CN120708377A - A multi-dimensional early warning system for groundwater dynamic monitoring based on data analysis - Google Patents
A multi-dimensional early warning system for groundwater dynamic monitoring based on data analysisInfo
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Abstract
本发明涉及地下水监测技术领域，具体涉及一种基于数据分析的地下水动态监测多元预警系统，包括数据融合采集模块、动态地质建模模块、深度学习预测模块、多层次智能预警模块以及智能响应决策模块；其中：数据融合采集模块：用于采集地多维数据；动态地质建模模块：构建地下水含水层的三维动态模型；深度学习预测模块：预测地下水位和水质的未来变化趋势；多层次智能预警模块：建立多层次预警机制；智能响应决策模块：制定相应的地下水管理和应急响应策略。本发明，通过实时整合多源数据并结合智能预警与自动化管理，显著提升了地下水监测的精确性和响应速度，有效支持地下水资源的可持续管理与保护。
The present invention relates to the field of groundwater monitoring technology, and specifically to a multi-level early warning system for dynamic groundwater monitoring based on data analysis, comprising 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. The system comprises: a data fusion acquisition module for collecting multi-dimensional data; a dynamic geological modeling module for constructing a three-dimensional dynamic model of a groundwater aquifer; a deep learning prediction module for predicting future trends in groundwater levels and water quality; a multi-level intelligent early warning module for establishing a multi-level early warning mechanism; and an intelligent response decision module for formulating corresponding groundwater management and emergency response strategies. The present invention significantly improves the accuracy and response speed of groundwater monitoring by integrating multi-source data in real time and combining intelligent early warning with automated management, effectively supporting the sustainable management and protection of groundwater resources.
Description
Technical Field
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.
Background
With the rapid development of industrialization and urban treatment, groundwater resources face unprecedented pressure and challenges, fluctuation of groundwater level and change of water quality are directly related to drinking water safety, agricultural irrigation and ecological environment stability, although the prior art can monitor some dynamic changes of groundwater, such as water level and basic water quality parameters, the technologies generally depend on point location monitoring and periodic data updating, and it is difficult to reflect comprehensive states and rapid changes of groundwater resources in real time, and in addition, the existing monitoring systems often lack effective data fusion capability, and cannot integrate multidimensional data from different sensors and monitoring points, resulting in insufficient decision support capability in terms of groundwater management and emergency response.
At present, main technical problems faced by a groundwater monitoring and early warning system include how to accurately integrate multisource monitoring data, update a geological model in real time and respond to groundwater emergencies quickly, and the problems are that groundwater management often does not react quickly enough, so that negative effects caused by groundwater pollution and excessive exploitation cannot be effectively prevented or relieved.
Therefore, there is an urgent need for a system capable of monitoring dynamic changes of groundwater in real time, which should be capable of accurately fusing and analyzing data from various monitoring devices such as geological radar, hydrological sensors, and the like, and also providing multi-level intelligent early warning based on the data.
Disclosure of Invention
Based on the above purpose, the invention provides a groundwater dynamic monitoring multi-element early warning system based on data analysis.
The underground water dynamic monitoring multi-element early warning system based on data analysis 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 configuring a plurality of sensors and synchronously acquiring multidimensional data of groundwater level, soil conductivity, surface vegetation coverage and aquifer change, and integrating the acquired multidimensional information into unified groundwater state data through a data fusion algorithm;
The dynamic geologic modeling module is used for constructing a three-dimensional dynamic model of the groundwater aquifer by utilizing a geologic modeling algorithm based on the groundwater state data generated by the data fusion acquisition module and combining with the regional geologic structure information;
the deep learning prediction module acquires three-dimensional dynamic model data updated in real time from the dynamic geological modeling module, predicts future change trend of the groundwater level and the water quality through a deep learning algorithm by combining historical groundwater monitoring data, and transmits a prediction result to the multi-level intelligent early warning module to trigger an early warning mechanism;
The multi-level intelligent early warning module establishes a multi-level early warning mechanism based on the trend prediction data provided by the deep learning prediction module, sets corresponding early warning thresholds according to different risk levels, and triggers early warning signals when a prediction result reaches the corresponding thresholds;
And the intelligent response decision module combines the early warning signals transmitted by the multi-level intelligent early warning module and the three-dimensional dynamic model data generated by the dynamic geological modeling module to formulate corresponding underground water management and emergency response strategies, and realizes automatic implementation through integration with the regional water resource management system.
Optionally, the data fusion acquisition module comprises a sensing unit, a data processing unit and a data transmission unit, wherein:
The sensing unit comprises a geological radar, multiband remote sensing equipment and a hydrological sensor, wherein the geological radar is used for detecting the depth and structural characteristics of an underground aquifer, the multiband remote sensing equipment is used for monitoring the change condition of surface vegetation coverage, the hydrological sensor is used for measuring the underground water level and the soil conductivity, and the sensing unit also comprises the step of collecting underground water and environmental information thereof in the same time and space range so as to realize synchronous collection of multidimensional data;
The data processing unit is used for receiving the multidimensional data from the sensing unit, processing the multidimensional information from different sensors through a preset data fusion algorithm, wherein the data fusion algorithm comprises a weighted average method and a Kalman filtering algorithm, and the weighted average method firstly carries out weight distribution on the sensing data from different sources;
The data transmission unit is used for transmitting the processed unified groundwater state data to the dynamic geological modeling module, and comprises a wireless transmission device and a data encryption submodule, wherein the wireless transmission device is used for transmitting the data in real time through a wireless network, and the data encryption submodule is used for conducting encryption processing on the transmitted data.
Optionally, the data processing unit includes:
The method comprises the steps of weight distribution, namely carrying out preliminary normalization processing on sensing data from geological radar, multiband remote sensing equipment and hydrological sensors to ensure that the data output by each sensor are in the same dimension;
kalman filtering, namely, applying a Kalman filtering algorithm to weighted sensing data to eliminate noise and redundant information, wherein the Kalman filtering comprises a prediction step and an updating step, and the prediction step is estimated according to the state of the last moment And state transition matrixPredicting the state of the current momentThe updating step is to combine the sensor observation dataAnd predicting stateBy calculating Kalman gainUpdating the state;
Data integration, namely integrating the sensing data subjected to Kalman filtering to form uniform groundwater state data, wherein the integrated data is The calculation formula is as follows: , wherein, Represents the state data of the integrated underground water,Is the firstTime of day (time)The data from each sensor after kalman filtering,For the number of sensors actually configured in the system,Is the corresponding weight coefficient.
Optionally, the dynamic geologic modeling module comprises a data receiving unit, a geologic information integrating unit, a three-dimensional modeling unit and a dynamic updating unit, wherein:
The data receiving unit is used for receiving the underground water state data generated by the data fusion acquisition module and formatting the received data;
the geological information integration unit is used for collecting regional geological structure information, including stratum distribution, fault positions, lithology characteristics and geological history data, matching and integrating the regional geological structure information with the received groundwater state data and providing basic data for the three-dimensional modeling unit;
The three-dimensional modeling unit is connected with the geological information integration unit, and constructs a three-dimensional dynamic model of the groundwater aquifer by using a geological modeling algorithm, wherein the geological modeling algorithm comprises a spatial interpolation algorithm and a finite element method, the spatial interpolation algorithm is used for carrying out spatial distribution estimation on groundwater state data and calculating the groundwater level and aquifer change in each spatial unit;
And the dynamic updating unit is used for dynamically adjusting and updating the three-dimensional dynamic model according to the groundwater state data updated in real time and the regional geological structure change information, and continuously optimizing the precision of the three-dimensional dynamic model through iterative calculation.
Optionally, the three-dimensional modeling unit includes:
spatial distribution estimation, namely, using a spatial interpolation algorithm to carry out spatial distribution estimation on groundwater state data, and setting the spatial distribution estimation to be shared in a region Each measuring pointThe ground water level value of (2) isAnd its space coordinates areIn the target space unit by a spatial interpolation algorithmIs of the ground water level of (1)The calculation formula is as follows: , wherein, Indicating the measuring pointWith the target space unitThe distance between the two plates is set to be equal,Estimating the ground water level of each space unit by means of weight distribution for interpolating powerAnd aquifer change conditions;
Space unit combination, namely after space distribution estimation is completed, combining the space units by using a finite element method to construct a complete three-dimensional dynamic model of the groundwater aquifer, dividing a region into a plurality of finite element units, wherein the volume of each unit is WhereinRepresent the firstThe finite element units are combined with the underground water level fields of the finite element units through a finite element method to form a continuous three-dimensional aquifer model;
Model construction and integration, namely integrating the results of all finite element units into a complete three-dimensional dynamic model after finite element calculation is completed.
Optionally, the dynamic updating unit includes:
Data input and initialization, wherein the dynamic updating unit receives real-time updated groundwater status data And regional geologic structure change informationFirstly, for the existing three-dimensional dynamic modelInitializing, and setting initial parameters of the modelComprising hydraulic conductivity coefficientCoefficient of water storage;
Model correction based on real-time updated groundwater status dataAnd regional geologic structure change informationFor initial model parametersCorrecting to generate new model parametersThe correction formula is:
, wherein, AndIn order to correct the coefficient of the light,AndIs old groundwater state data and geological structure information;
Iterative calculation using corrected new parameters Recalculating a three-dimensional dynamic modelHydraulic conductivity for each finite element unit by finite element methodAnd water storage coefficientAnd carrying out iterative updating, wherein an iterative equation is as follows:; , wherein, AndRespectively represent the firstThe first iterationThe hydraulic conductivity and water storage coefficient of each finite element unit,AndIs the firstThe result of the calculation of the number of iterations,AndIn order to observe the value of the value,AndIs an iteration step length;
Model convergence and updating, namely after finishing a plurality of iterative calculations, checking the convergence of the model, and when the iterative result meets the convergence condition, namely And is also provided withStopping the iteration and obtaining the final iteration resultAs an updated three-dimensional dynamic model, a final three-dimensional dynamic modelExpressed as: , wherein, AndFor the converged hydraulic conductivity and water storage coefficient,Is the total number of finite element units.
Optionally, the deep learning prediction module comprises a data input unit, a feature extraction unit, a prediction model training unit and a trend prediction unit, wherein:
A data input unit for receiving real-time updated three-dimensional dynamic model data from the dynamic geologic modeling module Historical groundwater monitoring dataPreprocessing the received data, including data standardization and normalization, so as to ensure that the input data are analyzed on the same scale;
A feature extraction unit for extracting three-dimensional dynamic model data Historical monitoring dataExtracting features including the rate of change of groundwater levelIndex of water qualityAnd its spatial distribution pattern;
The prediction model training unit is used for training a deep learning prediction model according to the extracted characteristic data, wherein the deep learning algorithm is a long-term and short-term memory network, and a prediction model of the groundwater level and the water quality is built through time sequence analysis of historical data;
The trend prediction unit is used for predicting the future ground water level and the water quality by using the trained deep learning model and is used for updating the three-dimensional dynamic model data in real time Latest feature dataPredicting and outputting the groundwater level in a specified time period in the futureAnd water quality change trend。
Optionally, the multi-level intelligent early warning module comprises a data receiving unit, a risk assessment unit, a threshold setting unit and an early warning signal triggering unit, wherein:
the data receiving unit is used for receiving trend prediction data from the deep learning prediction module, wherein the trend prediction data comprises future groundwater level change trend data and water quality change trend data;
The risk assessment unit is used for carrying out risk assessment based on the received trend prediction data, and assessing the risk level of each region according to the severity of the groundwater level change trend and the water quality change trend and combining the geological conditions, the historical events and the factors of socioeconomic effects of the region, wherein the risk level is divided into a plurality of levels including low, medium, high and extremely high, and each level corresponds to different potential hazard degrees;
The threshold setting unit is used for setting specific early warning thresholds for different risk levels according to preset safety standards and historical data;
The early warning signal triggering unit is used for monitoring trend prediction data, triggering early warning signals of corresponding grades immediately when the prediction result is detected to reach or exceed a set early warning threshold value, and sending out early warning signals through various ways including email, short messages, alarm sounds and control center display according to different risk grades, so that relevant departments or personnel can receive early warning information in time and take corresponding measures.
Optionally, the risk assessment unit includes:
trend data analysis, namely, received groundwater level change trend data And water quality change trend dataAnalyzing, and calculating the variation amplitude and the variation rate of each region in the predicted time period;
Regional geologic condition weighting by calculating geologic sensitivity coefficients based on the geologic condition of each region, including formation type, fault density, lithology characteristics The method is used for reflecting the influence of geological conditions on groundwater change;
The historical event influence evaluation comprises the steps of calculating the influence coefficient of the historical event according to the historical event data of the corresponding area, including the historical disasters, pollution events or emergency events related to underground water ;
Socioeconomic impact assessment by evaluating socioeconomic factors including population density, economic activity density and infrastructure distribution, calculating socioeconomic impact coefficients;
Comprehensive risk score calculation, namely analyzing trend data and obtaining geological sensitivity coefficientHistorical event influence coefficientCoefficient of socioeconomic impactIn combination, calculate a composite risk score for each regionThe formula is: , wherein, To the point ofThe weight coefficient of each factor; Is the change rate of the groundwater level; the change amplitude of the water quality index is;
risk grading according to comprehensive risk score Dividing each region into multiple risk classes including low risk, medium risk, high risk and extremely high risk, wherein the low risk isThe middle risk isHigh risk ofAt very high risk of。
Optionally, the intelligent response decision module comprises an early warning signal receiving unit, a strategy generating unit and an execution control unit, wherein:
The early warning signal receiving unit is used for receiving an early warning signal from the multi-level intelligent early warning module, wherein the early warning signal comprises risk grade information and corresponding early warning grade, and simultaneously receives three-dimensional dynamic model data generated by the dynamic geological modeling module;
The strategy generation unit is used for formulating corresponding underground water management and emergency response strategies according to the received early warning signals and the three-dimensional dynamic model data, selecting a response strategy template according to the risk level of the early warning signals, and generating specific management measures and emergency response plans by combining the underground water level, the aquifer distribution and the water quality state reflected in the three-dimensional dynamic model data, wherein the specific management measures and the emergency response plans comprise underground water extraction limit, manual water supplement, pollution source control and emergency resource scheduling;
and the execution control unit is used for converting the generated underground water management and emergency response strategy into specific operation instructions and sending the operation instructions to related execution equipment or control systems.
The invention has the beneficial effects that:
According to the invention, through integrating multi-source data from geological radar, multi-band remote sensing equipment, hydrological sensor and the like in real time, a comprehensive and fine ground water state monitoring solution can be provided, not only can the change of ground water level and water quality be continuously tracked, but also a geological model can be updated in real time, so that the change of ground water resources can be immediately captured and accurately reflected, and due to the highly integrated monitoring capability, a ground water management department can more quickly identify potential risks and problems such as pollution diffusion or abnormal water level drop, thereby more effective preventive measures and countermeasures can be adopted.
According to the invention, by constructing a multi-level intelligent early warning mechanism and deeply integrating with the regional water resource management system, an automatic process from data acquisition and risk assessment to emergency response is realized, so that the speed and accuracy of emergency response are greatly improved, the resource allocation and management efficiency are optimized, the social and economic losses caused by groundwater problems can be effectively reduced through an intelligent early warning and response strategy, the public and ecological safety guarantee is enhanced, and a powerful technical support is provided for sustainable management of groundwater resources.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system for dynamically monitoring and multi-element early warning of groundwater in an embodiment of the invention;
Fig. 2 is a schematic diagram of a multi-level intelligent early warning module according to an embodiment of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and to specific embodiments. While the invention has been described herein in detail in order to make the embodiments more detailed, the following embodiments are preferred and can be embodied in other forms as well known to those skilled in the art, and the accompanying drawings are only for the purpose of describing the embodiments more specifically and are not intended to limit the invention to the specific forms disclosed herein.
It should be noted that references in the specification to "one embodiment," "an example embodiment," "some embodiments," etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the relevant art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
Generally, the terminology may be understood, at least in part, from the use of context. For example, the term "one or more" as used herein may be used to describe any feature, structure, or characteristic in a singular sense, or may be used to describe a combination of features, structures, or characteristics in a plural sense, depending at least in part on the context. In addition, the term "based on" may be understood as not necessarily intended to convey an exclusive set of factors, but may instead, depending at least in part on the context, allow for other factors that are not necessarily explicitly described.
1-2, The underground water dynamic monitoring multi-element early warning system based on data analysis 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 configuring a plurality of sensors and synchronously acquiring multidimensional data of groundwater level, soil conductivity, surface vegetation coverage and aquifer change, and integrating the acquired multidimensional information into uniform groundwater state data through a data fusion algorithm for use by a subsequent module;
The dynamic geologic modeling module is used for constructing a three-dimensional dynamic model of the groundwater aquifer by utilizing a geologic modeling algorithm based on the groundwater state data generated by the data fusion acquisition module and combining with regional geologic structure information, and transmitting the model to the deep learning prediction module after being updated in real time for further trend analysis and risk assessment;
the deep learning prediction module acquires three-dimensional dynamic model data updated in real time from the dynamic geological modeling module, predicts future change trend of the groundwater level and the water quality through a deep learning algorithm by combining historical groundwater monitoring data, and transmits a prediction result to the multi-level intelligent early warning module to trigger an early warning mechanism;
The multi-level intelligent early warning module establishes a multi-level early warning mechanism based on trend prediction data provided by the deep learning prediction module, sets corresponding early warning thresholds according to different risk levels, triggers early warning signals when a prediction result reaches the corresponding thresholds, and transmits early warning information to the intelligent response decision module to generate a specific coping strategy;
And the intelligent response decision module combines the early warning signals transmitted by the multi-level intelligent early warning module and the three-dimensional dynamic model data generated by the dynamic geological modeling module to formulate corresponding underground water management and emergency response strategies, and realizes automatic implementation through integration with the regional water resource management system.
The data fusion acquisition module comprises a sensing unit, a data processing unit and a data transmission unit, wherein:
The sensing unit comprises a geological radar, multiband remote sensing equipment and a hydrological sensor, wherein the geological radar is used for detecting the depth and structural characteristics of an underground aquifer, the multiband remote sensing equipment is used for monitoring the change condition of surface vegetation coverage, the hydrological sensor is used for measuring the underground water level and the soil conductivity, and the sensing unit also comprises the step of collecting underground water and environmental information thereof in the same time and space range so as to realize synchronous collection of multidimensional data;
The data processing unit is connected with the sensing unit and is used for receiving multidimensional data from the sensing unit and processing multidimensional information from different sensors through a preset data fusion algorithm, the data fusion algorithm comprises a weighted average method and a Kalman filtering algorithm, the weighted average method firstly carries out weight distribution on the sensing data from different sources and is used for reflecting the importance of the data of each sensor;
The data transmission unit is used for transmitting the processed unified groundwater state data to the dynamic geological modeling module, the data transmission unit comprises a wireless transmission device and a data encryption sub-module, the wireless transmission device is used for transmitting the data in real time through a wireless network, the data encryption sub-module is used for conducting encryption processing on the transmitted data to ensure the safety and the integrity of the data in the transmission process, and the data fusion acquisition module can synchronously acquire, process and integrate the state data of groundwater and the environment thereof in multiple dimensions through the tight matching of the units, so that the system can conduct subsequent analysis and early warning based on comprehensive and accurate data.
The data processing unit includes:
The weight distribution comprises the steps of carrying out preliminary normalization processing on sensing data from geological radar, multiband remote sensing equipment and hydrological sensors to enable the data output by the sensors to be comparable under the same dimension, and carrying out weight distribution on the normalized data by using a weighted average method, wherein the calculation formula is as follows:
, wherein, Represent the firstThe weight of the individual sensors is determined,Represent the firstThe standard deviation of the individual sensor data,Determining the importance of different sensors in data fusion by the formula for the total number of the sensors, wherein the sensors with higher weights have larger contribution to the final result;
Kalman filtering, namely, applying a Kalman filtering algorithm to the weighted sensing data to eliminate noise and redundant information, wherein the Kalman filtering comprises a prediction step and an updating step, and the prediction step is estimated according to the state of the last moment And state transition matrixPredicting the state of the current momentThe formula is:
, wherein, Is the firstThe predicted state of the moment in time,In the form of a state transition matrix,In order to control the input matrix,Is the firstControl input of time of day, updating step by combining sensor observation dataAnd predicting stateBy calculating Kalman gainUpdating the state, wherein the formula is as follows:
; , wherein, Is the firstThe kalman gain at the moment in time,In order to predict the error covariance matrix,In order to observe the matrix,In order to observe the noise covariance matrix,Is the firstFiltering noise and redundant information through the step to obtain more accurate sensing data;
Data integration, namely integrating the sensing data subjected to Kalman filtering to form uniform groundwater state data, wherein the integrated data is The calculation formula is as follows: , wherein, Represents the state data of the integrated underground water,Is the firstTime of day (time)The data from each sensor after kalman filtering,For the number of sensors actually configured in the system,The integrated data accurately reflect the real state of the underground water by the calculation for the corresponding weight coefficient, and a reliable data base is provided for the subsequent modules; through the steps, the data processing unit can effectively process and integrate multidimensional data from different sensors, and provides accurate and reliable basic data for dynamic monitoring and early warning of the system.
The dynamic geological modeling module comprises a data receiving unit, a geological information integrating unit, a three-dimensional modeling unit and a dynamic updating unit, wherein:
the data receiving unit is used for receiving the groundwater status data generated by the data fusion acquisition module, including multidimensional information of groundwater level, soil conductivity, surface vegetation coverage and aquifer change, and formatting the received data to ensure the consistency of the data in subsequent processing;
the geological information integration unit is used for collecting regional geological structure information, including stratum distribution, fault positions, lithology characteristics and geological history data, matching and integrating the regional geological structure information with the received groundwater state data and providing basic data for the three-dimensional modeling unit;
The three-dimensional modeling unit is connected with the geological information integration unit and is used for constructing a three-dimensional dynamic model of the groundwater aquifer by using a geological modeling algorithm, wherein the geological modeling algorithm comprises a spatial interpolation algorithm and a finite element method, the spatial interpolation algorithm is used for carrying out spatial distribution estimation on groundwater state data and calculating the groundwater level and aquifer change in each spatial unit;
The dynamic updating unit is used for dynamically adjusting and updating the three-dimensional dynamic model according to the groundwater state data updated in real time and the regional geologic structure change information, the dynamic updating unit continuously optimizes the precision of the three-dimensional dynamic model through iterative computation to ensure that the model can always reflect the current groundwater state, and the dynamic geologic modeling module can effectively integrate multi-source data through the cooperative work of the units, and builds and dynamically updates the three-dimensional model of the groundwater aquifer by utilizing an advanced geologic modeling algorithm to provide high-precision basic data for the deep learning prediction module of the system.
The construction of the three-dimensional dynamic model of the groundwater aquifer in the three-dimensional modeling unit comprises the following steps:
spatial distribution estimation, namely, using a spatial interpolation algorithm to carry out spatial distribution estimation on groundwater state data, and setting the spatial distribution estimation to be shared in a region Each measuring pointThe ground water level value of (2) isAnd its space coordinates areIn the target space unit by spatial interpolation algorithm (e.g. inverse distance weighting, IDW)Is of the ground water level of (1)The calculation formula is as follows: , wherein, Indicating the measuring pointWith the target space unitThe distance between the two plates is set to be equal,For interpolating powers, the algorithm estimates the ground water level of each space unit by means of weight distribution, usually based on experience or actual conditionsAnd aquifer change conditions;
Space unit combination, namely after space distribution estimation is completed, combining the space units by using a finite element method to construct a complete three-dimensional dynamic model of the groundwater aquifer, dividing a region into a plurality of finite element units, wherein the volume of each unit is WhereinRepresent the firstA finite element unit, wherein the underground water level field in each unit is set asThe formula of the finite element method is: , wherein, The gradient operator is represented by a gradient operator,Is a matrix of hydraulic conductivity coefficients,Is an underground water level field in the unit,Combining the underground water level fields of each finite element unit by a finite element method for source items (such as injection or extraction amount of underground water) in the unit to form a continuous three-dimensional aquifer model;
Model construction and integration, namely integrating the results of all finite element units into a complete three-dimensional dynamic model after finite element calculation is completed, and setting the final three-dimensional model of the groundwater aquifer as The formula is:
, wherein, The three-dimensional dynamic model constructed in the method can accurately reflect the dynamic distribution of groundwater, the change condition of a flow path and an aquifer, and provides a high-precision data base for subsequent deep learning prediction and early warning.
The dynamic updating unit includes:
Data input and initialization, wherein the dynamic updating unit receives real-time updated groundwater status data And regional geologic structure change informationFirstly, for the existing three-dimensional dynamic modelInitializing, and setting initial parameters of the modelComprising hydraulic conductivity coefficientCoefficient of water storage;
Model correction based on real-time updated groundwater status dataAnd regional geologic structure change informationFor initial model parametersCorrecting to generate new model parametersThe correction formula is:
, wherein, AndIn order to correct the coefficient of the light,AndIs old groundwater state data and geological structure information;
Iterative calculation using corrected new parameters Recalculating a three-dimensional dynamic modelHydraulic conductivity for each finite element unit by finite element methodAnd water storage coefficientAnd carrying out iterative updating, wherein an iterative equation is as follows:; , wherein, AndRespectively represent the firstThe first iterationThe hydraulic conductivity and water storage coefficient of each finite element unit,AndIs the firstThe result of the calculation of the number of iterations,AndIn order to observe the value of the value,AndIs an iteration step length;
Model convergence and updating, namely after finishing a plurality of iterative calculations, checking the convergence of the model, and when the iterative result meets the convergence condition, namely And is also provided with(WhereinConvergence threshold), stopping the iteration, and obtaining the final iteration resultAs an updated three-dimensional dynamic model, a final three-dimensional dynamic modelExpressed as: , wherein, AndFor the converged hydraulic conductivity and water storage coefficient,Through the steps, the dynamic updating unit can dynamically adjust and optimize the precision of the three-dimensional dynamic model, ensure that the model can reflect the change of the groundwater state and the dynamic adjustment of the geological structure in real time, and provide more accurate basic data for the prediction and the early warning of the system.
The deep learning prediction module comprises a data input unit, a feature extraction unit, a prediction model training unit and a trend prediction unit, wherein:
A data input unit for receiving real-time updated three-dimensional dynamic model data from the dynamic geologic modeling module Historical groundwater monitoring dataPreprocessing the received data, including data standardization and normalization, so as to ensure that the input data are analyzed on the same scale;
a feature extraction unit connected with the data input unit for extracting three-dimensional dynamic model data Historical monitoring dataExtracting features including the change rate of underground water levelIndex of water quality(Such as pH value, pollutant concentration) and spatial distribution mode thereof, and the formula of feature extraction is as follows: , wherein, Is shown at time intervalsThe change of the underground water level in the water quality indexThe feature extraction unit reduces the multidimensional water quality data into a plurality of main components through a main component analysis (PCA) method so as to reduce the complexity of the data;
The prediction model training unit is used for training a deep learning prediction model according to the extracted characteristic data, wherein the deep learning algorithm is a long-short-term memory network (LSTM), and a prediction model of the groundwater level and the water quality is established through time sequence analysis of historical data, and a loss function of model training The definition is as follows: , wherein, Represent the firstThe number of observations made is a function of the number of observations,The predicted value is represented by a value of the prediction,By minimizing the loss function for the total number of samplesOptimizing model parametersTo improve the prediction accuracy;
The trend prediction unit is connected with the prediction model training unit and is used for predicting the future groundwater level and water quality by using the trained deep learning model, and the trend prediction unit is used for updating the three-dimensional dynamic model data in real time Latest feature dataPredicting and outputting the groundwater level in a specified time period in the futureAnd water quality change trendThe prediction result formula is:; , wherein, AndIn order to train a well-trained deep learning predictive model,The deep learning prediction module can effectively utilize the historical data and the real-time updated three-dimensional dynamic model to accurately predict the change trend of the groundwater level and water quality in the future through the cooperative work of the units, and provides reliable data support for the early warning mechanism of the system.
The multi-level intelligent early warning module comprises a data receiving unit, a risk assessment unit, a threshold setting unit and an early warning signal triggering unit, wherein:
The data receiving unit is used for receiving trend prediction data from the deep learning prediction module, wherein the trend prediction data comprises future groundwater level change trend data and water quality change trend data, and an evaluation basis is provided for the subsequent unit;
the risk assessment unit is connected with the data receiving unit, carries out risk assessment based on the received trend prediction data, and assesses the risk level of each area according to the severity of the groundwater level change trend and the water quality change trend and by combining the geological conditions, the historical events and the factors of social economic influence of the area, wherein the risk level is divided into a plurality of levels including low, medium, high and extremely high, and each level corresponds to different potential hazard degrees;
The threshold setting unit is connected with the risk assessment unit and used for setting specific early warning thresholds for different risk levels according to preset safety standards and historical data respectively, and the threshold setting unit can dynamically adjust the thresholds so as to adapt to geological and hydrological conditions which change in real time;
The system comprises a threshold setting unit, a warning signal triggering unit, a multi-level intelligent warning module and a water quality control unit, wherein the threshold setting unit is connected with the warning signal triggering unit and is used for monitoring trend prediction data, immediately triggering warning signals of corresponding levels when a prediction result is detected to reach or exceed a set warning threshold, displaying the warning signals through various ways including emails, short messages, warning sounds and control centers according to different risk levels, sending the warning signals to ensure that related departments or personnel can timely receive warning information and take corresponding measures, and the multi-level intelligent warning module can establish and operate a multi-level warning mechanism according to the trend prediction data provided by the deep learning prediction module through organic combination of the units so as to ensure that the corresponding warning signals can be effectively triggered under different risk levels and timely prevent or reduce adverse effects possibly caused by underground water level changes and water quality deterioration.
The risk assessment unit includes:
trend data analysis, namely, received groundwater level change trend data And water quality change trend dataAnd analyzing, and calculating the change amplitude and rate of each area in the predicted time period, wherein for the change of the groundwater level, the calculation formula is as follows: , wherein, Indicating the rate of change of the groundwater level,As the current ground water level is to be determined,To predict time period, for water quality change trend, extracting change amplitude of key water quality index (such as pollutant concentration, pH value, etc.)To evaluate the degree of deterioration of water quality;
Regional geologic condition weighting by calculating geologic sensitivity coefficients based on the geologic condition of each region, including formation type, fault density, lithology characteristics The coefficient is used for reflecting the influence of geological conditions on groundwater change, and the geological sensitivity coefficient is obtained through expert evaluation or historical data regression, and the formula is as follows: , wherein, Is the firstThe geological sensitivity coefficients of the individual regions are calculated,Represent the firstThe influencing factors of the individual geological conditions,For the corresponding weight to be given,The higher the geological sensitivity coefficient is, the greater the sensitivity of the region to groundwater level and water quality changes is for the total number of geological conditions;
The historical event influence evaluation comprises the steps of calculating the influence coefficient of the historical event according to the historical event data of the corresponding area, including the historical disasters, pollution events or emergency events related to underground water The coefficients are obtained by weighted averaging of the frequency, severity and consequences of the historical events, given by: , wherein, Is the firstThe historical event impact coefficients for the individual regions,Represent the firstThe severity score of each event is calculated,Indicating the extent of the impact of the event on the groundwater,For the recovery time after the event has occurred,The higher the influence coefficient is for the total number of the historical events, which indicates that the corresponding area is influenced by serious groundwater in history;
socioeconomic impact assessment by evaluating socioeconomic factors including population density, economic activity density and infrastructure distribution, calculating socioeconomic impact coefficients The formula is: , wherein, Is the firstSocioeconomic impact coefficients for the individual zones,Representing the population density of the region,Indicating the density of the economic activity and,AndTo adjust the coefficients, the higher the socioeconomic impact coefficient is, the greater the potential impact of the groundwater change in the area on socioeconomic is shown;
comprehensive risk score calculation, analyzing trend data to obtain results AndAnd geological sensitivity coefficientHistorical event influence coefficientCoefficient of socioeconomic impactIn combination, calculate a composite risk score for each regionThe formula is: , wherein, To the point ofThe weight coefficient of each factor is determined according to the actual situation; Is the change rate of the groundwater level; the change amplitude of the water quality index is;
risk grading according to comprehensive risk score Dividing each region into multiple risk classes including low risk, medium risk, high risk and extremely high risk, wherein the low risk isThe middle risk isHigh risk ofAt very high risk ofThrough the steps, the risk assessment unit can comprehensively consider the groundwater level change trend, the water quality change trend, the geological conditions, the historical events and the socioeconomic factors, comprehensively assess the groundwater risk level of each area and provide accurate risk level information for the multi-level intelligent early warning module.
The intelligent response decision module comprises an early warning signal receiving unit, a strategy generating unit and an execution control unit, wherein:
The early warning signal receiving unit is used for receiving an early warning signal from the multi-level intelligent early warning module, wherein the early warning signal comprises risk level information and corresponding early warning levels, and the early warning signal receiving unit also receives three-dimensional dynamic model data generated by the dynamic geological modeling module at the same time so as to ensure that the actual state and dynamic change trend of the current underground water are considered in the decision process;
The strategy generation unit is connected with the early warning signal receiving unit and is used for formulating corresponding underground water management and emergency response strategies according to the received early warning signals and the three-dimensional dynamic model data, selecting an applicable response strategy template according to the risk level of the early warning signals, and generating specific management measures and emergency response plans by combining the underground water level, the aquifer distribution and the water quality state reflected in the three-dimensional dynamic model data, wherein the specific management measures and the emergency response plans comprise underground water extraction limit, manual water supplement, pollution source control and emergency resource scheduling;
The execution control unit is connected with the strategy generation unit and used for converting the generated underground water management and emergency response strategy into specific operation instructions and sending the operation instructions to related execution equipment or control systems, such as a water pumping station, a manual water supplementing device, a pollution treatment facility and the like.
The invention is intended to cover any alternatives, modifications, equivalents, and variations that fall within the spirit and scope of the invention. In the following description of preferred embodiments of the invention, specific details are set forth in order to provide a thorough understanding of the invention, and the invention will be fully understood to those skilled in the art without such details. In other instances, well-known methods, procedures, flows, components, circuits, and the like have not been described in detail so as not to unnecessarily obscure aspects of the present invention.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. The underground water dynamic monitoring multi-element early warning system based on data analysis is characterized by comprising 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 configuring a plurality of sensors and synchronously acquiring multidimensional data of groundwater level, soil conductivity, surface vegetation coverage and aquifer change, and integrating the acquired multidimensional information into unified groundwater state data through a data fusion algorithm;
The dynamic geologic modeling module is used for constructing a three-dimensional dynamic model of the groundwater aquifer by utilizing a geologic modeling algorithm based on the groundwater state data generated by the data fusion acquisition module and combining with the regional geologic structure information;
the deep learning prediction module acquires three-dimensional dynamic model data updated in real time from the dynamic geological modeling module, predicts future change trend of the groundwater level and the water quality through a deep learning algorithm by combining historical groundwater monitoring data, and transmits a prediction result to the multi-level intelligent early warning module to trigger an early warning mechanism;
The multi-level intelligent early warning module establishes a multi-level early warning mechanism based on the trend prediction data provided by the deep learning prediction module, sets corresponding early warning thresholds according to different risk levels, and triggers early warning signals when a prediction result reaches the corresponding thresholds;
And the intelligent response decision module combines the early warning signals transmitted by the multi-level intelligent early warning module and the three-dimensional dynamic model data generated by the dynamic geological modeling module to formulate corresponding underground water management and emergency response strategies, and realizes automatic implementation through integration with the regional water resource management system.
2. The underground water dynamic monitoring multi-element early warning system based on data analysis according to claim 1, wherein the data fusion acquisition module comprises a sensing unit, a data processing unit and a data transmission unit, wherein:
The sensing unit comprises a geological radar, multiband remote sensing equipment and a hydrological sensor, wherein the geological radar is used for detecting the depth and structural characteristics of an underground aquifer, the multiband remote sensing equipment is used for monitoring the change condition of surface vegetation coverage, the hydrological sensor is used for measuring the underground water level and the soil conductivity, and the sensing unit also comprises the step of collecting underground water and environmental information thereof in the same time and space range so as to realize synchronous collection of multidimensional data;
The data processing unit is used for receiving the multidimensional data from the sensing unit, processing the multidimensional information from different sensors through a preset data fusion algorithm, wherein the data fusion algorithm comprises a weighted average method and a Kalman filtering algorithm, and the weighted average method firstly carries out weight distribution on the sensing data from different sources;
The data transmission unit is used for transmitting the processed unified groundwater state data to the dynamic geological modeling module, and comprises a wireless transmission device and a data encryption submodule, wherein the wireless transmission device is used for transmitting the data in real time through a wireless network, and the data encryption submodule is used for conducting encryption processing on the transmitted data.
3. The underground water dynamic monitoring and multielement early warning system based on data analysis according to claim 2, characterized in that the data processing unit comprises:
The method comprises the steps of weight distribution, namely carrying out preliminary normalization processing on sensing data from geological radar, multiband remote sensing equipment and hydrological sensors to ensure that the data output by each sensor are in the same dimension;
kalman filtering, namely, applying a Kalman filtering algorithm to weighted sensing data to eliminate noise and redundant information, wherein the Kalman filtering comprises a prediction step and an updating step, and the prediction step is estimated according to the state of the last moment And state transition matrixPredicting the state of the current momentThe updating step is to combine the sensor observation dataAnd predicting stateBy calculating Kalman gainUpdating the state;
Data integration, namely integrating the sensing data subjected to Kalman filtering to form uniform groundwater state data, wherein the integrated data is The calculation formula is as follows: , wherein, Represents the state data of the integrated underground water,Is the firstTime of day (time)The data from each sensor after kalman filtering,For the number of sensors actually configured in the system,Is the corresponding weight coefficient.
4. The underground water dynamic monitoring multi-element early warning system based on data analysis according to claim 1, wherein the dynamic geologic modeling module comprises a data receiving unit, a geologic information integrating unit, a three-dimensional modeling unit and a dynamic updating unit, wherein:
The data receiving unit is used for receiving the underground water state data generated by the data fusion acquisition module and formatting the received data;
the geological information integration unit is used for collecting regional geological structure information, including stratum distribution, fault positions, lithology characteristics and geological history data, matching and integrating the regional geological structure information with the received groundwater state data and providing basic data for the three-dimensional modeling unit;
The three-dimensional modeling unit is connected with the geological information integration unit, and constructs a three-dimensional dynamic model of the groundwater aquifer by using a geological modeling algorithm, wherein the geological modeling algorithm comprises a spatial interpolation algorithm and a finite element method, the spatial interpolation algorithm is used for carrying out spatial distribution estimation on groundwater state data and calculating the groundwater level and aquifer change in each spatial unit;
And the dynamic updating unit is used for dynamically adjusting and updating the three-dimensional dynamic model according to the groundwater state data updated in real time and the regional geological structure change information, and continuously optimizing the precision of the three-dimensional dynamic model through iterative calculation.
5. The underground water dynamic monitoring multi-element early warning system based on data analysis according to claim 4, wherein the three-dimensional modeling unit comprises:
spatial distribution estimation, namely, using a spatial interpolation algorithm to carry out spatial distribution estimation on groundwater state data, and setting the spatial distribution estimation to be shared in a region Each measuring pointThe ground water level value of (2) isAnd its space coordinates areIn the target space unit by a spatial interpolation algorithmIs of the ground water level of (1)The calculation formula is as follows: , wherein, Indicating the measuring pointWith the target space unitThe distance between the two plates is set to be equal,Estimating the ground water level of each space unit by means of weight distribution for interpolating powerAnd aquifer change conditions;
Space unit combination, namely after space distribution estimation is completed, combining the space units by using a finite element method to construct a complete three-dimensional dynamic model of the groundwater aquifer, dividing a region into a plurality of finite element units, wherein the volume of each unit is WhereinRepresent the firstThe finite element units are combined with the underground water level fields of the finite element units through a finite element method to form a continuous three-dimensional aquifer model;
Model construction and integration, namely integrating the results of all finite element units into a complete three-dimensional dynamic model after finite element calculation is completed.
6. The underground water dynamic monitoring and multielement early warning system based on data analysis according to claim 5, characterized in that the dynamic updating unit comprises:
Data input and initialization, wherein the dynamic updating unit receives real-time updated groundwater status data And regional geologic structure change informationFirstly, for the existing three-dimensional dynamic modelInitializing, and setting initial parameters of the modelComprising hydraulic conductivity coefficientCoefficient of water storage;
Model correction based on real-time updated groundwater status dataAnd regional geologic structure change informationFor initial model parametersCorrecting to generate new model parametersThe correction formula is:
, wherein, AndIn order to correct the coefficient of the light,AndIs old groundwater state data and geological structure information;
Iterative calculation using corrected new parameters Recalculating a three-dimensional dynamic modelHydraulic conductivity for each finite element unit by finite element methodAnd water storage coefficientAnd carrying out iterative updating, wherein an iterative equation is as follows:; , wherein, AndRespectively represent the firstThe first iterationThe hydraulic conductivity and water storage coefficient of each finite element unit,AndIs the firstThe result of the calculation of the number of iterations,AndIn order to observe the value of the value,AndIs an iteration step length;
Model convergence and updating, namely after finishing a plurality of iterative calculations, checking the convergence of the model, and when the iterative result meets the convergence condition, namely And is also provided withStopping the iteration and obtaining the final iteration resultAs an updated three-dimensional dynamic model, a final three-dimensional dynamic modelExpressed as: , wherein, AndFor the converged hydraulic conductivity and water storage coefficient,Is the total number of finite element units.
7. The underground water dynamic monitoring multi-element early warning system based on data analysis according to claim 1, wherein the deep learning prediction module comprises a data input unit, a feature extraction unit, a prediction model training unit and a trend prediction unit, wherein:
A data input unit for receiving real-time updated three-dimensional dynamic model data from the dynamic geologic modeling module Historical groundwater monitoring dataPreprocessing the received data, including data standardization and normalization, so as to ensure that the input data are analyzed on the same scale;
A feature extraction unit for extracting three-dimensional dynamic model data Historical monitoring dataExtracting features including the rate of change of groundwater levelIndex of water qualityAnd its spatial distribution pattern;
The prediction model training unit is used for training a deep learning prediction model according to the extracted characteristic data, wherein the deep learning algorithm is a long-term and short-term memory network, and a prediction model of the groundwater level and the water quality is built through time sequence analysis of historical data;
The trend prediction unit is used for predicting the future ground water level and the water quality by using the trained deep learning model and is used for updating the three-dimensional dynamic model data in real time Latest feature dataPredicting and outputting the groundwater level in a specified time period in the futureAnd water quality change trend。
8. The underground water dynamic monitoring multi-element early warning system based on data analysis according to claim 1, wherein the multi-level intelligent early warning module comprises a data receiving unit, a risk assessment unit, a threshold setting unit and an early warning signal triggering unit, wherein:
the data receiving unit is used for receiving trend prediction data from the deep learning prediction module, wherein the trend prediction data comprises future groundwater level change trend data and water quality change trend data;
The risk assessment unit is used for carrying out risk assessment based on the received trend prediction data, and assessing the risk level of each region according to the severity of the groundwater level change trend and the water quality change trend and combining the geological conditions, the historical events and the factors of socioeconomic effects of the region, wherein the risk level is divided into a plurality of levels including low, medium, high and extremely high, and each level corresponds to different potential hazard degrees;
The threshold setting unit is used for setting specific early warning thresholds for different risk levels according to preset safety standards and historical data;
The early warning signal triggering unit is used for monitoring trend prediction data, triggering early warning signals of corresponding grades immediately when the prediction result is detected to reach or exceed a set early warning threshold value, and sending out early warning signals through various ways including email, short messages, alarm sounds and control center display according to different risk grades, so that relevant departments or personnel can receive early warning information in time and take corresponding measures.
9. The underground water dynamic monitoring multi-element early warning system based on data analysis of claim 8, characterized in that the risk assessment unit comprises:
trend data analysis, namely, received groundwater level change trend data And water quality change trend dataAnalyzing, and calculating the variation amplitude and the variation rate of each region in the predicted time period;
Regional geologic condition weighting by calculating geologic sensitivity coefficients based on the geologic condition of each region, including formation type, fault density, lithology characteristics The method is used for reflecting the influence of geological conditions on groundwater change;
The historical event influence evaluation comprises the steps of calculating the influence coefficient of the historical event according to the historical event data of the corresponding area, including the historical disasters, pollution events or emergency events related to underground water ;
Socioeconomic impact assessment by evaluating socioeconomic factors including population density, economic activity density and infrastructure distribution, calculating socioeconomic impact coefficients;
Comprehensive risk score calculation, namely analyzing trend data and obtaining geological sensitivity coefficientHistorical event influence coefficientCoefficient of socioeconomic impactIn combination, calculate a composite risk score for each regionThe formula is: , wherein, To the point ofThe weight coefficient of each factor; Is the change rate of the groundwater level; the change amplitude of the water quality index is;
risk grading according to comprehensive risk score Dividing each region into multiple risk classes including low risk, medium risk, high risk and extremely high risk, wherein the low risk isThe middle risk isHigh risk ofAt very high risk of。
10. The underground water dynamic monitoring multi-element early warning system based on data analysis according to claim 1, wherein the intelligent response decision module comprises an early warning signal receiving unit, a strategy generating unit and an execution control unit, wherein:
The early warning signal receiving unit is used for receiving an early warning signal from the multi-level intelligent early warning module, wherein the early warning signal comprises risk grade information and corresponding early warning grade, and simultaneously receives three-dimensional dynamic model data generated by the dynamic geological modeling module;
The strategy generation unit is used for formulating corresponding underground water management and emergency response strategies according to the received early warning signals and the three-dimensional dynamic model data, selecting a response strategy template according to the risk level of the early warning signals, and generating specific management measures and emergency response plans by combining the underground water level, the aquifer distribution and the water quality state reflected in the three-dimensional dynamic model data, wherein the specific management measures and the emergency response plans comprise underground water extraction limit, manual water supplement, pollution source control and emergency resource scheduling;
and the execution control unit is used for converting the generated underground water management and emergency response strategy into specific operation instructions and sending the operation instructions to related execution equipment or control systems.
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| CN120893328A (en) * | 2025-09-30 | 2025-11-04 | 机械工业勘察设计研究院有限公司 | A system and method for predicting the water level of buoyancy-resistant structures |
| CN121185676A (en) * | 2025-11-25 | 2025-12-23 | 陕西正盛环境检测有限公司 | An integrated system for stratified sampling and on-site detection for coordinated monitoring of soil and groundwater. |
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| CN120893328A (en) * | 2025-09-30 | 2025-11-04 | 机械工业勘察设计研究院有限公司 | A system and method for predicting the water level of buoyancy-resistant structures |
| CN120893328B (en) * | 2025-09-30 | 2026-02-03 | 机械工业勘察设计研究院有限公司 | Anti-floating fortification water level prediction system and method |
| CN121185676A (en) * | 2025-11-25 | 2025-12-23 | 陕西正盛环境检测有限公司 | An integrated system for stratified sampling and on-site detection for coordinated monitoring of soil and groundwater. |
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