883 results about "Groundwater" patented technology

The invention provides an underground water pollutant concentration prediction method and system based on machine learning, and relates to the technical field of underground water pollutant concentration prediction.The method comprises the steps that historical data, hydrogeological parameters, meteorological data, human activity data and geochemical parameters of underground water pollutant concentration of a target area are preprocessed; dividing a training set, a verification set and a test set; constructing a preset resolution feature set based on a geochemical mechanism; selecting an adaptive machine learning model according to data characteristics and coupling a physical mechanism; performing hyper-parameter tuning by adopting Bayesian optimization, and supplementing small sample data in combination with transfer learning to complete model training; predicting the underground water pollutant concentration of the target area by using the trained model, and outputting a pollutant concentration prediction result with an uncertainty interval; the invention provides a technical scheme for predicting the concentration of underground water pollutants, which is efficient, accurate and high in adaptability.

The invention provides an underground water environment automatic monitoring super station state supervision method and system. The method comprises the following steps: collecting a multi-source dynamic time sequence data set and carrying out time-space alignment; generating a dynamic characteristic index set by using a nonlinear dynamic characteristic extraction method; based on the index set, constructing a self-adaptive cooperative measurement network to perform anomaly monitoring, and generating an anomaly detection result and a cooperative control instruction; generating real-time monitoring and early warning information by using a dynamic threshold adjustment algorithm; and generating an adaptive control instruction and a dynamic resource allocation scheme by using a neural network adaptive control strategy and a resource scheduling optimization algorithm. According to the method, a dynamic characteristic index set is generated, a C-C method is adopted to reconstruct a high-dimensional phase space, a Wolf algorithm and a G-P algorithm are combined to calculate related indexes and dimensions, and a multi-scale fractal mode is analyzed through an R / S analysis method and wavelet transform. The methods capture complex dynamic behaviors of the underground water system, and solve the problem that the traditional method is insufficient in non-linear feature extraction capability.

The invention discloses a tunnel or mine water gushing space-time prediction method and system coupled with a hydrodynamic numerical model, and the method comprises the steps: outputting multi-source data based on an identified and verified underground water numerical model, complementing the missing of measured data, quantifying the difference between the permeability characteristics of a fault and a normal stratum, and coupling the difference to a data system, and tunnel or mine excavation space data are merged. And constructing an LSTM-isolated forest-K neighbor regression coupling model, and configuring a multifunctional module to realize multi-scene data co-training. The preprocessed multivariate time series data is divided into a training set and a test set, hidden features are extracted through a coupling model, anomaly detection results are fused, a residual error correction model is synchronously trained, and hyper-parameters and weights are adaptively optimized according to multi-engineering prediction error feedback. And based on the trained coupling model, carrying out synchronous water gushing space-time prediction by adopting a window rolling strategy, and outputting prediction data meeting engineering precision in combination with residual correction. And reliable technical support is provided for safety prevention and control of engineering construction.

The invention discloses an integrated intelligent management and control method and system for underground water pumping and injection based on the Internet of Things, and the method comprises the steps: carrying out the geological investigation of a target region, constructing a three-dimensional geological model, and planning the layout of a pumping and injection well group and a sensor network according to the three-dimensional geological model; deploying a monitoring network and collecting data based on the layout scheme, and fusing a three-dimensional geologic model to construct a polluted site digital twinborn body; groundwater situation data are extracted through the digital twinborn body, a pollutant migration path and a water level dynamic state are predicted by using an LSTM model, and pollution scene simulation information corrected by geologic features is output; by taking the simulation information as input and coupling geological parameters as constraints, generating a pumping and injection well group cooperative control strategy set by adopting a BLMFO double-layer multi-objective optimization algorithm; and finally, a control instruction is executed, the real-time situation is monitored in the repairing process, strategy change suggestions including the flow adjustment amplitude, the agent optimization scheme and equipment operation parameter correction are judged and pushed, the repairing efficiency is improved, and the method adapts to the complex geological environment.

The invention provides a saline-alkali soil underground water regulation and control method and device, electronic equipment and a storage medium, and relates to the technical field of land governance. Inputting the first historical monitoring information into a pre-constructed prediction model to obtain a predicted value of soil salinity in a future time period; the predicted value of the soil salinity is input into the analysis model, and the target water level of underground water in the future time period is obtained; wherein the target water level of the underground water is used for restraining the target value of the soil salinity to be smaller than a preset salinity threshold value and the target value of the soil humidity to be larger than a preset humidity threshold value; generating a water level adjusting strategy based on the target water level of the underground water, and controlling the opening or closing of the water pump based on the water level adjusting strategy. According to the method, the soil salinity in the future time period is predicted by acquiring the saline-alkali soil monitoring information, so that the target water level of the underground water is obtained, the water level regulation strategy is generated, and the perspectiveness, dynamic nature and stability of underground water level regulation are ensured.

The invention provides an underground water environment monitoring system and method, and the method comprises the steps: carrying out the three-dimensional fluorescence spectrum full-wave band scanning of a known type of pollution source reference sample in a target monitoring region, and obtaining a three-dimensional fluorescence spectrum data set; performing characteristic analysis on the three-dimensional fluorescence spectrum data set to obtain fluorescence characteristic components, and determining a characteristic excitation wavelength corresponding to each fluorescence characteristic component; the classification contribution degree of each characteristic excitation wavelength is evaluated, and a characteristic excitation wavelength combination during source tracing of the pollution source is obtained through screening; performing fixed wavelength scanning on the to-be-detected underground water sample in the target monitoring area according to the characteristic excitation wavelength combination, and determining the initial type of the pollution source; and determining the relative contribution degree of the type of the pollution source of the to-be-detected underground water sample, simulating the migration trend of the pollution plume according to the underground water migration and diffusion model, and generating the position of the pollution source of the to-be-detected underground water sample. By adopting the scheme of the application, the pollution source of the underground water can be quickly identified and positioned at low cost.

The invention relates to the technical field of hydrological monitoring, and discloses a regional groundwater reserve change dynamic monitoring method based on gravity satellite time series data analysis. According to the method, the multi-source data scale effect and observation noise are effectively eliminated, the physical reliability and resolution of groundwater signal extraction are improved, and the recognition precision and early warning perspectiveness of the abnormal loss trend are enhanced. A full-chain monitoring closed loop of multi-source satellite observation, high-precision physical inversion and dynamic statistical benchmark is constructed, multi-dimensional environment data is aligned, a component separation strategy based on multi-element physical mechanism constraint is established based on a water balance principle, and anomaly diagnosis is performed in combination with a self-adaptively updated long-time-sequence historical database.

The invention relates to the technical field of geological engineering and underground water simulation, and discloses a regional underground water simulation method and system based on a physical mechanism and deep learning, and the method comprises the steps: converting the static attributes of an observation well into the embedding of nodes and edges through a graph neural network, and obtaining spatial features; according to the dynamic input of the observation well, modeling time sequence response of the underground water system under external dynamic driving through a long short-term memory neural network; deep fusion of spatial features and time dynamics is realized through a feature interaction mechanism, and a groundwater level simulation value is generated; combining a water balance constraint with a relative water level consistency constraint between observation wells to jointly construct a composite loss function for quantifying a difference between an analog value and a real observation value, and guiding parameter updating; the time-space dynamic and physical mechanism can be sensed synchronously, and the method is more reliable and efficient.

The invention discloses an arid region groundwater reserve estimation method and system based on multi-source remote sensing data, and relates to the technical field of groundwater monitoring, and the method comprises the steps: collecting land water reserve data and remote sensing image data of a target arid region, obtaining a land water reserve change sequence according to the land water reserve data, carrying out the region weighting of the target arid region, and obtaining a target arid region groundwater reserve change sequence; obtaining a land water reserve change sequence of the target arid region; based on the remote sensing image data of the target arid region, using a digital elevation model to obtain a surface water reserve change sequence of the target arid region, using a land surface process model to obtain a soil water reserve change sequence of the target arid region, and establishing an underground water reserve model of the target arid region; and performing error analysis on an output result of the groundwater reserve model by using a Monte Carlo simulation method. According to the method, through collaborative inversion of multi-source remote sensing data and integration of the groundwater reserve models, accurate quantification of spatial-temporal dynamic of the groundwater reserves in the arid region is realized.

The invention discloses a stratigraphic water content layering inversion method based on a shallow seismic full waveform, and relates to the technical field of geophysical exploration data processing, and the method comprises the following steps: 1, under the same survey line condition, synchronously obtaining a seismic wave full waveform, complex impedance and an induced polarization frequency spectrum; 2, constructing a layered initial model containing a layer boundary line and an in-layer water content initial value; based on the layered initial model, executing dual-domain quantile alignment interface keeping iteration until convergence, and obtaining a final layer boundary line and an in-layer water content field; the double-domain quantile alignment interface maintaining iteration comprises the following steps: updating an in-layer moisture content field and a layer boundary line according to a seismic wave full waveform; and performing total variation regularization processing on the updated in-layer water content field to maintain the transition characteristic of the layer boundary line. According to the method, an efficient and reliable solution with actual guidance value is provided for mineral resource exploration, geological structure detection, groundwater survey and engineering geology evaluation.

The invention belongs to the technical field of environment monitoring, and discloses an underground water environment monitoring system, which comprises a monitoring well body; the packers can be remotely controlled and are arranged in the depth direction of the monitoring well body; the optical fiber array can invert the seepage velocity field of the stratum around the well by applying controlled thermal pulse and monitoring temperature response; the robot flying shuttle can move in the well, and an in-situ chemical analysis module or a geophysical imaging module can be carried on the robot flying shuttle; the artificial intelligence model runs on the remote cloud platform; the artificial intelligence model can fuse multi-source data, takes a seepage field obtained by active detection and a stratum structure obtained by imaging as physical constraints, and can autonomously dispatch a robot shuttle to execute minute-level in-situ chemical diagnosis or scan inter-well strata. According to the invention, the leap from passive observation to active intelligent exploration is realized, and the monitoring precision and timeliness are significantly improved.

The invention discloses a soil and groundwater pollution diffusion prediction method and system based on a time-space diagram neural network, and relates to the crossing field of environmental information modeling and artificial intelligence, and the prediction method comprises the following steps: S1, obtaining multi-source soil and groundwater monitoring data and external driving data; s2, generating a static hydraulic communication matrix and a dynamic convection dominant matrix, and constructing a multi-relation dynamic graph structure; s3, performing parallel processing on graph convolution operation of the static hydraulic connection matrix and the dynamic convection dominant adjacency matrix, capturing time dynamic characteristics, and obtaining a pollutant concentration prediction result; and S4, outputting structured prediction data and a visual product. By constructing a multi-relation dynamic graph structure fusing a hydrogeological physical mechanism and a dynamic flow field response, distortion of a pollution plume leading edge deduction trajectory caused by misjudgment of a spatial dependency relation can be avoided, so that the prediction precision of a non-uniform anisotropic pollution diffusion path is improved.

The invention discloses a water-rich stratum underground cavity targeted treatment method, and relates to the field of underground cavity targeted treatment, and the method comprises the steps: constructing an underground cavity recognition model through improved YOLOv8, and recognizing an underground cavity in a water-rich stratum; a three-dimensional geologic model is constructed, a drilling track pointing to the cavity from the earth surface or the existing structure is designed, targeted drilling is constructed, and a treatment channel is formed; with the cavity as the center, curtain grouting holes are formed, rapid-hardening type double-liquid grout is injected, and a three-dimensional waterproof curtain is formed; underground water in the three-dimensional waterproof curtain is guided and drained through part of targeted drill holes, and desilting is conducted; and self-compacting filling materials are pumped into the cavity through the other targeted drill holes for main body backfilling, and cavity cracks are reinforced by adopting a sleeve valve pipe grouting technology. And the filling and reinforcing effect is verified, and a monitoring system is arranged to monitor the stratum stability. Through a technical path of combining artificial intelligence identification, three-dimensional accurate positioning and active three-dimensional water stop, safe and efficient targeted treatment of the underground cavity of the water-rich stratum is realized.

The invention discloses a multi-dimensional comprehensive evaluation method for permeability resistance of overlying strata of a working face, and belongs to the technical field of mine engineering and geological safety evaluation. 12 types of core factors such as rock strength, rock core quality, physical and mechanical properties of rock mass, hydrological properties and mining influence characteristics are integrated; a full-process evaluation framework of data acquisition, index system construction, weight distribution, comprehensive modeling and verification optimization is constructed, an analytic hierarchy process and an entropy evaluation method are coupled to determine index weights, a dynamic evaluation model is constructed in combination with a fuzzy mathematics theory, and accurate classification and risk early warning of the permeability resistance of the overlying strata are realized. According to the method, through comprehensive analysis of a plurality of factors, the accuracy of overlying strata permeability resistance evaluation can be improved, so that the underground water permeation path and the permeation amount are predicted more accurately, the uncertainty is reduced, the comprehensiveness and the accuracy of evaluation are remarkably improved, and a scientific basis can be provided for mine water disaster prevention and control and safe mining.

The invention belongs to the technical field of inter-basin underground water circulation flux estimation, and particularly relates to an infiltration and lateral inflow separated underground water flux remote sensing estimation method and system. The method includes: acquiring a multi-source remote sensing data set of a research area; performing partition correction on evapotranspiration data of different land utilization type areas in the research area; the method comprises the following steps: dividing a research region into equal-area units, identifying a vertical dominant unit and a lateral dominant unit, and further determining a cross-basin underground water exchange zone; and solving a local vertical infiltration amount by using the vertical dominant unit and interpolating to form a background field, establishing a combined equation set which takes the background field as an initial value and takes the dynamic characteristic parameters of the underground water as additional constraints for the lateral dominant unit, solving to obtain an external lateral inflow amount, and further obtaining a cross-basin underground water circulation flux. According to the method, the vertical dominant unit and the lateral dominant unit are identified, and the cross-basin groundwater circulation flux is quantitatively estimated.

This application provides a large vertical shaft and its construction method, belonging to the field of underground construction technology. The shaft includes: a shaft cylinder composed of multiple ring segments; a ring-shaped top beam located on the outer side of the top of the shaft cylinder; and tension piles positioned below the top ring beam and outside the shaft cylinder along the shaft's depth direction. The bottom of the tension piles is lower than the bottom of the shaft cylinder and is cast integrally with it. The top of the tension piles is fixedly connected to the top ring beam. The horizontal distance between the tension piles and the outer side of the shaft cylinder is less than or equal to the pile diameter. The large vertical shaft and its construction method provided in this application integrate the tension piles, the top ring beam fixed to the ground, and the shaft cylinder into a single structure, capable of resisting the buoyancy caused by groundwater surrounding a deep shaft. This method is suitable for constructing ultra-large and ultra-deep vertical shafts with diameters exceeding 16 meters and depths exceeding 50 meters.

The invention relates to the technical field of shale gas development underground water monitoring, and discloses a shale gas development area deep underground water environment intelligent monitoring and early warning method. According to the method, a dynamic simulation model of a deep groundwater environment is constructed based on geological structure characteristics and historical environment data of a shale gas exploitation area, and a basic framework is provided for subsequent analysis. And then, acquiring a groundwater environment real-time monitoring data set including water quality concentration, water level fluctuation and soil seepage rate through a preset monitoring well array, and realizing comprehensive capture of groundwater environment information. Then, the real-time monitoring data set is imported into the dynamic simulation model for calibration, and it is ensured that the model can accurately reflect the actual situation. And then operating the calibrated model, simulating the migration process of the pollutants in the underground water, and outputting a risk prediction value. And finally, comparing the risk prediction value with a preset safety critical value, and generating an early warning instruction according to a result.

The invention discloses a sampling device for groundwater microorganism detection, and belongs to the technical field of groundwater detection equipment, a sampling rotating mechanism comprises a fixed column, the fixed column is sleeved with a spiral lifting sleeve, a guide block is slidably connected into the spiral lifting sleeve, and a reset spring is fixedly connected between the guide block and the spiral lifting sleeve; a sliding groove is formed in the fixed column and comprises a plurality of inclined lifting grooves and straight moving grooves which are communicated end to end; the upper end of the rotary lifting sleeve is rotatably connected with a sliding sleeve through a fixing sleeve, the inner side wall of the sliding sleeve is fixedly connected with a limiting block which is slidably connected to the side wall of the fixing column, and the side wall of the sliding sleeve is fixedly connected with an L-shaped pressing rod and a sampling mechanism which are rotatably connected with the fixing column. After the sampling rotating mechanism reaches the specified underground depth, the sampling mechanisms can be opened one by one through the rolling depth measuring mechanism, underground water is collected, and the device can improve the remote management capability of the multiple sampling mechanisms.

The invention discloses a deep learning-based large deformation prediction method for a high ground stress soft rock tunnel, and belongs to the field of tunnel engineering geological disaster prediction. By constructing a double-branch network architecture of'geological mode identification-deep learning prediction ', accurate prediction of large deformation of the tunnel is realized. The method comprises the following steps: firstly, collecting six qualitative indexes such as a rock mass structure, a weathering degree and an underground water condition and three quantitative indexes such as a strength-stress ratio, support rigidity and an equivalent hole diameter, performing encoding and normalization preprocessing, and respectively inputting the indexes into an upper-layer network and a lower-layer network; outputting probability distribution of six types of modes prone to large deformation, such as a weak homogeneous type mode and a block crack structure type mode; and the lower layer predicts the deformation and divides risk levels through a full-connection neural network based on the geological mode identification result and the quantitative index. According to the method, the complex coupling mechanism of large deformation of the high-crustal-stress soft rock tunnel can be fully reflected; adaptability prediction is realized for different geological modes.

The invention provides an underground water reserve dynamic early warning method and platform based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: collecting and obtaining the hydrogeological structure information and multi-mode underground water monitoring data of an underground water reserve region; constructing an underground water reserve twinborn model, wherein the underground water reserve twinborn model comprises a plurality of underground water reserve twinborn correlation models of a plurality of hydrogeological unit areas; historical groundwater mining data is called, and dynamic prediction and risk assessment of the groundwater reserve state are carried out in combination with the current groundwater reserve state and the current meteorological data; and presetting a groundwater reserve risk judgment boundary, and if the risk assessment result falls into the groundwater reserve risk judgment boundary, generating a groundwater reserve early warning signal. The technical problem that in the prior art, groundwater management mostly depends on a static model or a simplified mathematical model, complexity of groundwater and interaction between the groundwater and various external factors cannot be fully reflected, and then accurate groundwater early warning is difficult to achieve is solved.

The invention relates to the technical field of early warning and monitoring, in particular to a deep groundwater supplementation data analysis and early warning system which comprises a signal processing module, an oxygen consumption analysis module, a nitrogen transmetabolism module, a risk identification module and a coupling optimization module. According to the method, the metabolism trend is analyzed by synchronously collecting underground multi-depth chemical indexes and removing noise, establishing a unified time sequence data set, extracting dissolved oxygen, nitrate and ammonia nitrogen concentration change characteristics, calculating the oxygen dissipation rate and constructing the time-depth distribution relation, and combining nitrogen conversion rate regression. A risk section is identified according to the directions of oxygen dissipation increase and metabolism decrease, the micro-potential gradient and coupling index change are further analyzed in combination with an electrochemical change rule, dynamic identification and risk early warning of the deep groundwater refilling process are achieved, and monitoring is changed from static data comparison to multi-dimensional dynamic analysis. The accuracy and the timeliness of abnormal recognition are improved, and the complement safety and the continuity and the reliability of data results are guaranteed.

The invention discloses an underground water burial depth trend surface partition prediction method based on set evaluation, and relates to the technical field of underground water data processing. Underground water burial depth monitoring data, meteorology, land types and geological environment factors are fully integrated through time sequence continuous acquisition and standardization processing of multi-source data; the method comprises the following steps: constructing an initial trend surface, quantitatively describing continuous spatial distribution of the underground water burial depth of a target irrigation area by combining a weighted spatial interpolation method, extracting long-term trend and short-term fluctuation components through time sequence decomposition, fusing with spatial distribution features to form a multi-scale space-time feature set, constructing a prediction model by using a deep learning network architecture, and predicting the underground water burial depth of the target irrigation area. Meanwhile, the time evolution law and the space distribution mode of the underground water burial depth are captured, and prediction of the underground water burial depth in the future time period is achieved. According to the method, the time-space evolution map of the underground water burial depth is quantitatively generated, so that a decision basis is provided for water resource management, irrigation area scheduling and ecological protection.

The invention discloses a method for determining industrial soil and underground water preferential management and control pollutants through multistage screening, and belongs to the technical field of environment management and pollutant risk assessment.The method comprises the steps that an industrial pollutant database of an area to be managed and controlled is constructed; forming a specific pollutant list; quantitative indexes are determined, a TOPSIS method is adopted to sort the pollutants, and preferential control pollutants are determined; according to the method, a three-stage progressive screening framework of'suspected pollutant library-particular pollutant-preferential control pollutant 'is carried out, a multi-criterion comprehensive evaluation system of a weight and TOPSIS sorting model is fused, multi-dimensional risk quantitative sorting is carried out on the pollutants, and the pollutants needing preferential control are accurately locked; the defects that a traditional screening technology is high in subjectivity, single in consideration dimension, lack of industry pertinence and the like are overcome, the comprehensiveness, scientificity and accuracy of pollutant screening are remarkably improved, and effective technical support is provided for environmental risk management and control.

The invention relates to a waste dump heavy metal pollution risk assessment system based on artificial intelligence. The system comprises a regional data acquisition module, a data primary processing module, a soil assessment model construction module, a water body assessment model construction module and a comprehensive pollution risk assessment module. According to the invention, original data is obtained through data acquisition; a data primary processing method of grid standardization, fracture network topology construction, feature engineering and enhancement and data set segmentation is adopted; an improved random forest model is adopted as a soil evaluation model, a feature weighting mechanism and diffusion simulation of a multi-physical process are introduced, and pollution rules under different paths are simulated by using a multi-path learning strategy; a mixed deep learning model is adopted as a water evaluation model, a hydrological topology network is constructed, a special response function is designed, and meanwhile, delayed release and long-term accumulation characteristics of pollutants are captured by using a space-time memory mechanism, so that the risk evaluation precision of the underground water level is improved.

The invention relates to the technical field of underground water simulation, in particular to an underground water numerical simulation method coupled with vegetation ecology. The method realizes bidirectional coupling between groundwater and vegetation ecology through iteration, and specifically comprises the following steps: inverting vegetation coverage based on groundwater burial depth; inversion of actual evapotranspiration is carried out based on underground water burial depth and vegetation coverage; and finally, updating the net supply amount of the groundwater model based on evapotranspiration until convergence. According to the method, a statistical function relation is established to replace a complex physical model, and drainage and overflow cell water levels are corrected in iteration to ensure calculation stability.

The invention relates to the technical field of pollution risk assessment, and provides an AI-based karst region soil-underground water cooperative pollution assessment system. According to the system, multi-source soil and underground water heavy metal time sequence monitoring data are fused, cross-medium collaborative characterization characteristics are constructed, an enhanced time sequence modeling method with a parameter efficient adaptation capability is introduced, and a composite dynamic loss function oriented to non-stationary pollution evolution characteristics is designed; and collaborative prediction and uncertainty description of the heavy metal pollution states of the soil and the underground water are realized. Furthermore, a prediction result is combined with spatial gridding analysis, a collaborative pollution risk index and a risk zoning result are constructed, technical conversion from pollution evolution modeling to spatial risk assessment and early warning output is realized, and reliable technical support is provided for dynamic early warning, graded prevention and control and environment management decision of heavy metal pollution in the karst region.

The invention relates to the technical field of drainage management, in particular to an intelligent data analysis system for drainage management, which is characterized in that full-chain data from a source, a permeable area, underground water and a tail end are comprehensively acquired through a drainage acquisition module, and a comprehensive water level capable of reflecting the overall load state of the system is calculated through a weighted fusion formula; according to the evaluation method based on the curve form similarity, a comprehensive water level curve is drawn, and the comprehensive water level curve is compared with an expected drainage curve, so that the system can objectively give a conclusion that drainage management is qualified or unqualified in a rainfall event. Compared with single-point over-limit alarm, the dynamic response capability of the system in the time dimension can be reflected more comprehensively, management evaluation is standardized and scientific, and intelligent, accurate and robust self-calibration of core capability parameters of the drainage system is achieved by establishing a dynamic proportional adjustment mechanism based on a preset integral difference value.

The invention relates to the technical field of underground water recharging, and particularly discloses a deep foundation pit underground water pumping and recharging balanced vacuum pressure type recharging device which comprises a basic hardware system, a sealing isolation system, a dynamic regulation and control system and a monitoring feedback system. The sealing isolation system comprises a vacuum well lid; according to the pipeline sealing assembly, a hydraulic drive sealing ring is arranged at the joint of a dewatering well water outlet pipe and a recharge well water inlet pipe, the sealing ring automatically expands to fill gaps during water injection, and air permeation is thoroughly isolated; through triple air isolation formed by water pumping pipe head immersion, vacuum well lid sealing and hydraulic sealing rings, contact between underground water and air is reduced to the maximum extent, oxidation reaction is reduced from the source, and the precipitation amount of iron and manganese oxides is reduced by 60% or above; meanwhile, bubbles and sediments can be effectively removed, more than 90% of bubbles and sediments can be actively removed through a periodic back raising and silt pumping mechanism, the anti-blocking performance of the recharge well is remarkably improved, and therefore the service life of the recharge well is prolonged.

The invention discloses a disturbance risk assessment method and system for a shield tunnel to pass through a spring runoff area, and relates to the technical field of tunnel construction safety assessment and underground water environmental protection, and the method comprises the steps: collecting multi-source information data of the spring runoff area, carrying out the data fusion, feature extraction and clustering, dividing a spring runoff sensitive area, and carrying out the feature extraction and clustering of the spring runoff sensitive area; constructing an indoor physical model test system of a similar stratum of the spring runoff sensitive area; carrying out a shield tunneling simulation experiment, collecting macro-micro data of a permeation path, and extracting a macro-micro evolution rule of a shield on a choke effect of a spring runoff sensitive area; introducing an excavation face limit support pressure constraint equation, and constructing a coupling model to verify and calibrate a macro-micro evolution rule; and taking the shield tunneling parameters and the geological parameters as input and the choked flow characteristic parameters as output, training an intelligent inversion model, embedding the intelligent inversion model into the coupling model, and forming closed-loop evaluation of the choked flow disturbance risk of the spring water runoff area according to actual shield tunneling data.

A method for predicting CH4 production and CO2 sequestration in a CO2-ECBM process belongs to the field of natural hydrogen resource exploration, and is technically characterized by comprising the following steps: constructing a multiphase and multicomponent coupling model for describing the migration process of water, methane and carbon dioxide of a stratum coal bed gas reservoir; the method comprises the following steps: constructing a geologic model of a stratum coalbed methane reservoir, performing grid division on the geologic model to obtain a plurality of grid units reflecting different spatial distribution positions of the coalbed methane reservoir, and setting initial conditions and boundary conditions of the geologic model; and according to the multiphase and multicomponent coupling model, solving spatio-temporal evolution characteristics of pressure and concentration of water, methane and carbon dioxide of each grid unit of the geologic model to obtain distribution in time and space. According to the method, the influence of the underground water in the coal bed gas on the migration behavior of methane and carbon dioxide is fully considered, the limitation of prediction result deviation caused by neglecting the effect of the underground water in the existing research is overcome, and the accurate prediction of the methane yield and the carbon sequestration amount in the CO2-ECBM process is realized.