1247 results about "Ground water level" patented technology

The invention relates to an optimization method for dynamic monitoring of an underground water level. Comprising the following steps: uniformly arranging a plurality of sensors in an underground water monitoring area, and monitoring multi-dimensional parameters including water pressure, temperature and humidity; the sensors form a gridding monitoring system through a wireless network; inputting multi-dimensional data acquired by the sensor array into a data fusion module, performing cooperative processing on the data by using a data fusion algorithm, and filtering noise and abnormal values; a unified water level change model is constructed, so that data of different parameters complement each other; dynamically adjusting the weight in the fusion algorithm regularly according to the historical data and the new measurement value; in the monitoring area, based on the dimensions of time and space, a space-time analysis model of underground water level changes is constructed, and the periodicity and regularity of water level fluctuation are excavated; and performing trend analysis on the historical water level data by adopting a time sequence analysis algorithm including ARIMA or LSTM to generate short-term and long-term water level prediction.

The invention discloses a geological disaster early warning method and system based on big data analysis, and particularly relates to the technical field of geological disaster early warning. Geological deformation time series data of a target area are obtained through satellite remote sensing monitoring, feature importance is evaluated through a random forest algorithm, and regional geological risk grades are divided; meanwhile, wavelet transformation and long-short-term memory network modeling are carried out on rainfall spatio-temporal data of the meteorological monitoring station, and the potential influence of rainfall is evaluated; high-risk areas are screened, an underground water level change rate and rock-soil body strength correlation model is constructed, and a support vector machine algorithm is adopted to evaluate the landslide probability; and finally, analyzing a geological risk grading result, the potential influence degree of rainfall on the target area and the landslide occurrence probability by adopting an analytic hierarchy process, calculating a comprehensive risk index, comparing the comprehensive risk index with a comprehensive risk threshold value, and judging whether to trigger grading early warning, thereby realizing data fusion and intelligent analysis, early warning in advance, and improving the early warning efficiency. And effective help is provided for disaster prevention and reduction.

The invention belongs to the technical field of road deformation monitoring, and discloses a road slope deformation monitoring system and method, and the system comprises a fixed point monitoring mechanism which monitors the displacement, strain, underground water level and inclination parameters of a slope in real time at key points and key positions of the road slope; the route planning module plans the flight route of the unmanned aerial vehicle; the cruise monitoring mechanism is used for monitoring the side slope in all directions; the weather data acquisition module is used for acquiring local weather data; the three-dimensional laser scanner data analysis module is used for identifying abnormal conditions on the side slope; the image analysis module is used for identifying abnormal conditions on the slope; the comprehensive evaluation module is used for comprehensively evaluating the stability of the slope by utilizing a comprehensive evaluation model; and the alarm module comprises an alarm. According to the invention, the fixed-point monitoring mechanism and the cruise monitoring mechanism are combined to monitor the slope in all directions, the comprehensive evaluation module fuses data of various monitoring means, and the comprehensive evaluation model is utilized to comprehensively evaluate the slope stability.

The invention relates to a road collapse risk comprehensive assessment method based on multi-source data fusion, and the method comprises the following steps: collecting and preprocessing multi-source data related to road collapse, the multi-source data related to road collapse comprising remote sensing image data, underground water level monitoring data, geological exploration data and surface deformation monitoring data; spatio-temporal feature fusion and multi-level data fusion are carried out on the preprocessed multi-source data, the spatio-temporal feature fusion comprises spatial registration and time alignment, and the multi-level data fusion comprises low-level data fusion and high-level data fusion; and performing feature extraction on the fused multi-level data, inputting a feature extraction result into a road collapse risk assessment model constructed based on a machine learning model, outputting a road collapse risk occurrence probability, and generating a road collapse risk level according to the road collapse risk occurrence probability. Compared with the prior art, the method can comprehensively and accurately predict and evaluate the risk of road collapse.

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 relates to the technical field of environment evaluation, in particular to a comprehensive evaluation method and system for a mine geological environment. The method comprises the following steps that mine ore content data, underground water level data, soil acidity and alkalinity data and mine geological meteorological condition data are obtained and subjected to time sequence synchronization processing, and corresponding ore content data, underground water level data, soil acidity and alkalinity data and mine geological meteorological condition data in the same time sequence range are obtained; mine geologic structure exploration data are obtained, three-dimensional space structure modeling and environment risk factor monitoring of all sites are carried out, meanwhile, environment risk comprehensive evaluation analysis and environment risk management analysis are carried out on a three-dimensional space model of mine geologic structure distribution, and mine geologic environment risk management decision suggestions are generated. And executing a corresponding mine geological environment risk level management decision treatment plan. According to the invention, comprehensive dynamic monitoring of the geological environment of the mine can be realized on different time scales and spatial position dimensions.

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 provides a BIM (Building Information Modeling)-based rail transit construction geological exploration profile generation method, which belongs to the technical field of exploration measurement and comprises the step of comprehensively improving the accuracy of rail transit construction geological exploration through an advanced data acquisition and intelligent analysis method. In a construction site, multi-dimensional data of geological drilling, geological radar, geophysical prospecting testing and the like are acquired through various precise instruments, and a three-dimensional coordinate system is firstly established; and deep feature extraction is performed on complex data by using a deep learning algorithm, and key information such as geological stratification, rock and soil parameters and underground water level is accurately obtained. Intelligent fusion of multi-source data is realized by constructing a geological stratification model, a rock and soil parameter model and an underground water level model, and a comprehensive geological information model is generated. And importing the extracted geological profile data into a BIM platform, and carrying out strict precision verification and optimization by adopting a professional geological information collaborative equation set to finally form a highly accurate and reliable rail transit construction geological exploration profile.

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 geological static and mining disturbance dynamic factor coupling disaster-causing risk prediction method, and relates to the technical field of geological risk prediction methods. The method comprises the following steps of S01, geological static factor analysis, S02, mining disturbance dynamic factor analysis, S03, coupling disaster-causing risk prediction model construction, S04, risk assessment and early warning system development, S05, early warning mechanism establishment, S06, model verification and S07, risk management and decision support. The accuracy of geological disaster risk prediction is remarkably improved; according to the method, a comprehensive risk prediction model is constructed by quantifying the complexity of a geological structure, such as an area coefficient, a structural area loss coefficient and the like, and combining ground stress changes and underground water level changes caused by mining activities; the data-driven method can accurately predict the occurrence probability of geological disasters, provides more scientific decision support for mine safety management, and effectively reduces the occurrence risk of geological disasters.

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.

According to the coastal saline-alkali land underground water level adjusting system based on real-time monitoring and intelligent regulation and control, the system integrates the soil salinity sensor, the underground water level sensor and the central processing unit, future trend prediction is carried out by monitoring key parameters such as soil salinity and underground water level in real time in combination with a deep learning model, and the real-time monitoring and intelligent regulation and control are achieved. And precise intelligent regulation and control are realized. The system can effectively reduce soil salinization, prevent seawater from flowing backwards, improve the utilization efficiency of water resources and reduce the labor management cost at the same time. The method is characterized in that the application of multi-parameter comprehensive monitoring, a deep learning prediction model and a self-adaptive intelligent regulation and control algorithm provides a new technical solution for coastal saline-alkali land treatment.

The invention discloses an earth geology physical three-dimensional modeling method based on multi-data fusion, which comprises the following steps: A, data acquisition: acquiring geological data through geological exploration and measurement technologies, comprising geological profile, underground water level, lithology, structure, surface digital elevation model (DEM) data, remote sensing image data, surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data. According to the invention, data of geological profile, underground water level, lithology, structure, earth surface digital elevation model (DEM) data, remote sensing image data, earth surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data are collected. And the data can be effectively integrated.

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

The invention relates to the technical field of safety evaluation, in particular to an ultra-deep soft soil foundation pit safety evaluation method and system, and the method comprises the following steps: obtaining the geological data of an ultra-deep soft soil foundation pit, recording the soil type, soil density, underground water level and temperature data, carrying out the data arrangement and formatting, and generating a geological data record sheet. According to the method, the detailed geological data of the ultra-deep soft soil foundation pit are acquired and arranged, the accuracy and the applicability of the basic data are enhanced, so that subsequent analysis is carried out according to clear actual conditions, and the timeliness and the accuracy of risk prediction are ensured by directly associating prediction of mechanical behaviors with risk assessment; the risk assessment model can be dynamically adjusted, the effectiveness and accuracy of early warning are improved, the risk dependency relationship model and the monitoring scheme are integrated, theoretical and actual operations are effectively linked, the systematicness and adaptability of construction safety management are effectively improved, and the accident prevention capability is remarkably improved.

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 settlement prediction system based on soft soil creep, which relates to the technical field of settlement prediction, and dynamically calculates stress increment, total stress increment and pore water pressure by collecting the loading rate and underground water level change of a foundation in real time, and further calculates vertical effective stress based on the intermediate variables. Therefore, an accurate void ratio calculation formula is constructed. The systematized process not only improves the precision of settlement prediction, but also enhances the adaptability of the model to the change of actual working conditions. Specifically, through collection and processing of real-time data, the method can quickly respond to changes of foundation conditions, and it is ensured that model parameters are always in the optimal state. Calculating the void ratio by using the initial model parameters and evaluating the difference between a predicted value and an actual observed value in combination with an error function, so as to realize iterative optimization of the initial model parameters and finally obtain a corrected empirical model. The robustness and reliability of the model are remarkably improved, and settlement prediction is more accurate and reliable.

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 discloses an underground water level prediction method and system based on a space-time attention gating neural network. The method comprises the following steps: constructing a historical underground water level space-time sequence data set; performing principal component analysis on the space-time sequence data set to obtain a dimension-reduced space-time sequence data set; for the spatio-temporal sequence data set after dimension reduction, constructing a plurality of spatio-temporal characteristic matrixes through a sliding window method; training an underground water level prediction model by using the plurality of spatial-temporal characteristic matrixes to obtain a trained underground water level prediction model; obtaining time-space sequence data of historical underground water level of a to-be-measured position, constructing a time-space characteristic matrix of the underground water level, and predicting the underground water level of the to-be-measured position by using the trained underground water level prediction model; according to the method, rapid and accurate prediction of the underground water level is realized, and the generalization ability, the prediction precision and the interpretability of an underground water level prediction model are greatly improved while the modeling cost is reduced.

The invention discloses an underground water level monitoring method and system, and belongs to the technical field of water level monitoring, and the method specifically comprises the steps: arranging a pressure sensor and a temperature sensor in to-be-monitored underground water, initializing the sensors, carrying out the calibration, collecting the pressure and temperature data in an underground water well in real time, and carrying out the real-time monitoring of the pressure and temperature data; the height of the underground water level is calculated and corrected according to the pressure and temperature data, filtering calibration processing is conducted on the corrected height of the underground water level, environmental noise and sensor errors are eliminated, modeling and analysis are conducted on stored historical data of the underground water level, and the future change trend of the underground water level is predicted; transmitting the finally estimated underground water level data and the predicted change trend of the underground water level, storing the data in real time and monitoring the data in real time; model parameters can be dynamically adjusted according to historical data, high-precision prediction is performed on short-term and long-term trends of the underground water level, and the method adapts to underground water level characteristics of different regions and different time periods, so that abnormal fluctuation of the underground water level is monitored in real time.

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 reservoir storage capacity analysis, in particular to an intelligent reservoir storage capacity analysis method and system based on multi-source data fusion, and the method comprises the following steps: collecting ground water level sensor data, meteorological station data, LiDAR point cloud data and aerial image data, synchronizing the data through a unified timestamp, integrating the data, summarizing the integrated data, and carrying out the analysis of the reservoir storage capacity. And generating a comprehensive multi-source data set. According to the invention, through real-time updating and accurate tracking of daily reservoir capacity changes, the accuracy of reservoir capacity estimation is significantly improved, daily cumulative statistics of water level, rainfall and evaporation capacity data is integrated, dynamic and detailed management is promoted, seasonal changes and long-term trends of the reservoir water level are analyzed, and the reservoir capacity estimation accuracy is improved. The method can accurately reveal the direct effect of manual intervention and the influence of the manual intervention on the natural supply mode of the reservoir, can make a scientific decision based on a comprehensive data view, integrates short-term prediction and real-time data flow monitoring to enable an early warning system to quickly respond, and optimizes the efficiency and effectiveness of disaster management.

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 discloses a method for dynamically predicting the underground water level during excavation of an overtopping ground layer foundation pit, and relates to the technical field of dynamic prediction of the underground water level, which comprises the following steps of: acquiring multi-source data related to the underground water level in an engineering range; comprising information of soil moisture content, stratum permeability coefficient, rainfall capacity, foundation pit excavation depth, foundation pit dewatering scheme and foundation pit waterproof and drainage measures; historical underground water level, underground aquifer data and Yangtze River water level change information are obtained. According to the method, data quality is optimized through multi-source data acquisition, correlation analysis and z-score standardization, nonlinear modeling is realized in combination with a support vector machine (SVM) and Bayesian optimization, and model precision and generalization ability are improved. Model parameters are dynamically adjusted through a real-time monitoring and feedback optimization mechanism, the adaptability to sudden hydrological changes is enhanced, engineering risks are effectively reduced, and construction safety and economical efficiency are guaranteed.

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.