139 results about "Tailings dam" patented technology

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

The invention belongs to the field of safety and stability analysis of tailing dams, and discloses a tailing dam stability analysis method based on a GCN-AGRU fusion model and a related device, and the method comprises the steps: obtaining the monitoring data of a multi-source heterogeneous sensor of a tailing dam; preprocessing the monitoring data to obtain multi-source heterogeneous time series data; performing feature analysis on the multi-source heterogeneous time series data to obtain key factors of the stable state of the tailing dam; constructing a heterogeneous hierarchical causal graph by using the key factors; determining a key factor stable state index system according to the heterogeneous hierarchy causal diagram; a multi-source GCN-AGRU fusion space-time prediction model is utilized to obtain space information of the state of the tailing dam; performing time correlation modeling on the multi-source heterogeneous time sequence data to obtain a time sequence of the state of the tailing dam; calculating a predicted value of the multi-source heterogeneous sensor according to the spatial information and the time sequence; and determining the stability state trend of the tailing dam according to the predicted value in combination with a key factor stability state index system. The intelligent sensing of the situation of the tailing dam can be realized.

The invention belongs to the field of tailing pond safety risk identification and accident analysis, and discloses a tailing dam accident path identification method based on a mixed causal knowledge graph and a related device, and the method comprises the steps: building a mixed causal logic model through employing an obtained tailing dam accident initial hazard source; solving a set of accident cause links of the tailing dam by utilizing the mixed causal logic model; in the set of the tailing dam accident cause links, the logic relation of the tailing dam accident and the accident cause links are used as nodes and edges of a knowledge graph respectively, and a Python + Neo4j method is used for constructing a tailing dam accident knowledge graph; and coding and classifying the tailing dam accident knowledge graph by using a BERT-GCN-SVM model to realize tailing dam accident path identification. The mine tailing dam accident path identification method is improved and optimized based on the thought of the knowledge graph, a set of mine tailing dam accident chain identification method is formed, and the mine tailing dam accident path identification method can be used as a new tool for mine tailing dam accident directional control and disaster situation deduction.

The invention discloses a tailing dam dynamic stability monitoring and early warning method and system, and belongs to the technical field of geotechnical engineering safety and structural health monitoring, and the method specifically comprises the steps: obtaining tailing dam monitoring data in real time, building and updating a numerical model of a tailing dam based on the tailing dam monitoring data, and calculating the dynamic stability of the tailing dam according to the numerical model. And calculating a current infiltration line in the tailing dam, analyzing the updated numerical model by adopting a self-adaptive crack surface optimization method without presetting a slip crack surface shape to obtain a current tailing dam safety coefficient and a critical slip crack surface, and predicting a future safety coefficient change trend by utilizing a machine learning model based on historical monitoring data and weather forecast data. If the current tailing dam safety coefficient and the future safety coefficient change trend meet predefined early warning conditions, an early warning signal is generated, otherwise, the early warning signal is not generated; according to the method, dynamic, real-time and accurate evaluation and predictive early warning of the stability of the tailing dam are realized, and the safety management level of the tailing pond is remarkably improved.

The invention discloses a tailing pond displacement dynamic prediction method and system based on a multi-algorithm coupling model. The prediction method comprises the following steps: S1, data acquisition and preprocessing; s2, decomposing a displacement signal; s3, trend term displacement prediction; s4, performing feature derivation; s5, periodic term displacement prediction; and S6, predicting result fusion. The prediction system comprises the following modules: a data acquisition module, a data preprocessing module, a displacement signal decomposition module, a trend term displacement prediction module, a feature derivation and optimization module, a periodic term displacement prediction module and a prediction result fusion module. Through the advanced multi-algorithm coupling model, the high-precision dynamic prediction of the displacement of the tailing pond is realized, the precision of the displacement prediction of the tailing dam and the long-term dependence modeling capability are improved, the generalization capability of the model to a complex displacement mode is enhanced, and powerful technical support is provided for the safety monitoring and disaster early warning of the tailing dam.

The invention discloses a GCN-LSTM-based tailing dam multi-point settlement deformation prediction method. The method comprises the steps of collecting and preprocessing original settlement monitoring data of monitoring points in a research area; constructing a graph structure for settlement data between all monitoring point pairs, setting a threshold value, connecting node pairs with significant correlation coefficient relationships by using edges, constructing a weighted undirected graph, and converting the weighted undirected graph into a normalized adjacent matrix as the input of a graph convolutional network GCN; extracting the spatial topology of each monitoring point by using a GCN; capturing long-term time dependence in the settlement process by using a gating mechanism of the LSTM network; and fusing the GCN and the LSTM network, and finally outputting a predicted value through a full connection layer. According to the method, the spatial topological graph among the monitoring points of the tailing dam is constructed, and the GCN and LSTM networks are fused, so that the spatial correlation among the monitoring points and the time dynamic characteristics of the settlement data are effectively captured, and high-precision tailing dam multipoint settlement prediction is realized.

The invention provides an intelligent tailing dam displacement prediction and early warning method, and belongs to the technical field of safety prediction and early warning, and the method comprises the steps: obtaining online monitoring historical data of a tailing dam, and carrying out the preprocessing of the data; constructing a GRU main prediction model to predict main prediction displacement; obtaining an error sequence through the measured data and the main prediction displacement; decomposing the error sequence into a trend term error sequence and a noise term error sequence through PSO-VMD-TOPSIS, and respectively constructing a trend term error correction model and a noise term error correction model based on GRU; an error correction value and a standard deviation of a trend item and a noise item are obtained through an MC-dropout technology; a dynamic weight calculation mechanism is constructed, a final displacement prediction value and a confidence interval are obtained in combination with the main prediction displacement, and whether an alarm is given or not is judged; according to the invention, high-precision real-time prediction, uncertainty quantification and real-time early warning of the displacement of the tailing dam are realized, and intelligent decision support is provided for safety state evaluation and disaster early warning of the tailing dam.

The invention discloses a tailing pond water level intelligent early warning system based on multi-modal data sensing, and the system comprises a data acquisition module which collects multi-dimensional data, and obtains water level seepage fusion features through principal component analysis; the behavior acquisition module is used for acquiring external rainfall characteristics and acquiring water level conduction behavior data by excavating a space-time nonlinear correlation mode between the water level seepage fusion characteristics and the external rainfall characteristics; the spatial topology module is used for acquiring a safety threshold through historical risk data and constructing an improved tailing pond spatial topology network in combination with a graph convolutional network; according to the improved tailing pond spatial topology network, on the basis of a graph convolution network infrastructure, the construction of the improved spatial topology network is completed by designing a topology perception attention mechanism and stacking multiple layers of graph convolution; and water level early warning data: inputting the water level conduction behavior data and the water level seepage fusion features obtained in real time into the improved tailing pond spatial topology network to obtain an early warning result.

The invention provides a tailing dam instability situation multi-source information fusion early warning method. The method comprises the steps that tailing dam parameters of a tailing dam are collected, and first multi-source early warning parameters are determined from the tailing dam parameters; obtaining a landform model of the tailing dam, and establishing a multi-scale multi-factor coupling mechanical model of the tailing dam based on the landform model and the first multi-source early warning parameter; inputting the parameters of the tailing dam into a multi-scale multi-factor coupling mechanical model to predict and simulate the dam break process of the tailing dam to obtain a prediction and simulation result; and on the basis of the prediction simulation result, early warning is carried out on instability of the tailing dam. Therefore, according to the scheme, the multi-scale multi-factor coupling mechanical model is used for early warning, physical mechanisms and interaction of the tailing dam on different scales are considered, the instability trend of the tailing dam can be predicted more accurately by performing coupling analysis on multiple factors, and the early warning precision is improved.

The downstream dam slope seepage drainage device comprises a plug, a suction filtration pipe, a vertical water conveying pipe, a connecting pipe and a transverse drainage pipe, the plug is located at the end of the whole seepage drainage device, the suction filtration pipe is connected to the upper portion of the plug, the vertical water conveying pipe is connected to the upper portion of the suction filtration pipe, the connecting pipe is connected to the upper portion of the vertical water conveying pipe, and the transverse drainage pipe is connected to the lower portion of the vertical water conveying pipe. A connector connected with a vacuum machine is arranged on the transverse drainage pipe, one end of the transverse drainage pipe is sealed, and a first valve capable of being opened and closed is arranged at an opening in the other end of the transverse drainage pipe. According to the downstream dam slope seepage drainage device for lowering the seepage line of the tailing dam, suction filtration and drainage can be actively carried out on the silted position in the tailing pond dam body, and suction filtration and drainage work with precise positioning can be carried out on the silted position possibly occurring at any time, so that the safety and stability of the tailing dam are guaranteed.

The invention relates to the technical field of mineral separation, in particular to a method for reducing the content of sericite in porphyry type copper tailings. The method specifically comprises the following steps that reselection grading operation is conducted on the porphyry type copper tailings, and a coarse-fraction product and a fine-fraction product are obtained; the reselected fine-fraction product is fed into magnetic separation operation to remove ferromagnetic minerals; performing multi-channel flotation operation on the strong magnetic tailings to remove sulfide minerals; the obtained desulfurized flotation tailings are fed into reverse flotation operation after the ore pulp concentration and the pH value are adjusted, and the reverse flotation tailings are sericite products; in order to solve the problems that the content of micro-fine particle sericite in the porphyry type copper tailing dam is high, and the dam break risk hidden danger is increased, the reselection-magnetic separation-desulfurization flotation-reverse flotation combined process is adopted, the recovery rate of the sericite in the copper tailings is 26%-30%, the content of the sericite in the tailings is greatly reduced, and the safety of the dam body is improved.

The utility model provides a tailing slurry discharge device which comprises a fixing part, a buffer plate and a connecting part, the fixing part is used for being fixedly connected with an ore drawing pipe, one end of the connecting part is connected with the fixing part, and the other end of the connecting part is connected with the buffer plate; the buffering plate is arranged between the ore drawing pipe and the beach face of the tailing dam in a suspended mode and arranged below an opening of the ore drawing pipe, the horizontal highest point of the buffering plate is arranged on the orthographic projection extension line of the axis of the ore drawing pipe on the buffering plate, a plurality of through holes are formed in the buffering plate, and when tailing slurry is discharged through the ore drawing pipe, under the action of gravity, the tailing slurry impacts the buffering plate and is borne by the buffering plate. The buffering plate is arranged, so that tailing slurry can be dispersed by the buffering plate and discharged downwards along the through hole and the slope of the buffering plate, the potential energy of tailing discharging is dispersed, a beach face scouring pit cannot be formed, and the good scattering and scattering effect and the uniform deposition effect are achieved.

The invention provides a hyperspectral tailing pond dry beach line intelligent analysis method, and belongs to the technical field of image processing. Through multiband characteristics of a hyperspectral remote sensing image, principal component clustering analysis and characteristic wavelength selection are combined, so that the problems of insufficient spectral information of the image after dimension reduction and serious data redundancy caused by use of a full-band image are solved; single-band images corresponding to different characteristic bands emphasize different target information in the original image, and different combinations are beneficial to fully exerting the advantages of different target information of each band, so that a water area and a dry beach are distinguished more accurately; a double-branch network model is adopted, the difference between spectral dimensions and spatial dimensions is synchronously analyzed, spectral features and spatial information are fused, and the two branches have certain complementarity, so that rich spatial spectrum information of an input hyperspectral remote sensing image can be fully excavated, a dry beach line can be identified more accurately and effectively, the length of the dry beach is calculated, manual intervention is reduced, and the working efficiency is improved. The treatment efficiency is improved.

The invention provides a tailing pond dam break simulation system and method. The method comprises the steps that a real shot image of a tailing pond is collected, basic information of solid waste is recognized, real condition information of the tailing pond is established, pressure information of different solid waste on the tailing pond is analyzed, and a three-dimensional pressure simulation model of the tailing pond is established in combination with structural information of the tailing pond; obtaining a bearing pressure threshold value corresponding to each dam body structure in the tailing pond, constructing dam break positions corresponding to the tailing pond in different preset breach states according to the preset breach states and the bearing pressure threshold value corresponding to each dam body structure, and monitoring each dam break position in the three-dimensional pressure simulation model. According to the method, the dam break time corresponding to each corresponding dam break position is obtained, a waste treatment scheme of the tailings pond is established, the breach position and the breach time when dam break occurs in the tailings pond are analyzed by using the model, and then a scheme for cleaning waste is generated within effective time, so that the aim of nipping in the bud is fulfilled, and the waste treatment efficiency is improved. And unnecessary damage caused by dam break is avoided.

The invention discloses an intelligent identification method and platform for tailings pond disaster hidden dangers, and relates to the technical field of intelligent monitoring, and the method comprises the steps: carrying out the automatic partitioning based on the global monitoring data of a tailings pond, and constructing a layered data collection system; establishing a physical and mechanical database, and combining consolidation and mechanical data to construct a mechanical parameter inversion model; identifying physical and mechanical characteristics of the dam body through the model, determining disaster types and performing grading; deducing a disaster evolution process, analyzing a disaster chain and constructing a structure tree; and judging a disaster occurrence rate according to the disaster chain, and if the disaster occurrence rate exceeds an early warning threshold, generating a risk early warning report. The technical problem that in the prior art, a tailing pond disaster hidden danger recognition system is insufficient in accuracy is solved, and the technical effect of improving the accuracy of tailing pond disaster hidden danger recognition is achieved through accurate recognition of tailing pond disaster types, dynamic adjustment of disaster grades and real-time deduction of the disaster evolution process.

The invention discloses a micro-nano rock-soil robot geological disaster multi-source information fusion early warning system which is characterized by comprising a micro-nano rock-soil robot cluster, a wireless energy supply system, electromagnetic modified micro-nano particles, a data transmission and control module, an injection deployment system, a main control unit, an information fusion analysis module and an early warning module. The early warning system can realize multi-dimensional and multi-scale information monitoring of geological disasters, has high timeliness, accuracy and reliability, is suitable for application scenes of landslide, slope collapse, tunnel deformation, tailing dam safety monitoring and the like, and provides technical support for early warning and risk assessment of geological disasters.

The present invention discloses a tailings dam constructed with the aid of mining waste rock and a construction method thereof. The flood diversion dam includes a gravity dam body cast with rubble stone concrete and a back-pressure platform. The back-pressure platform is filled with mining waste rock. The back-pressure platform is constructed on the downstream side of the gravity dam body, and the back-pressure platform is arranged in contact with the inclined surface of the downstream side of the gravity dam body. The back-pressure platform spanning the entire riverbed of the present invention can significantly improve the overall stability and safety of the flood diversion dam, effectively control the uneven settlement caused by the gravity dam on the weak covering layer foundation, greatly reduce the risk of dam body cracking, and prevent the gravity dam covering layer foundation from deep shear overall instability and damage. The platform filling construction of the present invention keeps rising synchronously with the gravity dam body, and the back-pressure platform is used to build the access road to the warehouse during the construction period, which effectively solves the problem of transporting tens of thousands of cubic meters of rubble stone into the warehouse during the gradual casting and rising process of the rubble stone concrete gravity dam.

The invention relates to the field of mine geologic modeling, in particular to a dynamic geologic modeling method and system based on self-adaptive updating of multi-source heterogeneous data. The method comprises the steps that a tailings pond state feature data set is acquired, and a time-space lag prediction model is constructed through a machine learning method according to the tailings pond state feature data set; a tailing pond operation plan information set is obtained, and based on the tailing pond operation plan information set and the current ore pulp liquid level state, a time-space lag prediction model is utilized to predict a pore water pressure change information set in the tailing dam in the future period; and dynamically evaluating the stability of the tailing dam according to the pore water pressure change information set, and outputting stability early warning information. In the mine geologic modeling process, timely configuration of emergency resources and accurate control of prevention and control opportunities are achieved, and linkage dam body instability and disastrous dam break accidents are further reduced.

The application relates to the field of mine geological modeling, in particular to a dynamic geological modeling method and system based on adaptive updating of multi-source heterogeneous data. The method comprises the following steps: acquiring tailing pond state feature data set, constructing a time-space lag prediction model through a machine learning method according to the tailing pond state feature data set; acquiring a tailing pond operation plan information set, predicting a pore water pressure change information set inside a tailing dam in a future period of time by using the time-space lag prediction model based on the tailing pond operation plan information set and a current ore pulp liquid level state; and performing dynamic evaluation of tailing dam stability according to the pore water pressure change information set and outputting stability early warning information. In the mine geological modeling process, timely allocation of emergency resources and accurate grasp of prevention and control time are realized, and chain instability of dam body and disastrous dam break accidents are reduced.

The invention provides an anti-seepage slope protection material as well as a preparation method and application thereof. The anti-seepage slope protection material comprises a cementing material, an alkali activator and waste rock aggregate, wherein in terms of the total weight of the cementing material being 100%, the cementing material comprises 20%-80% of mudstone powder and 20%-80% of slag powder; the material simultaneously realizes resource utilization of three solid wastes, namely mudstone, slag powder and mining area barren rock, has excellent anti-seepage performance, compressive strength, anti-disturbance capability and durability, and is suitable for anti-seepage water-resisting layers of open pit coal mine dumping sites, tailing dams or industrial solid waste storage yards.

The embodiment of the invention provides a tailing dam rainfall infiltration model test system and method under the freezing and thawing cycle effect, and relates to the technical field of tailing dam stability detection. The system comprises a pressure bearing box, an environment control device, a rainfall infiltration device and an intelligent monitoring device. Wherein a tailing dam model is arranged in the pressure-bearing box, the environment control device is communicated with the internal space of the pressure-bearing box, and environment parameters such as the test temperature, the test air pressure and the test oxygen concentration of the internal space of the pressure-bearing box can be adjusted. The rainfall infiltration device can simulate different rainfall phenomena according to meteorological data. The intelligent monitoring device can collect monitoring data through the sensor group and generate a test result for evaluating the stability of the tailing dam through the data processing unit. The system can simulate a low-pressure and low-oxygen environment and a freeze-thaw cycle phenomenon in a high-cold and high-altitude area, can realize dynamic process simulation of a tailing dam model under freeze-thaw cycle and rainfall conditions, and improves the accuracy of a test result.

This application provides a method, system, equipment, and storage medium for dynamically fitting and generating phreatic lines in tailings dams, relating to the field of tailings dam monitoring technology. The method includes: acquiring phreatic line monitoring data of a target tailings dam; preprocessing the phreatic line monitoring data to obtain target monitoring data; constructing a dynamic fitting function library based on the dam body parameters and foundation parameters of the target tailings dam; performing parameter fitting based on the target monitoring data and the dynamic fitting function library, calculating the goodness of fit, and selecting a target fitting function based on the goodness of fit; and drawing the phreatic line of the target tailings dam based on the target fitting function and the target monitoring location information. This application achieves fully automated and intelligent generation from raw monitoring data to high-precision phreatic lines, effectively ensuring the authenticity and stability of the phreatic line representation.

This application relates to the technical field of dam protection, and in particular to a construction process for tailings dam protection, comprising the following steps: S1, prefabricating burlap sacks, filling the sacks with rock, soil, and grass seeds, and bundling multiple sacks into groups; S2, cleaning and leveling the dam surface; S3, setting up anti-slip units on the dam surface, and placing the bundled burlap sacks onto the anti-slip units; S4, sprinkling water on the sacks and waiting for the plants to sprout. This application can improve the construction efficiency of dam protection and reduce construction costs.

The tailing pond system in the cold region comprises a tailing dam, a dry beach area, a clarification area, a first ore drawing pipeline and a heat dissipation pipeline, and valves are arranged at the inlet ends of the first ore drawing pipeline and the heat dissipation pipeline; the heat dissipation pipeline comprises a second ore drawing pipeline, a connecting pipeline, a pre-buried pipeline, a first anti-blocking pipeline and a second anti-blocking pipeline which are communicated in sequence; a first ore drawing pipeline and a second ore drawing pipeline are alternately arranged on the tailing dam, and a pre-buried pipeline is laid in the dry beach area; and an outlet of the second anti-blocking pipeline is higher than the level of tailing slurry discharged in the dry beach area. The invention further discloses an ore drawing method of the tailing pond system. The heat generated when the tailing slurry is discharged is used for eliminating a tailing pond freezing layer lenticular body in a cold region, it is guaranteed that the dam body infiltration line is not lifted, and the dam body safety is guaranteed.

The invention relates to the field of tailing pond supervision, and discloses a tailing pond water level dynamic early warning system, method and equipment and a storage medium. The system comprises a water level monitoring module used for continuously collecting real-time water level data of discrete point locations of a tailing pond area; the edge calculation module is used for preprocessing the real-time water level data and generating a corresponding trigger type inspection instruction; the trigger routing inspection module is used for responding to the trigger routing inspection instruction, executing a routing inspection task on a region corresponding to the trigger routing inspection instruction, and collecting spatial domain water level data of a tailing pond region; and the data processing module is used for fusing the water level field data and the water level prediction data according to the real-time water level data to generate multi-level safety early warning information. According to the application, through cooperative operation of all the modules, a set of three-dimensional monitoring and early warning system is constructed, evolution from traditional single passive monitoring to global active early warning is realized, and the accuracy and reliability of tailing pond safety monitoring are improved.

The application provides a tailing dam instability trend multi-source information fusion early warning method. The method comprises the following steps: collecting tailing dam parameters of a tailing dam, and determining first multi-source early warning parameters from the tailing dam parameters; obtaining a topographic and geomorphic model of the tailing dam, and establishing a multi-scale multi-factor coupled mechanical model of the tailing dam based on the topographic and geomorphic model and the first multi-source early warning parameters; inputting the tailing dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam-bursting process of the tailing dam, and obtaining a prediction simulation result; and early warning the instability of the tailing dam based on the prediction simulation result. Thus, the present scheme uses the multi-scale multi-factor coupled mechanical model for early warning, considers the physical mechanism and interaction of the tailing dam at different scales, and can more accurately predict the instability trend of the tailing dam by coupling analysis of multiple factors, thereby improving the early warning precision.

The invention relates to the technical field of tailing pond infiltration line detection, and particularly discloses a direct-reading visual artificial intelligence detection device for a tailing pond infiltration line, two sides of a top plate are rotatably connected with supporting parts, the side surface of the top plate is fixedly connected with a gradienter, and the bottom of the top plate is fixedly connected with a blocking part. According to the direct-reading visual artificial intelligence detection device for the tailing pond infiltration line, the detection mechanism is arranged, the detection piece is lowered through the infiltration line detection pipe and can buzz after making contact with the water surface, and after receiving an alarm, detection personnel detect the situation through the video detection piece and directly read infiltration line burial depth data according to a scale cable; the accuracy of manual detection data of the tailing dam infiltration line is ensured, accurate comparison and analysis of online monitoring data of the infiltration line are facilitated, effective and accurate operation of an online monitoring device of the infiltration line is ensured, the stability of a dam body is kept, and dam break accidents are completely eradicated.

The application provides a tailing pond dam-break whole process simulation method and system, including constructing a three-dimensional geological grid model according to ground point cloud data of a target area, cooperating with drilling data, solving an initial ground stress field through a virtual mass method and setting a stress imbalance rate condition; a landslide simulation stage is based on the initial ground stress field and a landslide impact force to simulate a mountain rupture process, obtaining a damage area boundary and a pore water pressure distribution; when a dam body displacement mutation or a stress threshold value is exceeded in a disaster response, a dam-break simulation stage of a deformation damage area is started; in the dam-break simulation stage, a mud and sand flow movement path is simulated based on the damage area boundary and topographic data; finally, a disaster path graph, an accumulation thickness cloud graph and an influence range thermal cloud graph are generated based on the mud and sand flow movement path; the application breaks through the limitations of traditional methods, first realizes high-precision and efficient simulation of the whole process of "deformation, damage, dam-break and disaster" of a tailing pond dam-break, and provides a quantitative prevention and control basis for high-risk scenarios.

The application provides a tailing pond seepage sensitivity analysis and dam break early warning method based on fissure characteristics, acquires collection images of a tailing pond dam body based on a computer vision technology, analyzes and processes the collection images, determines joint fissure regions and joint fissure characteristics, realizes accurate acquisition of tailing pond joint fissure characteristics, provides accurate data basis for tailing pond seepage sensitivity, determines the saturation surface distribution and the hydraulic gradient distribution of the tailing pond dam body based on the joint fissure regions and the joint fissure characteristics, performs seepage sensitivity analysis and calculation on the tailing pond dam body, obtains seepage calculation results, performs dam break judgment on each type of result based on standard results, performs early warning and reminding according to the dam break judgment, realizes accurate analysis and early warning of the stability of the tailing pond, and ensures the safety of the tailing pond.

The invention discloses a cloud model-based tailing dam rainfall stability risk assessment method. The method comprises the following steps of S1, establishing a complete index system; s2, carrying out denoising preprocessing on the monitoring data by adopting a variational mode decomposition (VMD) algorithm; s3, carrying out weight analysis, and determining a combined weight; and S4, constructing a cloud model, determining a standard cloud of the cloud model, and judging a risk level. According to the method, the VMD algorithm is innovatively introduced to carry out denoising reconstruction on the monitoring data, original noisy data does not need to be stored, only effective signal components are reserved, risk level distribution characteristics can be described only through expected value-entropy-hyper-entropy parameterized representation of the cloud model and by using three groups of core parameters, dependence of a traditional method on full-amount historical data is replaced, and the method is high in reliability and high in reliability. The problem that the size of the buffer area is difficult to match the data size is thoroughly solved; under the mechanism, the risk evolution key information can be completely reserved with lower storage cost, and risk misjudgment caused by data loss can be avoided.