Underground water dynamic monitoring multi-element early warning system based on data analysis

Abstract

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.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater monitoring, and in particular to a multi-element early warning system for groundwater dynamic monitoring based on data analysis. Background Art

[0002] With the rapid development of industrialization and urbanization, groundwater resources are facing unprecedented pressure and challenges. Fluctuations in groundwater levels and changes in water quality are directly related to drinking water safety, agricultural irrigation, and ecological environmental stability. Although existing technologies are able to monitor certain dynamic changes in groundwater, such as water levels and basic water quality parameters, these technologies usually rely on point monitoring and periodic data updates, making it difficult to reflect the comprehensive status and rapid changes of groundwater resources in real time. In addition, existing monitoring systems often lack effective data fusion capabilities and are unable to integrate multidimensional data from different sensors and monitoring points, resulting in insufficient decision-making support capabilities in groundwater management and emergency response.

[0003] Currently, the main technical challenges facing groundwater monitoring and early warning systems include how to accurately integrate multi-source monitoring data, update geological models in real time, and quickly respond to groundwater emergencies. The existence of these problems often makes groundwater management less responsive and unable to effectively prevent or mitigate the negative impacts of groundwater pollution and over-exploitation.

[0004] Therefore, there is an urgent need for a system that can monitor the dynamic changes of groundwater in real time. The system should be able to not only accurately integrate and analyze data from various monitoring equipment such as geological radars and hydrological sensors, but also provide multi-level intelligent early warnings based on these data. Summary of the Invention

[0005] Based on the above objectives, the present invention provides a multi-element early warning system for groundwater dynamic monitoring based on data analysis.

[0006] A multi-level early warning system for groundwater dynamic monitoring based on data analysis, including 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: Data fusion acquisition module: Equipped with multiple sensors, it is used to synchronously collect multi-dimensional data on groundwater level, soil conductivity, surface vegetation cover, and aquifer changes. The collected multi-dimensional information is then integrated into unified groundwater status data through data fusion algorithms. Dynamic geological modeling module: Based on the groundwater status data generated by the data fusion acquisition module and combined with regional geological structure information, a geological modeling algorithm is used to construct a three-dimensional dynamic model of the groundwater aquifer; Deep Learning Prediction Module: This module obtains real-time updated 3D dynamic model data from the dynamic geological modeling module, combines it with historical groundwater monitoring data, and uses deep learning algorithms to predict future trends in groundwater levels and water quality. The prediction results are then transmitted to the multi-level intelligent early warning module to trigger the early warning mechanism. Multi-level intelligent early warning module: Based on the trend prediction data provided by the deep learning prediction module, a multi-level early warning mechanism is established. The corresponding warning thresholds are set according to different risk levels, and the warning signal is triggered when the prediction results reach the corresponding thresholds. Intelligent response decision module: Combining the warning signals transmitted by the multi-level intelligent early warning module with the three-dimensional dynamic model data generated by the dynamic geological modeling module, it formulates corresponding groundwater management and emergency response strategies, and realizes automated implementation through integration with the regional water resources management system.

[0007] Optionally, the data fusion acquisition module includes a sensing unit, a data processing unit and a data transmission unit; wherein: Sensing unit: includes geological radar, multi-band remote sensing equipment and hydrological sensors. The geological radar is used to detect the depth and structural characteristics of underground aquifers, the multi-band remote sensing equipment is used to monitor changes in surface vegetation cover, and the hydrological sensor is used to measure groundwater levels and soil conductivity. The sensing unit also collects groundwater and environmental information within the same time and space range, realizing the synchronous collection of multi-dimensional data. Data processing unit: used to receive multidimensional data from the sensor units and process the multidimensional information from different sensors using a preset data fusion algorithm. The data fusion algorithm includes a weighted average method and a Kalman filter algorithm. The weighted average method first assigns weights to the sensor data from different sources; then the Kalman filter algorithm is used to eliminate noise and redundant information, and finally the processed data is integrated into unified groundwater status data; Data transmission unit: used to transmit the processed unified groundwater status data to the dynamic geological modeling module. The data transmission unit includes a wireless transmission device and a data encryption submodule. The wireless transmission device transmits data in real time through a wireless network. The data encryption submodule is used to encrypt the transmitted data.

[0008] Optionally, the data processing unit includes: Weight distribution: First, perform preliminary normalization on the sensor data from geological radar, multi-band remote sensing equipment and hydrological sensors to make the data output by each sensor in the same dimension; then use the weighted average method to distribute the weights of the normalized data; Kalman filter: Apply the Kalman filter algorithm to the weighted sensor data to eliminate noise and redundant information. Kalman filter includes prediction step and update step; the prediction step is based on the state estimation of the previous moment. and the state transition matrix , predict the current state ; The update step is to combine sensor observation data and predicted status , by calculating the Kalman gain Update the status; Data integration: Integrate the sensor data after Kalman filter processing to form unified groundwater status data. Let the integrated data be , the calculation formula is: ,in, represents the integrated groundwater status data, For the Moment The data from each sensor is Kalman filtered. is the number of sensors actually configured in the system, is the corresponding weight coefficient.

[0009] Optionally, the dynamic geological modeling module includes a data receiving unit, a geological information integration unit, a three-dimensional modeling unit and a dynamic update unit; wherein: Data receiving unit: used to receive the groundwater status data generated by the data fusion acquisition module and format the received data; Geological information integration unit: used to collect regional geological structure information, including stratum distribution, fault location, lithologic characteristics, and geological history data, and match and integrate the regional geological structure information with the received groundwater status data to provide basic data for the 3D modeling unit; 3D modeling unit: connected to the geological information integration unit, using a geological modeling algorithm to construct a 3D dynamic model of the groundwater aquifer. The geological modeling algorithm includes a spatial interpolation algorithm and a finite element method. The spatial interpolation algorithm is used to estimate the spatial distribution of groundwater status data and calculate the groundwater level and aquifer changes in each spatial unit. The finite element method is used to combine spatial units to construct a 3D dynamic model of the groundwater aquifer. The 3D dynamic model is used to reflect the dynamic distribution and flow path of groundwater. Dynamic update unit: used to dynamically adjust and update the three-dimensional dynamic model based on real-time updated groundwater status data and regional geological structure change information. The dynamic update unit continuously optimizes the accuracy of the three-dimensional dynamic model through iterative calculations.

[0010] Optionally, the three-dimensional modeling unit includes: Spatial distribution estimation: Use spatial interpolation algorithm to estimate the spatial distribution of groundwater status data. measuring points, each measuring point The groundwater level is Its spatial coordinates are , through the spatial interpolation algorithm, in the target space unit Groundwater level The calculation formula is: ,in, Indicates measuring point Target space unit The distance between The interpolation power is used to estimate the groundwater level of each spatial unit by weight distribution. and aquifer changes; Spatial unit combination: After completing the spatial distribution estimation, the finite element method is used to combine these spatial units to construct a complete three-dimensional dynamic model of the groundwater aquifer. The area is first divided into multiple finite element units, and the volume of each unit is ,in Indicates the Finite element units are used to combine the groundwater level fields of each finite element unit to form a continuous three-dimensional aquifer model. Model construction and integration: After completing the finite element calculation, the results of all finite element units are integrated into a complete three-dimensional dynamic model.

[0011] Optionally, the dynamic update unit includes: Data input and initialization: Dynamic update unit receives real-time updated groundwater status data and regional geological structure change information , first of all, the existing three-dimensional dynamic model Initialize and set the initial parameters of the model , including hydraulic conductivity Water storage coefficient ; Model calibration: based on real-time updated groundwater status data and regional geological structure change information , for the initial model parameters Perform corrections to generate new model parameters , the correction formula is: ,in, and is the correction factor, and For old groundwater status data and geological structure information; Iterative calculation: Use the corrected new parameters Recalculate 3D dynamic model , the hydraulic conductivity of each finite element is calculated by the finite element method. and water storage coefficient Perform iterative update, the iterative equation is: ; ,in, and Respectively represent The first iteration The hydraulic conductivity and storage coefficient of each finite element, and For the The calculation results of the iterations are and is the observed value, and is the iteration step length; Model convergence and update: After completing several iterative calculations, check the convergence of the model. When the iterative results meet the convergence conditions, that is, and , then stop the iteration and convert the final iteration result As the updated 3D dynamic model, the final 3D dynamic model Expressed as: ,in, and are the hydraulic conductivity and storage coefficient after convergence, is the total number of finite element elements.

[0012] Optionally, the deep learning prediction module includes a data input unit, a feature extraction unit, a prediction model training unit, and a trend prediction unit; wherein: Data input unit: used to receive real-time updated 3D dynamic model data from the dynamic geological modeling module and historical groundwater monitoring data , and preprocess the received data, including data standardization and normalization, to ensure that the input data are analyzed at the same scale; Feature extraction unit: used to extract data from 3D dynamic models and historical monitoring data Extract features from the groundwater level change rate , water quality indicators and its spatial distribution patterns; Prediction model training unit: used to train a deep learning prediction model based on the extracted feature data. The deep learning algorithm is a long short-term memory network. Through time series analysis of historical data, a prediction model for groundwater level and water quality is established. Trend prediction unit: used to predict future groundwater levels and water quality using a trained deep learning model. The trend prediction unit is based on real-time updated three-dimensional dynamic model data. and the latest feature data Make predictions and output the groundwater level within a specified time period in the future and water quality trends .

[0013] Optionally, the multi-level intelligent early warning module includes a data receiving unit, a risk assessment unit, a threshold setting unit and an early warning signal triggering unit; wherein: Data receiving unit: used to receive trend prediction data from the deep learning prediction module, wherein the trend prediction data includes future groundwater level change trend data and water quality change trend data; Risk Assessment Unit: Conducts risk assessment based on received trend forecast data. It assesses the risk level of each area based on the severity of groundwater level and water quality trends, combined with regional geological conditions, historical events, and socioeconomic factors. The risk level is divided into multiple levels, including low, medium, high, and very high, with each level corresponding to a different degree of potential harm. Threshold setting unit: used to set specific warning thresholds for different risk levels based on predetermined safety standards and historical data; Early warning signal trigger unit: used to monitor trend forecast data and immediately trigger the corresponding level of early warning signal when it detects that the forecast result reaches or exceeds the set early warning threshold. According to the different risk levels, early warning signals are issued through various channels including email, SMS, alarm sound, and control center display to ensure that relevant departments or personnel can receive early warning information in time and take corresponding measures.

[0014] Optionally, the risk assessment unit includes: Trend data analysis: Received groundwater level change trend data and water quality trend data Conduct analysis to calculate the magnitude and rate of change for each region over the forecast period; Weighted regional geological conditions: Calculate the geological sensitivity coefficient based on the geological conditions of each region, including stratum type, fault density, and lithologic characteristics , used to reflect the impact of geological conditions on groundwater changes; Historical event impact assessment: Combine historical event data of the corresponding area, including historical floods, pollution incidents or groundwater-related emergencies, to calculate the historical event impact coefficient ; Socioeconomic impact assessment: Calculate the socioeconomic impact coefficient by evaluating socioeconomic factors, including population density, economic activity density, and infrastructure distribution. ; Comprehensive risk score calculation: trend data analysis results and geological sensitivity coefficient , historical event impact coefficient and socioeconomic impact coefficient Combined, calculate the comprehensive risk score for each area , the formula is: ,in, to is the weight coefficient of each factor; is the rate of change of groundwater level; is the variation range of water quality index; Risk level classification: Based on comprehensive risk score , each area is divided into multiple risk levels, including low risk, medium risk, high risk and extremely high risk; among them, low risk is ; Medium risk ; High risk ; Very high risk .

[0015] Optionally, the intelligent response decision module includes a warning signal receiving unit, a strategy generating unit and an execution control unit; wherein: Warning signal receiving unit: used to receive warning signals from the multi-level intelligent warning module, the warning signals including risk level information and corresponding warning levels, and at the same time receive the three-dimensional dynamic model data generated by the dynamic geological modeling module; Strategy Generation Unit: This unit is used to formulate corresponding groundwater management and emergency response strategies based on the received warning signals and 3D dynamic model data. It selects a response strategy template based on the risk level of the warning signal. Furthermore, it combines the groundwater level, aquifer distribution, and water quality reflected in the 3D dynamic model data to generate specific management measures and emergency response plans, including groundwater extraction restrictions, artificial water recharge, pollution source control, and emergency resource scheduling. Execution control unit: used to convert the generated groundwater management and emergency response strategies into specific operation instructions, and send the operation instructions to relevant execution equipment or control systems.

[0016] Beneficial effects of the present invention: This invention, by real-time integration of multi-source data from geological radar, multi-band remote sensing equipment, and hydrological sensors, can provide a comprehensive and detailed groundwater status monitoring solution. It can not only continuously track changes in groundwater levels and water quality, but also update geological models in real time to ensure that changes in groundwater resources can be captured immediately and accurately reflected. This highly integrated monitoring capability enables groundwater management departments to more quickly identify potential risks and problems, such as pollution spread or abnormal water level drops, so as to adopt more effective preventive measures and response strategies.

[0017] This invention, by building a multi-level intelligent early warning mechanism and deeply integrating it with the regional water resources management system, realizes an automated process from data collection, risk assessment to emergency response. This not only greatly improves the speed and accuracy of emergency response, but also optimizes resource allocation and management efficiency. Through intelligent early warning and response strategies, it can effectively reduce the socioeconomic losses caused by groundwater problems, enhance public and ecological security, and provide strong technical support for the sustainable management of groundwater resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 Schematic diagram of a multi-element early warning system for dynamic monitoring of groundwater according to an embodiment of the present invention; Figure 2 Schematic diagram of a multi-level intelligent early warning module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0021] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0022] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0023] like Figure 1-Figure 2 As shown, a multi-element early warning system for groundwater dynamic monitoring based on data analysis includes 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: Data fusion acquisition module: Equipped with multiple sensors, it is used to synchronously collect multi-dimensional data on groundwater level, soil conductivity, surface vegetation cover, and aquifer changes. The collected multi-dimensional information is then integrated into unified groundwater status data through data fusion algorithms for use by subsequent modules. Dynamic geological modeling module: Based on the groundwater status data generated by the data fusion acquisition module and combined with regional geological structure information, a geological modeling algorithm is used to construct a three-dimensional dynamic model of the groundwater aquifer. The model is updated in real time and passed to the deep learning prediction module for further trend analysis and risk assessment; Deep Learning Prediction Module: This module obtains real-time updated 3D dynamic model data from the dynamic geological modeling module, combines it with historical groundwater monitoring data, and uses deep learning algorithms to predict future trends in groundwater levels and water quality. The prediction results are then transmitted to the multi-level intelligent early warning module to trigger the early warning mechanism. Multi-level intelligent early warning module: Based on the trend prediction data provided by the deep learning prediction module, a multi-level early warning mechanism is established. The corresponding warning thresholds are set according to different risk levels. When the prediction results reach the corresponding thresholds, the warning signal is triggered. At the same time, the warning information is transmitted to the intelligent response decision module to generate specific response strategies. Intelligent response decision module: Combining the warning signals transmitted by the multi-level intelligent early warning module with the three-dimensional dynamic model data generated by the dynamic geological modeling module, it formulates corresponding groundwater management and emergency response strategies, and realizes automated implementation through integration with the regional water resources management system.

[0024] The data fusion acquisition module includes a sensing unit, a data processing unit and a data transmission unit; wherein: Sensing unit: includes geological radar, multi-band remote sensing equipment and hydrological sensors. The geological radar is used to detect the depth and structural characteristics of underground aquifers, the multi-band remote sensing equipment is used to monitor changes in surface vegetation cover, and the hydrological sensor is used to measure groundwater levels and soil conductivity. The sensing unit also collects groundwater and environmental information within the same time and space range, realizing the synchronous collection of multi-dimensional data. Data processing unit: connected to the sensor unit, used to receive multidimensional data from the sensor unit and process the multidimensional information from different sensors through a preset data fusion algorithm. The data fusion algorithm includes the weighted average method and the Kalman filter algorithm. The weighted average method first assigns weights to the sensor data from different sources to reflect the importance of each sensor data; then the Kalman filter algorithm is used to eliminate noise and redundant information, and finally the processed data is integrated into unified groundwater status data for analysis and use by subsequent modules; Data transmission unit: used to transmit the processed unified groundwater status data to the dynamic geological modeling module. The data transmission unit includes a wireless transmission device and a data encryption submodule. The wireless transmission device transmits data in real time through a wireless network. The data encryption submodule is used to encrypt the transmitted data to ensure the security and integrity of the data during the transmission process. Through the close cooperation of the above units, the data fusion acquisition module can synchronously collect, process and integrate the status data of groundwater and its environment in multiple dimensions, ensuring that the system can perform subsequent analysis and early warning based on comprehensive and accurate data.

[0025] The data processing unit includes: Weight distribution: First, perform preliminary normalization on the sensor data from geological radar, multi-band remote sensing equipment, and hydrological sensors to make the data output by each sensor comparable in the same dimension; then use the weighted average method to distribute the weights of the normalized data. The calculation formula is: ,in, Indicates the The weight of each sensor, Indicates the The standard deviation of the sensor data, is the total number of sensors. Through this formula, the importance of different sensors in data fusion is determined. Sensors with higher weights contribute more to the final result. Kalman filter: Apply the Kalman filter algorithm to the weighted sensor data to eliminate noise and redundant information. Kalman filter includes prediction step and update step; the prediction step is to estimate the state based on the previous moment. and the state transition matrix , predict the current state , the formula is: ,in, For the The predicted state at the moment, is the state transition matrix, is the control input matrix, For the The control input at the moment; the update step is to combine the sensor observation data and predicted status , by calculating the Kalman gain Update the status, the formula is: ; ,in, For the The Kalman gain at time t, is the forecast error covariance matrix, is the observation matrix, is the observation noise covariance matrix, For the Update the state estimate at each moment; through this step, noise and redundant information are filtered out to obtain more accurate sensor data; Data integration: Integrate the sensor data after Kalman filter processing to form unified groundwater status data. Let the integrated data be , the calculation formula is: ,in, represents the integrated groundwater status data, For the Moment The data from each sensor is Kalman filtered. is the number of sensors actually configured in the system, is the corresponding weight coefficient. Through the above calculation, the integrated data accurately reflects the real state of groundwater, providing a reliable data basis for subsequent modules. Through the above steps, the data processing unit can effectively process and integrate multi-dimensional data from different sensors, providing accurate and reliable basic data for the dynamic monitoring and early warning of the system.

[0026] The dynamic geological modeling module includes a data receiving unit, a geological information integration unit, a 3D modeling unit, and a dynamic update unit; wherein: Data receiving unit: used to receive groundwater status data generated by the data fusion acquisition module, including multi-dimensional information on groundwater level, soil conductivity, surface vegetation cover and aquifer changes, and format the received data to ensure data consistency in subsequent processing; Geological information integration unit: used to collect regional geological structure information, including stratum distribution, fault location, lithologic characteristics, and geological history data, and match and integrate the regional geological structure information with the received groundwater status data to provide basic data for the 3D modeling unit; 3D modeling unit: Connected to the geological information integration unit, it uses geological modeling algorithms to construct a 3D dynamic model of the groundwater aquifer. The geological modeling algorithms include spatial interpolation algorithms and finite element methods. The spatial interpolation algorithm is used to estimate the spatial distribution of groundwater status data and calculate the groundwater level and aquifer changes in each spatial unit. The finite element method is used to combine spatial units to construct a 3D dynamic model of the groundwater aquifer. The 3D dynamic model is used to reflect the dynamic distribution and flow path of groundwater. Dynamic update unit: used to dynamically adjust and update the three-dimensional dynamic model based on real-time updated groundwater status data and regional geological structure change information. The dynamic update unit continuously optimizes the accuracy of the three-dimensional dynamic model through iterative calculations to ensure that the model can always reflect the current groundwater status. Through the collaborative work of the above units, the dynamic geological modeling module can effectively integrate multi-source data, use advanced geological modeling algorithms, construct and dynamically update the three-dimensional model of the groundwater aquifer, and provide high-precision basic data for the system's deep learning prediction module.

[0027] The 3D dynamic model of the groundwater aquifer constructed in the 3D modeling unit includes: Spatial distribution estimation: Use spatial interpolation algorithm to estimate the spatial distribution of groundwater status data. measuring points, each measuring point The groundwater level is Its spatial coordinates are , through spatial interpolation algorithms (such as inverse distance weighted method, IDW), in the target space unit Groundwater level The calculation formula is: ,in, Indicates measuring point Target space unit The distance between The interpolation power is usually selected based on experience or actual conditions. The algorithm estimates the groundwater level of each spatial unit by weight distribution. and aquifer changes; Spatial unit combination: After completing the spatial distribution estimation, the finite element method is used to combine these spatial units to construct a complete three-dimensional dynamic model of the groundwater aquifer. The area is first divided into multiple finite element units, and the volume of each unit is ,in Indicates the finite element units; let the groundwater level field in each unit be , then the formula of the finite element method is: ,in, represents the gradient operator, is the hydraulic conductivity matrix, is the groundwater level field within the unit, For source terms within the unit (e.g., groundwater injection or withdrawal), the groundwater level fields of each finite element unit are combined using the finite element method to form a continuous three-dimensional aquifer model; Model construction and integration: After completing the finite element calculation, the results of all finite element units are integrated into a complete three-dimensional dynamic model. The final three-dimensional model of the groundwater aquifer is , the formula is: ,in, is the total number of finite element units in the area. In this way, the constructed three-dimensional dynamic model can accurately reflect the dynamic distribution of groundwater, flow path and changes in aquifers, providing a high-precision data basis for subsequent deep learning prediction and early warning.

[0028] The dynamic update unit includes: Data input and initialization: Dynamic update unit receives real-time updated groundwater status data and regional geological structure change information , first of all, the existing three-dimensional dynamic model Initialize and set the initial parameters of the model , including hydraulic conductivity Water storage coefficient ; Model calibration: based on real-time updated groundwater status data and regional geological structure change information , for the initial model parameters Perform corrections to generate new model parameters , the correction formula is: ,in, and is the correction factor, and For old groundwater status data and geological structure information; Iterative calculation: Use the corrected new parameters Recalculate 3D dynamic model , the hydraulic conductivity of each finite element is calculated by the finite element method. and water storage coefficient Perform iterative update, the iterative equation is: ; ,in, and Respectively represent The first iteration The hydraulic conductivity and storage coefficient of each finite element, and For the The calculation results of the iterations are and is the observed value, and is the iteration step length; Model convergence and update: After completing several iterative calculations, check the convergence of the model. When the iterative results meet the convergence conditions, that is, and (in is the convergence threshold), the iteration is stopped and the final iteration result is As the updated 3D dynamic model, the final 3D dynamic model Expressed as: ,in, and are the hydraulic conductivity and storage coefficient after convergence, is the total number of finite element units; through the above steps, the dynamic update unit can dynamically adjust and optimize the accuracy of the three-dimensional dynamic model, ensuring that the model can reflect the changes in groundwater status and dynamic adjustments of geological structure in real time, and provide more accurate basic data for system prediction and early warning.

[0029] The deep learning prediction module includes a data input unit, a feature extraction unit, a prediction model training unit, and a trend prediction unit; wherein: Data input unit: used to receive real-time updated 3D dynamic model data from the dynamic geological modeling module and historical groundwater monitoring data , and preprocess the received data, including data standardization and normalization, to ensure that the input data are analyzed at the same scale; Feature extraction unit: The feature extraction unit is connected to the data input unit and is used to extract the 3D dynamic model data. and historical monitoring data Extract features from the groundwater level change rate , water quality indicators (such as pH value, pollutant concentration) and its spatial distribution pattern, the feature extraction formula is: ,in, Indicates the time interval Changes in groundwater levels within the area; water quality indicators ,The feature extraction unit reduces the multidimensional water quality data into several ,main components through the principal component analysis (PCA) method to reduce the ,complexity of the data; Prediction model training unit: used to train deep learning prediction models based on extracted feature data. The deep learning algorithm is the long short-term memory network (LSTM). Through time series analysis of historical data, a prediction model for groundwater level and water quality is established. The loss function of the model training is Defined as: ,in, Indicates the observations, represents the predicted value, is the total number of samples, by minimizing the loss function , optimize model parameters To improve prediction accuracy; Trend prediction unit: The trend prediction unit is connected to the prediction model training unit and is used to predict the future groundwater level and water quality using the trained deep learning model. The trend prediction unit is based on the real-time updated three-dimensional dynamic model data. and the latest feature data Make predictions and output the groundwater level within a specified time period in the future and water quality trends , the prediction result formula is: ; ,in, and For the trained deep learning prediction model, The deep learning prediction module is the latest extracted feature data; through the collaborative work of the above units, the deep learning prediction module can effectively utilize historical data and real-time updated three-dimensional dynamic models to accurately predict future trends in groundwater levels and water quality, providing reliable data support for the system's early warning mechanism.

[0030] The multi-level intelligent early warning module includes a data receiving unit, a risk assessment unit, a threshold setting unit, and an early warning signal triggering unit; wherein: Data receiving unit: used to receive trend prediction data from the deep learning prediction module. The trend prediction data includes future groundwater level change trend data and water quality change trend data, providing an evaluation basis for subsequent units; Risk Assessment Unit: Connected to the data receiving unit, it conducts risk assessment based on the received trend forecast data. It assesses the risk level of each area based on the severity of the groundwater level change trend and water quality change trend, combined with the regional geological conditions, historical events and socio-economic factors. The risk level is divided into multiple levels, including low, medium, high and very high, and each level corresponds to a different degree of potential harm; Threshold setting unit: connected to the risk assessment unit, used to set specific warning thresholds for different risk levels based on predetermined safety standards and historical data. The threshold setting unit can also dynamically adjust these thresholds to adapt to real-time changing geological and hydrological conditions; Early warning signal triggering unit: connected to the threshold setting unit, used to monitor trend forecast data, and immediately trigger the corresponding level of early warning signal when it detects that the forecast result reaches or exceeds the set early warning threshold. According to the different risk levels, early warning signals are issued through various channels including email, text messages, alarm sounds, and control center displays to ensure that relevant departments or personnel can receive early warning information in time and take corresponding measures; through the organic combination of the above units, the multi-level intelligent early warning module can establish and operate a multi-level early warning mechanism based on the trend forecast data provided by the deep learning prediction module, ensuring that the corresponding early warning signals can be effectively triggered under different risk levels, and timely prevent or reduce the adverse effects that may be caused by groundwater level changes and water quality deterioration.

[0031] The risk assessment unit includes: Trend data analysis: Received groundwater level change trend data and water quality trend data Perform analysis and calculate the magnitude and rate of change in each region during the forecast period; for groundwater level changes, the calculation formula is as follows: ,in, represents the rate of change of groundwater level, is the current groundwater level, To predict the water quality change trend over the forecast period, extract the change range of key water quality indicators (such as pollutant concentration, pH value, etc.) , to assess the extent of water quality deterioration; Weighted regional geological conditions: Calculate the geological sensitivity coefficient based on the geological conditions of each region, including stratum type, fault density, and lithologic characteristics This coefficient is used to reflect the impact of geological conditions on groundwater changes. The geological sensitivity coefficient is obtained through expert evaluation or historical data regression. The formula is: ,in, For the The geological sensitivity coefficient of the region, Indicates the The influencing factors of geological conditions are is the corresponding weight, is the total number of geological conditions. The higher the geological sensitivity coefficient, the greater the sensitivity of the region to changes in groundwater level and water quality. Historical event impact assessment: Combine historical event data of the corresponding area, including historical floods, pollution incidents or groundwater-related emergencies, to calculate the historical event impact coefficient , which is obtained by weighted average of the frequency, severity and consequences of historical events, and the formula is: ,in, For the The impact coefficient of historical events in a region, Indicates the The severity score of the event, Indicates the impact of the event on groundwater. is the recovery time after the incident, is the total number of historical events. A higher impact coefficient indicates that the corresponding area has been severely affected by groundwater in history. Socioeconomic impact assessment: Calculate the socioeconomic impact coefficient by evaluating socioeconomic factors, including population density, economic activity density, and infrastructure distribution. , the formula is: ,in, For the The socioeconomic impact coefficient of a region, represents the population density of the area, represents the density of economic activities, and For adjustment coefficients, the higher the socio-economic impact coefficient, the greater the potential impact of groundwater changes on the socio-economy in the region; Comprehensive risk score calculation: Trend data analysis results and and geological sensitivity coefficient , historical event impact coefficient and socioeconomic impact coefficient Combined, calculate the comprehensive risk score for each area , the formula is: ,in, to is the weight coefficient of each factor, which is determined according to the actual situation; is the rate of change of groundwater level; is the variation range of water quality index; Risk level classification: Based on comprehensive risk score , each area is divided into multiple risk levels, including low risk, medium risk, high risk and extremely high risk; among them, low risk is ; Medium risk ; High risk ; Very high risk After the risk level division is completed, the system will take corresponding early warning measures according to different risk levels; through the above steps, the risk assessment unit can comprehensively consider the groundwater level change trend, water quality change trend, geological conditions, historical events and socio-economic factors, comprehensively evaluate the groundwater risk level of each area, and provide accurate risk level information for the multi-level intelligent early warning module.

[0032] The intelligent response decision module includes an early warning signal receiving unit, a strategy generating unit and an execution control unit; wherein: Warning signal receiving unit: used to receive warning signals from the multi-level intelligent warning module. The warning signals include risk level information and corresponding warning levels. The warning signal receiving unit also receives the three-dimensional dynamic model data generated by the dynamic geological modeling module to ensure that the actual status and dynamic change trend of the current groundwater are taken into account in the decision-making process; Strategy Generation Unit: This unit is connected to the early warning signal receiving unit and is used to formulate corresponding groundwater management and emergency response strategies based on the received early warning signals and 3D dynamic model data. It selects an appropriate response strategy template based on the risk level of the early warning signal. Furthermore, it combines the groundwater level, aquifer distribution, and water quality reflected in the 3D dynamic model data to generate specific management measures and emergency response plans, including groundwater extraction restrictions, artificial water recharge, pollution source control, and emergency resource scheduling. Execution control unit: connected to the strategy generation unit, used to convert the generated groundwater management and emergency response strategies into specific operation instructions, and send the operation instructions to relevant execution equipment or control systems, such as pumping stations, artificial water replenishment equipment, pollution control facilities, etc.

[0033] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0034] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A multi-element early warning system for groundwater dynamic monitoring based on data analysis, characterized in that: It includes data fusion acquisition module, dynamic geological modeling module, deep learning prediction module, multi-level intelligent early warning module and intelligent response decision module; among them: Data fusion acquisition module: Equipped with multiple sensors, it is used to synchronously collect multi-dimensional data on groundwater level, soil conductivity, surface vegetation cover, and aquifer changes. The collected multi-dimensional information is then integrated into unified groundwater status data through data fusion algorithms. Dynamic geological modeling module: Based on the groundwater status data generated by the data fusion acquisition module and combined with regional geological structure information, a geological modeling algorithm is used to construct a three-dimensional dynamic model of the groundwater aquifer; Deep Learning Prediction Module: This module obtains real-time updated 3D dynamic model data from the dynamic geological modeling module, combines it with historical groundwater monitoring data, and uses deep learning algorithms to predict future trends in groundwater levels and water quality. The prediction results are then transmitted to the multi-level intelligent early warning module to trigger the early warning mechanism. Multi-level intelligent early warning module: Based on the trend prediction data provided by the deep learning prediction module, a multi-level early warning mechanism is established. The corresponding warning thresholds are set according to different risk levels, and the warning signal is triggered when the prediction results reach the corresponding thresholds. Intelligent response decision module: Combining the warning signals transmitted by the multi-level intelligent early warning module with the three-dimensional dynamic model data generated by the dynamic geological modeling module, it formulates corresponding groundwater management and emergency response strategies, and realizes automated implementation through integration with the regional water resources management system.

2. A multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 1, characterized in that: The data fusion acquisition module includes a sensing unit, a data processing unit and a data transmission unit; wherein: Sensing unit: includes geological radar, multi-band remote sensing equipment and hydrological sensors. The geological radar is used to detect the depth and structural characteristics of underground aquifers, the multi-band remote sensing equipment is used to monitor changes in surface vegetation cover, and the hydrological sensor is used to measure groundwater levels and soil conductivity. The sensing unit also collects groundwater and environmental information within the same time and space range, realizing the synchronous collection of multi-dimensional data. Data processing unit: used to receive multidimensional data from the sensor units and process the multidimensional information from different sensors using a preset data fusion algorithm. The data fusion algorithm includes a weighted average method and a Kalman filter algorithm. The weighted average method first assigns weights to the sensor data from different sources; then the Kalman filter algorithm is used to eliminate noise and redundant information, and finally the processed data is integrated into unified groundwater status data; Data transmission unit: used to transmit the processed unified groundwater status data to the dynamic geological modeling module. The data transmission unit includes a wireless transmission device and a data encryption submodule. The wireless transmission device transmits data in real time through a wireless network. The data encryption submodule is used to encrypt the transmitted data.

3. A multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 2, characterized in that: The data processing unit includes: Weight distribution: First, perform preliminary normalization on the sensor data from geological radar, multi-band remote sensing equipment and hydrological sensors to make the data output by each sensor in the same dimension; then use the weighted average method to distribute the weights of the normalized data; Kalman filter: Apply the Kalman filter algorithm to the weighted sensor data to eliminate noise and redundant information. Kalman filter includes prediction step and update step; the prediction step is based on the state estimation of the previous moment. and the state transition matrix , predict the current state ; The update step is to combine sensor observation data and predicted status , by calculating the Kalman gain Update the status; Data integration: Integrate the sensor data after Kalman filter processing to form unified groundwater status data. Let the integrated data be , the calculation formula is: ,in, represents the integrated groundwater status data, For the Moment The data from each sensor is Kalman filtered. is the number of sensors actually configured in the system, is the corresponding weight coefficient.

4. A multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 1, characterized in that: The dynamic geological modeling module includes a data receiving unit, a geological information integration unit, a three-dimensional modeling unit and a dynamic update unit; wherein: Data receiving unit: used to receive the groundwater status data generated by the data fusion acquisition module and format the received data; Geological information integration unit: used to collect regional geological structure information, including stratum distribution, fault location, lithologic characteristics, and geological history data, and match and integrate the regional geological structure information with the received groundwater status data to provide basic data for the 3D modeling unit; 3D modeling unit: connected to the geological information integration unit, using a geological modeling algorithm to construct a 3D dynamic model of the groundwater aquifer. The geological modeling algorithm includes a spatial interpolation algorithm and a finite element method. The spatial interpolation algorithm is used to estimate the spatial distribution of groundwater status data and calculate the groundwater level and aquifer changes in each spatial unit. The finite element method is used to combine spatial units to construct a 3D dynamic model of the groundwater aquifer. The 3D dynamic model is used to reflect the dynamic distribution and flow path of groundwater. Dynamic update unit: used to dynamically adjust and update the three-dimensional dynamic model based on real-time updated groundwater status data and regional geological structure change information. The dynamic update unit continuously optimizes the accuracy of the three-dimensional dynamic model through iterative calculations.

5. A multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 4, characterized in that: The three-dimensional modeling unit includes: Spatial distribution estimation: Use spatial interpolation algorithm to estimate the spatial distribution of groundwater status data. measuring points, each measuring point The groundwater level is Its spatial coordinates are , through the spatial interpolation algorithm, in the target space unit Groundwater level The calculation formula is: ,in, Indicates measuring point Target space unit The distance between The interpolation power is used to estimate the groundwater level of each spatial unit by weight distribution. and aquifer changes; Spatial unit combination: After completing the spatial distribution estimation, the finite element method is used to combine these spatial units to construct a complete three-dimensional dynamic model of the groundwater aquifer. The area is first divided into multiple finite element units, and the volume of each unit is ,in Indicates the Finite element units are used to combine the groundwater level fields of each finite element unit to form a continuous three-dimensional aquifer model. Model construction and integration: After completing the finite element calculation, the results of all finite element units are integrated into a complete three-dimensional dynamic model.

6. A multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 5, characterized in that: The dynamic update unit includes: Data input and initialization: Dynamic update unit receives real-time updated groundwater status data and regional geological structure change information , first of all, the existing three-dimensional dynamic model Initialize and set the initial parameters of the model , including hydraulic conductivity Water storage coefficient ; Model calibration: based on real-time updated groundwater status data and regional geological structure change information , for the initial model parameters Perform corrections to generate new model parameters , the correction formula is: ,in, and is the correction factor, and For old groundwater status data and geological structure information; Iterative calculation: Use the corrected new parameters Recalculate 3D dynamic model , the hydraulic conductivity of each finite element is calculated by the finite element method. and water storage coefficient Perform iterative update, the iterative equation is: ; ,in, and Respectively represent The first iteration The hydraulic conductivity and storage coefficient of each finite element, and For the The calculation results of the iterations are and is the observed value, and is the iteration step length; Model convergence and update: After completing several iterative calculations, check the convergence of the model. When the iterative results meet the convergence conditions, that is, and , then stop the iteration and convert the final iteration result As the updated 3D dynamic model, the final 3D dynamic model Expressed as: ,in, and are the hydraulic conductivity and storage coefficient after convergence, is the total number of finite element elements.

7. The multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 1 is characterized in that: The deep learning prediction module includes a data input unit, a feature extraction unit, a prediction model training unit and a trend prediction unit; wherein: Data input unit: used to receive real-time updated 3D dynamic model data from the dynamic geological modeling module and historical groundwater monitoring data , and preprocess the received data, including data standardization and normalization, to ensure that the input data are analyzed at the same scale; Feature extraction unit: used to extract data from 3D dynamic models and historical monitoring data Extract features from the groundwater level change rate , water quality indicators and its spatial distribution patterns; Prediction model training unit: used to train a deep learning prediction model based on the extracted feature data. The deep learning algorithm is a long short-term memory network. Through time series analysis of historical data, a prediction model for groundwater level and water quality is established. Trend prediction unit: used to predict future groundwater levels and water quality using a trained deep learning model. The trend prediction unit is based on real-time updated three-dimensional dynamic model data. and the latest feature data Make predictions and output the groundwater level within a specified time period in the future and water quality trends .

8. The multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 1 is characterized in that: The multi-level intelligent early warning module includes a data receiving unit, a risk assessment unit, a threshold setting unit and an early warning signal triggering unit; wherein: Data receiving unit: used to receive trend prediction data from the deep learning prediction module, wherein the trend prediction data includes future groundwater level change trend data and water quality change trend data; Risk Assessment Unit: Conducts risk assessment based on received trend forecast data. It assesses the risk level of each area based on the severity of groundwater level and water quality trends, combined with regional geological conditions, historical events, and socioeconomic factors. The risk level is divided into multiple levels, including low, medium, high, and very high, with each level corresponding to a different degree of potential harm. Threshold setting unit: used to set specific warning thresholds for different risk levels based on predetermined safety standards and historical data; Early warning signal trigger unit: used to monitor trend forecast data and immediately trigger the corresponding level of early warning signal when it detects that the forecast result reaches or exceeds the set early warning threshold. According to the different risk levels, early warning signals are issued through various channels including email, SMS, alarm sound, and control center display to ensure that relevant departments or personnel can receive early warning information in time and take corresponding measures.

9. The multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 8 is characterized in that: The risk assessment unit includes: Trend data analysis: Received groundwater level change trend data and water quality trend data Conduct analysis to calculate the magnitude and rate of change for each region over the forecast period; Weighted regional geological conditions: Calculate the geological sensitivity coefficient based on the geological conditions of each region, including stratum type, fault density, and lithologic characteristics , used to reflect the impact of geological conditions on groundwater changes; Historical event impact assessment: Combine historical event data of the corresponding area, including historical floods, pollution incidents or groundwater-related emergencies, to calculate the historical event impact coefficient ; Socioeconomic impact assessment: Calculate the socioeconomic impact coefficient by evaluating socioeconomic factors, including population density, economic activity density, and infrastructure distribution. ; Comprehensive risk score calculation: trend data analysis results and geological sensitivity coefficient , historical event impact coefficient and socioeconomic impact coefficient Combined, calculate the comprehensive risk score for each area , the formula is: ,in, to is the weight coefficient of each factor; is the rate of change of groundwater level; is the variation range of water quality index; Risk level classification: Based on comprehensive risk score , each area is divided into multiple risk levels, including low risk, medium risk, high risk and extremely high risk; among them, low risk is ; Medium risk ; High risk ; Very high risk .

10. The multi-element early warning system for groundwater dynamic monitoring based on data analysis according to claim 1 is characterized in that: The intelligent response decision module includes an early warning signal receiving unit, a strategy generating unit and an execution control unit; wherein: Warning signal receiving unit: used to receive warning signals from the multi-level intelligent warning module, the warning signals including risk level information and corresponding warning levels, and at the same time receive the three-dimensional dynamic model data generated by the dynamic geological modeling module; Strategy Generation Unit: This unit is used to formulate corresponding groundwater management and emergency response strategies based on the received warning signals and 3D dynamic model data. It selects a response strategy template based on the risk level of the warning signal. Furthermore, it combines the groundwater level, aquifer distribution, and water quality reflected in the 3D dynamic model data to generate specific management measures and emergency response plans, including groundwater extraction restrictions, artificial water recharge, pollution source control, and emergency resource scheduling. Execution control unit: used to convert the generated groundwater management and emergency response strategies into specific operation instructions, and send the operation instructions to relevant execution equipment or control systems.

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