909 results about "Ground water level" patented technology

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 provides an underground water dynamic evolution prediction method, and belongs to the technical field of underground water prediction based on deep learning. Hydrological parameters and geographic position data of monitoring points are collected, and a regional hydrogeological input tensor is constructed; then establishing an initial finite element model to carry out space extrapolation and flow field completion on underground water level distribution; based on an initial finite element model structure, designing a global optimization algorithm based on target decomposition, carrying out real-time structure optimization on the finite element model, then simulating underground water evolution of a non-monitoring area through a finite element numerical solution, and generating multi-node physical consistency hydrological time series data after resolution is improved; and inputting the generated hydrological time series data into a designed perceptual water level prediction model, and outputting underground water level prediction results at multiple moments and multiple positions in the future and corresponding underground water level thermodynamic diagrams. According to the method, more accurate, continuous and interpretable high-precision prediction of the underground water system is realized, and reliable support is provided for scientific management of underground water resources.

The invention relates to the technical field of geotechnical engineering digitization, in particular to a BIM (Building Information Modeling)-based three-dimensional geotechnical engineering investigation information model construction method. The construction method comprises the following steps: acquiring multidimensional geotechnical engineering investigation data, acquiring geotechnical engineering investigation data from multiple sources in real time, including geological drilling position coordinates, soil sample attributes, underground water level dynamic values and geological structure maps, and executing data synchronization operation in the acquisition process to ensure timestamp consistency. According to the method, the technical bottleneck of static physical process in traditional modeling is fundamentally broken through the synergistic effect of a multi-physics field coupled rock and soil information generation mechanism and the dynamic response architecture of the BIM framework.

The invention provides a mine water disaster monitoring and early warning method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting multi-source data of a mining area, carrying out the time-space alignment of the multi-source data, and generating a data set, the multi-source data comprising remote sensing data; preprocessing the data, inputting the preprocessed data into a multi-modal fusion network, and extracting surface water body distribution, lithologic permeability, underground water level and structural fracture characteristics to obtain a three-dimensional hydrogeological static model; the hydrological numerical model based on physical driving is coupled with the static model, and the dynamic model is used for simulating the dynamic change of an underground water flow field and a pollutant diffusion path; based on the dynamic model updated in real time, multi-target collaborative evaluation is carried out to evaluate the mining area water resource, ecological and social collaborative effect; and calling an unmanned aerial vehicle to inspect a leakage position or a settlement position in the hydrogeological risk map based on a multi-target collaborative evaluation result. According to the method, the prediction accuracy is improved through the multi-source data.

The invention relates to the field of building settlement monitoring, and discloses a multi-source data-artificial intelligence fused building deviation rectification settlement prediction method and system, and the method comprises the following steps: multi-source data collection: obtaining multi-class sensor data related to building settlement, the multiple types of sensors comprise a level gauge, a total station, an underground water level sensor and a soil body pressure sensor, and multi-modal data input is formed; the invention also discloses a multi-source data-artificial intelligence fused building deviation rectification settlement prediction system. The system comprises a data acquisition module, a data processing module, a deep learning prediction module, a model optimization module, an intelligent early warning module and a user interaction module. According to the invention, by dynamically adjusting the settlement risk threshold and introducing a multi-stage early warning signal and an intelligent decision support system, accurate early warning and real-time decision support of the settlement risk are realized, and the adaptability, accuracy and response efficiency of the system are significantly improved.

The invention belongs to the technical field of disaster early warning systems, and particularly relates to a dynamic early warning method and system for urban inland inundation and secondary disasters based on multi-source data fusion, and the method comprises a data collection module which is used for collecting the related data of urban inland inundation and secondary disasters from multiple sources in real time, the data includes but is not limited to meteorological data, rainfall data, ground water level data, urban drainage system state data, historical waterlogging event data and geographic information data; the data preprocessing module is used for carrying out cleaning, denoising, missing value filling and normalization processing on the data; the data fusion module is used for fusing data from different data sources and analyzing the data through a multi-source data fusion algorithm to generate a comprehensive urban inland inundation risk assessment model; through multi-source data fusion, accurate prediction and precision of urban inland inundation and secondary disaster occurrence are improved, so that the system can identify disaster risks earlier and more accurately.

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 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 provides an urban groundwater inrush risk assessment method based on a three-dimensional grid model and an XGBoost algorithm, and belongs to the technical field of geological disaster early warning. The method comprises the following steps: acquiring geological, environmental and historical disaster data in real time through a multi-source heterogeneous data interface; constructing a heterogeneous grid unit based on three-dimensional geological modeling, and dynamically binding parameters such as underground water level, fault activity and lithologic permeability coefficient; an XGBoost algorithm is combined with second-order Taylor expansion to optimize gradient calculation, and a nonlinear risk assessment model is constructed through Bayesian hyper-parameter tuning and SHAP value feature interpretive analysis; identifying contiguous dangerous areas based on spatial autocorrelation analysis and an adaptive threshold segmentation algorithm; and a risk thermodynamic diagram, geological section superposition and real-time early warning are realized through a 3D GIS platform. According to the method, the problems that a traditional method depends on a linear model, data is single and local risk description is insufficient are solved, and the accuracy, efficiency and engineering practicability of risk assessment are remarkably improved.

The invention discloses a side slope stability intelligent monitoring and supporting optimization method based on digital twinning, which comprises the following steps: extracting gradient, curvature and geological layering characteristics from a holographic terrain model, calculating a preliminary change trend of stress distribution of a rock-soil body in combination with a finite element analysis method, and determining spatial position distribution of a potential sliding surface of a side slope; according to the space coordinate set of the real-time monitoring point position, the data acquisition frequency of the sensor is adjusted through a time sequence analysis method, and a high-frequency monitoring data set reflecting dynamic changes of stress and underground water is obtained; extracting stress peak values and underground water level fluctuation characteristics from the high-frequency monitoring data set, and generating a multi-dimensional information matrix containing terrain, stress and water dynamics by combining spatial characteristics of the holographic terrain model and adopting a data fusion algorithm; according to the state parameters of the digital twinborn body, if it is judged that the stress distribution exceeds a preset threshold value, the stress distribution of the supporting structure is recalculated through finite element analysis, and an adjusted slope supporting design scheme is obtained.

The invention discloses a multivariable underground water level time sequence prediction method and device, equipment and a medium, and relates to the technical field of artificial intelligence. Performing feature variable contribution proportion calculation, feature screening and normalization processing on the processed data based on a random forest regression model by using an average impurity reduction method to obtain target data; constructing a prediction model based on a convolutional neural network and a long-short term memory network, and performing structure setting, cross validation, parameter setting and model training on the prediction model to obtain a target prediction model; the method comprises the following steps: inputting target data into a target prediction model to obtain a predicted value, generating an evaluation index based on the predicted value and an actual measured value, optimizing the target prediction model by using the evaluation index, improving the applicability, generalization ability and training speed of the model, optimizing the convergence of the model, and improving the accuracy and stability of multivariable groundwater level time sequence prediction. And the sustainable utilization efficiency of underground water resources is improved.

The invention provides an intelligent early warning method and system for phosphorus tailing improved soil heavy metal migration, relates to the technical field of computers, and accurately predicts the main migration direction of heavy metal pollutants by combining factors such as terrain gradient, hydrological flow direction and soil structure heterogeneity. The method comprises the following steps: acquiring soil information of a to-be-detected area and soil potential information of a detection point in the to-be-detected area; determining heavy metal concentration information of the detection point according to the soil potential information; the heavy metal concentration information is used for reflecting heavy metal concentration distribution conditions corresponding to different soil depths of the detection points; determining migration direction information of the heavy metal at the detection point; the migration direction information is used for reflecting the corresponding heavy metal migration direction distribution condition of the heavy metal of the detection point in the three-dimensional space; and predicting a pollution diffusion area based on the migration direction information, the organic decomposition rate and the underground water level fluctuation information, and performing visual display.

The invention relates to the technical field of safety assessment of building construction, and discloses an online simulation analysis method for safety assessment of a foundation pit, which comprises the following steps: S1, data acquisition, cleaning and preprocessing: acquiring soil pressure, underground water level, structural deformation, temperature and humidity information around the foundation pit by deploying a plurality of sensors, the invention further provides an online simulation analysis system for foundation pit safety assessment, online simulation analysis equipment for foundation pit safety assessment and a computer readable storage medium. The deep reinforcement learning algorithm, the intelligent early warning mechanism and the distributed storage and encryption technology are introduced, real-time monitoring, accurate early warning and data safety guarantee of foundation pit safety assessment are achieved, and the safety, efficiency and reliability of the foundation pit construction process are improved.

The invention relates to the technical field of underground water monitoring, in particular to an underground water dynamic monitoring multi-element early warning system based on data analysis, which comprises a data fusion acquisition module, a dynamic geological modeling module, a deep learning prediction module, a multi-level intelligent early warning module and an intelligent response decision module. Wherein the data fusion acquisition module is used for acquiring multi-dimensional data; the dynamic geologic modeling module is used for constructing a three-dimensional dynamic model of an underground water aquifer; the deep learning prediction module predicts the future change trend of the underground water level and the water quality; the multilevel intelligent early warning module is used for establishing a multilevel early warning mechanism; and the intelligent response decision module formulates corresponding underground water management and emergency response strategies. According to the invention, through real-time integration of multi-source data and combination of intelligent early warning and automatic management, the accuracy and response speed of underground water monitoring are significantly improved, and sustainable management and protection of underground water resources are effectively supported.

The invention relates to the field of farmland ecological protection, in particular to a salinization farmland protection forest configuration optimization system and method, and the system comprises a salinization monitoring module, an edge calculation module, a database, a three-dimensional model module, a model optimization module and a configuration scheme optimization module. The salinization monitoring module acquires data through an unmanned aerial vehicle and a hyperspectral imaging spectrometer and determines a soil salinity distribution state, the edge calculation module receives the data and carries out protection forest configuration model calculation, the database stores a calculation model and soil salinity and vegetation growth state data, and the three-dimensional model module generates a three-dimensional spatial-temporal model of forest growth. The model optimization module utilizes deep reinforcement learning to optimize a soil salinity and underground water level simulation model, the protection forest configuration effect is improved, the configuration scheme optimization module generates an optimal protection forest configuration scheme, a complex interaction process is accurately represented, and an efficient and accurate technical means is provided for ecological protection of salinized farmland.

The invention discloses a karst development area geological disaster identification method and system based on artificial intelligence, relates to the field of geological disaster early warning and risk assessment, and solves the problems that it is difficult to integrate geology and related data, it is difficult to construct a model to predict disaster categories based on historical disaster cases and real-time monitoring data, and the risk assessment efficiency is improved. The technical problem of lack of association of a prediction result with a geographic space to generate a risk distribution map and dynamically update a high-risk area is solved; comprising the following steps: introducing multi-source heterogeneous data, and combining historical geological disaster cases; preprocessing the data; extracting underground water level change rate, soil humidity and vibration frequency characteristics; combining a plurality of features into a feature matrix, and projecting the feature matrix to a principal component space; after training the model by using a random forest algorithm, optimizing the constructed model by combining cross validation with a Bayesian algorithm; the model prediction result is converted into a risk level value through a mapping rule; and generating a risk level distribution map based on the GIS data.

The disclosure relates generally to methods and systems for ground water prediction using integration of satellite and auxiliary observations. Conventional techniques in the art for the ground water level prediction are not accurate and efficient for predicting the ground water level. The present disclosure combines time-series archived remote sensing data with a wide array of past auxiliary datasets specific to a given region for precise prediction of future ground water condition, to predict the ground water situation i.e. rising or declining for a given region. These encompass diverse information such as weather conditions, soil properties, agricultural data, population statistics, water resources information, industrial details, drought records, seismic data, and surface deformation patterns. The present disclosure also considers different future scenarios which can be built on future cropping patterns, forecasted weather conditions, use of irrigation system in future, and future population growth of the given region.

The invention relates to the field of safety supervision, and particularly discloses a deep foundation pit construction quality safety supervision system and method. Firstly, a pithead image, collected by a camera, of a deep foundation pit construction site, deep foundation pit side wall soil layer inclination displacement measurement text data collected by an inclinometer and underground water level values, collected by an underground water level monitoring device, at a plurality of preset time points are obtained, then feature extraction and correlation analysis are conducted on the three parts through the deep learning technology, and the deep foundation pit side wall soil layer inclination displacement measurement text data and the underground water level values are obtained. And finally, a classification result is obtained through a classifier to judge whether a deep foundation pit construction quality safety early warning is sent out or not, so that potential safety hazards are found in time, early warning is carried out in advance, and smooth proceeding and safety of engineering construction are guaranteed.

The invention discloses a method and system for predicting methane emission flux based on a data coupling model, and the method comprises the steps: downloading remote sensing, soil, hydrology, observation and meteorological data of a target wetland region, and carrying out the data preprocessing; the TECO model and the Wetland-DNDC model are coupled, and hourly underground water level data output by the TECO model are transmitted to the DNDC model; preprocessing the wetland soil data, and calculating the water content change of the soil profile; operating the coupling model, and simulating methane generation, oxidation and emission; based on actually measured data, assimilating and optimizing key parameters by adopting Bayesian data, quantifying uncertainty through Monte Carlo simulation, and performing methane emission flux prediction data; and finally, carrying out precision verification on a prediction result and an actual measurement result. According to the method, high-precision prediction of the wetland methane emission flux is realized through the coupling model.

The invention belongs to the technical field of tunnel engineering and hydrogeological engineering, and particularly relates to a tunnel underground water pressure-limiting discharge management system and method based on data driving. A tunnel underground water pressure-limiting discharge management system based on data driving comprises a monitoring module, a processing module, a control module and an execution module. The monitoring module is used for collecting contact pressure, water pressure, steel bar stress, steel arch internal force, lining displacement, steel bar strain, underground water level and water turbidity as multi-source monitoring data; the contact pressure comprises the contact pressure between the primary support and the secondary lining and the contact pressure between the primary support and the surrounding rock. And the processing module is used for dynamically adjusting the weight coefficient of each monitoring index through an adaptive particle swarm optimization algorithm according to the multi-source information, and calculating a comprehensive early warning index # imgabs0 #. According to the scheme, through multi-source data real-time monitoring and self-adaptive algorithm dynamic adjustment, accurate drainage early warning is achieved, the structural risk is reduced, and safety and reliability are improved.

The invention provides a mine slope risk assessment method and system based on multi-source data space-time fusion, and relates to the technical field of mine monitoring, and the method comprises the steps: obtaining geological and engineering data, and constructing a basic model representing a slope three-dimensional space structure; collecting multi-source heterogeneous monitoring data on a mine slope field, and aligning and fusing the multi-source heterogeneous monitoring data with the basic model in a space-time dimension to generate a continuous dynamic parameter field; and setting a data fusion model, and taking the basic model and the dynamic parameter field as input to obtain a four-dimensional space-time fusion risk field. According to the method, the basic model representing the three-dimensional space structure of the slope is constructed, and a unified space-time bearing base is provided for all heterogeneous monitoring data, so that originally isolated data such as GNSS displacement, deep displacement, blasting vibration and underground water level can be fused in a unified coordinate system; the problem of data splitting in the prior art is fundamentally solved, and integral and integrated expression of slope engineering and geological conditions is formed.

The invention discloses a slope early warning method and system based on a knowledge graph, and relates to the technical field of multi-source monitoring slope early warning, and the method comprises the steps: 1, unifying laser, radar, satellite remote sensing and underground water level monitoring flow into an index-structure-disaster situation graph containing traceability metadata; 2, generating an instability trigger coefficient by using a gradient lifting model driven by a coupling trend and credibility; step 3, automatically outputting short messages, broadcasts and social contact templates, and implanting abstract codes for publishing; 4, comparing the returned text in real time, marking deviation nodes according to graph consistency, and tracing a source link; and step 5, performing Hash solidification on the core early warning segment and spreading the performance to the alliance chain, and writing back the knowledge graph optimization template and channel. According to the scheme, multi-source data alignment is achieved, early warning is accurate and reliable, cross-medium semantic consistency and evidence chain traceability are achieved, and the requirement of the whole mountain disaster prevention process is met.

The invention discloses a surface water and underground water combined regulation and control method considering runoff and underground water level recovery, and belongs to the technical field of comprehensive regulation and control of water resources. According to drainage basin water resource supply and demand conditions and engineering characteristics, on the basis of fully considering multiple factors such as economy, society and ecology, organic combination of multiple regulation and control measures is implemented, and the method becomes an important way for achieving efficient combined utilization of water resources, restoring the underground water level, improving the regional ecological environment and guaranteeing food safety. Besides, in order to comprehensively evaluate the combined regulation and control effect of the surface water and the underground water, an evaluation index system of the system needs to be established and is used for evaluating the recovery conditions of the river runoff and the underground water level. By quantitatively evaluating actual effects of different regulation and control measures, a decision maker can be helped to judge whether water resource distribution and use are reasonable or not, the regulation and control measures are adjusted and optimized according to evaluation results, an appropriate surface water-underground water combined regulation and control scheme is selected, and reference can be provided for sustainable management of drainage basin water resources.

The utility model discloses an underground water level monitor for geotechnical engineering, which relates to the technical field of water level monitoring and comprises a probe rod body and a protective sleeve sleeved outside the probe rod body, and the top end of the probe rod body is provided with a traction rope penetrating out of an annular seat at the top of the protective sleeve; an overturning frame is rotationally installed on the side face of the bottom of the protective sleeve, a floating plate and a plugging frame are arranged at the two ends of the overturning frame respectively, and the plugging frame is arranged at the end, close to the protective sleeve, of the overturning frame; a floating plate is arranged at the upper part of the floating plate; by means of the torque change design of the floating plate, the overturning frame and the plugging frame, when the floating plate does not enter water, the plugging plate covers the open groove in the lower end of the protective sleeve, and rock soil and other sundries can be prevented from entering the protective sleeve to affect the probe rod; when the floating plate enters water, the blocking plate is moved away, so that the probe rod can be in contact with the water body in time to monitor the water level.

A simulation method for characterizing the spatiotemporal changes of groundwater levels, which belongs to the technical field of computer machine learning; includes the steps of: obtaining long-term groundwater level data based on long-term groundwater monitoring wells; superimposing multiple hydrogeological parameter information based on groundwater level data; and constructing a regional neural network model with physical mechanism by using a two-dimensional groundwater seepage equation serving as a physical mechanism, thereby simulating the temporal and spatial evolution of regional groundwater levels. The present invention can strengthen the rational use of groundwater resources, implement scientific and effective supervision, and provide a scientific basis for protecting regional groundwater safety and promoting the sustainable development of groundwater resources.

A knowledge graph-driven groundwater-oriented method for building risk warning includes: extracting a relationship quadruple between groundwater level change and building structure change based on collected groundwater level data and building data; creating a cross-domain knowledge graph based on the relationship quadruple, and associating two different fields; updating the cross-domain knowledge graph in real time to realize the building risk warning caused by groundwater level changes. The risk warning is based on the causal relationship reflected in the factual data, making the risk warning more accurate, which can effectively solve the problem of the lack of risk warning system for buildings due to groundwater level changes in the related art.

The invention discloses an underground water seepage analysis and prediction method based on physical-data cooperative driving, and belongs to the field of underground water seepage, and the method comprises the following steps: establishing a hydrogeological numerical model, carrying out seepage simulation calculation, and carrying out comparison verification with field monitoring data to realize accurate mapping; meanwhile, a machine learning data set is constructed by utilizing a numerical simulation result, a prediction model based on a Stacking integrated learning structure is established in combination with field data of a construction roadway, a water curtain layer and an oil storage cavern layer, and the water seepage amount or the underground water level after excavation of a rock mass in front of a tunnel face is predicted by taking geological, hydrological and construction parameters as input. According to the method, a physical mechanism and data driving are fused, the prediction precision and interpretability are remarkably improved, and a scientific basis is provided for cave depot project grouting and excavation optimization.

The invention relates to a landslide disaster-causing effect analysis method considering three-dimensional stability and finite difference, which comprises the following steps: calculating underground water level change under event rainfall in real time through FSLAM, interacting with a stability analysis module, and realizing closed-loop feedback of rainfall infiltration-stability coefficient-motion simulation; and the stability coefficient serves as a strength reduction factor input by Massflow to dynamically adjust material parameters, and full-chain analysis from instability judgment to motion simulation is achieved. By using high-resolution DEM data, the search range of the sliding surface can be dynamically defined, and the sliding surface is a three-dimensional sliding surface, so that modeling of a non-circular surface is supported, and irregular geometrical shapes can be accurately captured. The method overcomes the limitation of simplification of landslide geometry and soil mass mechanical characteristics in traditional two-dimensional analysis, more accurately captures complex mechanical behaviors in the landslide instability process, and improves the reliability of stability calculation.

The invention discloses a river-groundwater response and connectivity quantification method based on an event scale. The method comprises the following steps: acquiring rainfall, river water level and groundwater level time sequence monitoring data of a research area; identifying an independent precipitation event; identifying an effective runoff response event, and calculating the features of the quantitative event scale river response; effective groundwater response events are identified and classified, and features of groundwater responses of quantitative event scales are calculated; and calculating the response peak time lag of the river and the underground water, and constructing a dynamic connectivity index. The method breaks through the limitation of a traditional steady-state analysis method, can realize dynamic response characteristic and connectivity evaluation with high time resolution, has high universality and expandability, can systematically reveal the event-driven coupling process of the river and the underground water, and improves the reliability of the river and the underground water. The method is suitable for water resource evaluation and connectivity diagnostic analysis in areas with complex climate-geological conditions.

The utility model discloses a surface mine high and steep slope monitoring device and belongs to the technical field of slope monitoring. The surface mine high and steep slope monitoring device comprises a base, the upper surface of the base is fixedly connected with a fixing rod in a penetrating mode, and the top end of the fixing rod is electrically connected with front-end data monitoring equipment. The front-end data monitoring device comprises a surface deformation monitoring device, a deep deformation monitoring device, a blasting vibration monitoring device, an underground water level monitoring device and a settlement monitoring device. Front-end data monitoring equipment is used for monitoring the deformation condition of the whole surface of a side slope, the inclination angle acceleration multifunctional instrument is installed on the side slope boulder outburst dangerous rock mass to monitor the stability condition of the side slope boulder dangerous rock mass, and the inclination angle acceleration multifunctional instrument assists in monitoring the development condition of side slope cracks and is arranged on the penetrating edge at certain intervals through deep clinometers. Monitoring the slippage condition of the fault zone in the drill hole of the potential fault zone of the slope, aiming at the surface mine slope subjected to blasting mining, mounting and arranging along a slope top platform according to a certain interval, and monitoring the overall damage degree of blasting operation to the mine slope.