hidden linear failure trend which is difficult to find by naked eyes in a non-contact manner, so as to improve the detection accuracy of the hidden linear failure trend. And refined data support is provided for slope stability evaluation.

The invention provides a method and device for evaluating the stability of a soil slope in a man-made forest area and electronic equipment, and relates to the technical field of slope protection. In the method, a multi-field coupling numerical model not only considers a hydrological effect and a mechanical effect, but also considers the comprehensive effect of the hydrological effect and the mechanical effect; and the comprehensive influence of the artificial forest on the slope stability under the real environment load can be accurately quantified and verified.

The invention relates to a photovoltaic land slope stability partition evaluation method, which comprises the following steps that: an unmanned aerial vehicle flies in an oblique photography manner according to a path planning algorithm, and acquires an image and point cloud data of a slope region; correcting the collected images, including distortion removal, noise processing, image splicing, denoising, filtering and point cloud splicing of the point cloud data; creating a real three-dimensional terrain model by combining the texture information of the image and the height data of the point cloud, and extracting terrain height information from the point cloud data to generate a digital elevation model; performing feature extraction on the three-dimensional terrain model, and analyzing a stability index of the slope; establishing a slope stability evaluation model and performing zoning evaluation, performing stability zoning evaluation according to the feature data and the clustering result, and optimizing a zoning standard; and visualizing a slope stability partition result, generating a stability partition map and a report, establishing a dynamic monitoring system, and regularly using the unmanned aerial vehicle to repeatedly fly to obtain updated slope data.

The invention provides a slope stability prediction method and device under a rainfall condition, and relates to the technical field of slope stability prediction, and the method comprises the steps: employing a regular grid to divide a slope, and constructing a spatial-temporal feature data set containing geological, hydrological and topographic parameters; calculating a regional cooperation index based on the multi-dimensional feature vector, and dividing the slope into a plurality of communities and isolated risk areas; calculating the dynamic safety coefficient of each community by taking the mass center sub-region as a representative, and dividing the stability level according to a threshold value; and establishing a three-dimensional digital twinborn model to realize risk visualization, triggering early warning for a continuous three-period safety coefficient decreasing area, and displaying an evolution track. According to the method, accurate monitoring and early warning of slope stability are realized through multi-source data fusion and dynamic partition evaluation.

The invention provides a field slope stability monitoring method based on acoustic emission, and belongs to the technical field of field slope stability monitoring. In order to solve the technical problems of large soil mass fracture signal attenuation, strong noise interference and poor real-time performance of traditional monitoring in the existing loess slope instability monitoring, the adopted technical scheme is as follows: an inclined hole channel is drilled in the main sliding direction of a slope body by adopting a hydraulic guide drilling machine; vertically inserting the manufactured passive waveguide device from the top surface of the slope top, burying the manufactured active waveguide device into the drilled inclined hole channel, and burying the surface of a slope body by using an in-situ soil body; the acoustic emission probes of the passive waveguide devices and the active waveguide devices are connected with a collection device through signal lines, and the acoustic emission probes send induced acoustic emission signals to the collection device; the acquisition device receives the acoustic emission signals, then preprocesses the acoustic emission signals, extracts time-frequency domain features, and transmits the time-frequency domain features to the cloud system through a wireless network; the method is applied to field slope stability monitoring.

The invention relates to the field of ecological restoration, and provides a method for breeding rumex japonicus on a dry and warm valley slope. The method comprises the following steps: (1) leveling a field; (2) covering the slope with soil; (3) improving the soil in situ; (4) excavating a reverse horizontal step; (5) constructing a flexible ecological water and fertilizer bin; (6) dibble seeding of hairy morinda herb seeds; (7) covering with a non-woven fabric; and (8) maintenance management. The method is suitable for a soil slope, a soil-rock mixed slope and a debris flow slope with the slope ratio not larger than 1: 0.5. Compared with a traditional method, the method has the advantages that the construction process is simple, construction is convenient, and the construction speed is high; the matrix structure is high in stability, the matrix can be uniformly attached to the side slope, and the water and soil loss resistance is very high; disturbance to the side slope is small, the bearing load of the side slope is small, and stability of the side slope is better facilitated; the plant survival rate is high and the recovery effect is good; the applicability is wide; the construction cost is low; the method can be widely applied to slope greening, road greening and mine restoration in the Tibet dry and warm valley areas.

The invention relates to an embankment slope stability assessment method based on artificial intelligence, and belongs to the technical field of embankment slope monitoring and assessment. The method comprises the following steps: acquiring and marking embankment slope stress sensing data; dividing the data into a plurality of spatio-temporal data blocks; constructing a stability evaluation model; extracting multi-scale convolution features by adopting multi-scale cavity convolution of a high-frequency channel and pooling-deconvolution operation of a low-frequency channel to obtain a fused multi-scale feature matrix; a hidden state sequence is obtained through a space attention mechanism and a double-door-setting mechanism; calculating time interval saliency based on the hidden state vector, then calculating a weighted feature vector, further obtaining a weighted feature matrix, and processing through deep convolution and point-by-point convolution to obtain a pooling feature vector; carrying out stability evaluation grade classification through learnable category prototype and gating feature transformation; and dynamically adjusting sample weight and constraint attention distribution by adopting a total loss function. According to the method, the progressive instability identification capability can be improved.

The invention provides a method for analyzing deformation characteristics of an exit slope of a flood discharge building, which comprises the following steps of: firstly, collecting geotechnical parameters, hydraulic load parameters, GNSS displacement, pore water pressure and clinometer data of the slope; then, constitutive parameters of the slope stability model are dynamically corrected, GNSS displacement is decomposed into creep and elastic displacement components, and the time delay error of pore water pressure data is compensated; thirdly, seepage-stress coupling stability calculation is conducted in combination with the compensated pore water pressure and inclinometer data, and the slope stability safety coefficient is obtained. And finally, outputting the creep displacement, the elastic displacement and the stability safety coefficient to an early warning terminal. According to the invention, the accuracy and reliability of slope stability evaluation can be improved.

The invention provides a mountainous area expressway loess slope stability monitoring system based on GNSS, and relates to the technical field of slope stability monitoring, the mountainous area expressway loess slope stability monitoring system comprises a data acquisition module, a characteristic data construction module, a prediction model construction module, a simulation module, a judgment module and the like, and adjacent monitoring points are determined through spatial position and geologic structure combined constraint; dynamically optimizing the incidence relation in combination with test and displacement correlation; the method comprises the following steps: standardizing multi-source data such as weather and traffic, constructing characteristic data of environment-slope interaction, and realizing dynamic prediction of displacement and displacement difference by using an LSTM model; and accurately distinguishing and isolating high risks and regional risks by combining spatial distribution characteristics based on comparison of a difference value between a minimum value and a second minimum value and a threshold value. The problems of monitoring point misassociation, insufficient data utilization, extensive risk assessment and the like are effectively solved, and the accuracy of slope stability monitoring and the early warning reliability are remarkably improved.

The invention discloses a landslide risk early warning method, device and equipment based on multi-source data fusion, relates to the technical field of natural disaster monitoring, and aims to solve the problem of low precision and timeliness of landslide risk early warning. The method comprises the following steps: collecting multi-source monitoring data of a target area in real time and constructing a multi-source parameter set, wherein the multi-source parameter set comprises rainfall characteristic data, soil hydrogeological parameters and slope morphological parameters; performing space-time discretization moisture migration simulation based on the multi-source parameter set, and generating space-time distribution data reflecting the dynamic change of the slope moisture field; calculating a slope stability index sequence according to the spatial and temporal distribution data to construct a prediction model reflecting a risk evolution trend; and setting a dynamic grading early warning threshold based on a prediction model output result, and triggering multi-grade early warning information when the risk grade reaches a preset condition.

The utility model relates to the technical field of side slope monitoring equipment, in particular to a foundation pit side slope stability dynamic monitoring device which comprises a hollow anchor rod embedded into a side slope, an anchor plate penetrates through the free end of the anchor rod and abuts against the surface of the side slope, and a self-locking nut and a fastening nut are installed at the free end of the anchor rod in a threaded mode. A detection rail is fixed to the fastening nut, a pointer is slidably installed on the detection rail, a distance sensor is arranged on the detection rail, a steel wire rope is fixed between the pointer and the anchoring end of the anchor rod, after the anchor rod is embedded into a side slope, the side slope is reinforced, meanwhile, when the side slope settles, landslide impact can be relieved, and when the side slope settles, the anchor rod is bent and deformed, and the side slope is prevented from falling off. The steel wire rope pulls the pointer, the distance between the pointer and the spacing sensor is increased, a signal is sent to the monitoring assembly, the monitoring assembly collects the collected side slope settlement information and then transmits the information to a monitoring terminal of a base, the settlement change in the side slope can be remotely monitored, and the monitoring information is more accurate and more visual.