Underground water supplement dynamic balance intelligent control device

Abstract

The invention relates to the technical field of groundwater refilling, in particular to a groundwater refilling dynamic balance intelligent control device, which comprises a data acquisition module for collecting feature data of groundwater in a target area through i observation wells in the target area, analyzing the feature data of the groundwater in the target area, and calculating the dynamic balance of the groundwater in the target area; the underground water level of the target area at the future P moment is predicted, the underground water loss rate of the target area at the future P moment is obtained according to the underground water level of the target area at the future P moment, and then the underground water storage capacity coefficient of the target area is calculated by obtaining the thickness, area and porosity of the aquifer of the target area in combination with the permeability coefficient. Inputting the obtained groundwater loss rate, the groundwater storage capacity coefficient and the groundwater level of the target area acquired in real time into the trained machine learning model to predict groundwater supplement amount; the control precision of the groundwater supplement amount is guaranteed, and dynamic balance of groundwater is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of groundwater recharge, and in particular to a groundwater recharge dynamic balance intelligent control device. Background Art

[0002] Groundwater is one of the world's important freshwater resources and is widely used for agricultural irrigation, industrial water and domestic water. However, due to over-exploitation and irrational water use, groundwater resources in many areas are facing the risk of depletion. In order to cope with the problem of declining groundwater levels, groundwater recharge technology has gradually been applied. Its purpose is to artificially replenish water to underground aquifers to restore or maintain the dynamic balance of groundwater.

[0003] The Chinese patent with the authorization announcement number CN110457831B discloses a method for evaluating the suitability of groundwater source recharge, including: evaluating the recharge potential of the target groundwater source to obtain a potential index; evaluating the recharge risk of the target groundwater source to obtain a risk index; evaluating the recharge benefit of the target groundwater source to obtain a benefit index; and weighting the potential index, risk index and benefit index according to the constructed comprehensive index model to generate a recharge suitability evaluation result and obtain a corresponding evaluation grade. The advantage of this invention is that it realizes the quantitative evaluation of the suitability of groundwater source recharge, has the advantages of clear indicators and simple calculation, and can realize the accurate screening of groundwater source target areas suitable for recharge.

[0004] As in the above application, the existing groundwater recharge control device generally analyzes the feasibility of groundwater recharge in some areas from the perspective of whether there is water storage space in the underground aquifer, but the evaluation index is relatively single, lacking the prediction of future groundwater loss and the analysis of groundwater storage capacity. That is, the groundwater recharge amount is determined based on a single groundwater loss, which has the problem of insufficient control accuracy and difficulty in effectively achieving the dynamic balance of groundwater. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a groundwater recharge dynamic balance intelligent control device.

[0006] The present invention adopts the following technical scheme, the groundwater recharge dynamic balance intelligent control device comprises:

[0007] The data acquisition module collects characteristic data of groundwater in the target area through i observation wells in the target area, wherein the characteristic data of groundwater include the thickness Hh of the aquifer, the groundwater level h, and the rainfall S and evaporation Z of the target area at the future time P;

[0008] The loss prediction module analyzes the characteristic data of groundwater in the target area, predicts the groundwater level in the target area at time P in the future, and obtains the groundwater loss rate in the target area at time P in the future according to the groundwater level in the target area at time P in the future;

[0009] The water storage capacity calculation module calculates the groundwater storage capacity coefficient of the target area by obtaining the thickness, area, porosity and permeability coefficient of the aquifer in the target area;

[0010] A data collection module collects historical groundwater recharge training data of the target area. The historical groundwater recharge training data is collected when the groundwater reaches a dynamic balance under recharge. The historical groundwater recharge training data includes the groundwater level, groundwater loss rate, groundwater storage capacity coefficient and groundwater recharge volume of the target area.

[0011] The model training module trains a machine learning model to predict the groundwater recharge amount at time P in the future based on historical groundwater recharge training data. It collects groundwater levels, groundwater loss rates, and groundwater storage capacity coefficients in the target area in real time, and predicts the groundwater recharge amount based on the trained machine learning model.

[0012] As a further description of the above technical solution: the method of analyzing the characteristic data of groundwater in the target area and predicting the groundwater level in the target area at time P in the future includes:

[0013] The historical groundwater characteristic data are converted into a training set using a sliding window method to train a machine learning model for predicting the groundwater level in the target area at time P in the future;

[0014] A training set generated by a sliding window method is used as the input of a machine learning model. The machine learning model takes the predicted value of the groundwater level in the next P hours as output, the real-time groundwater level as the prediction target, and minimizing the sum of the prediction accuracies of all training data as the training target; wherein the calculation formula for prediction accuracy is: zk=(ak-wk)2, wherein k is the number of the training data, zk is the prediction accuracy, ak is the predicted groundwater level corresponding to the kth group of training data, and wk is the actual groundwater level corresponding to the kth group of training data; the machine learning model is trained until the sum of the prediction accuracies converges and the training is stopped; the machine learning model is a recurrent neural network model.

[0015] As a further description of the above technical solution: According to the groundwater level of the target area at time P in the future, the groundwater loss rate of the target area at time P in the future is obtained as follows:

[0016]

[0017] Where Qp is the groundwater loss rate, h is the groundwater level, hp is the predicted groundwater level at future time P, and p is the time interval.

[0018] As a further description of the above technical solution: the expression of the groundwater storage capacity coefficient is:

[0019]

[0020] Where DXScs is the groundwater storage capacity coefficient, φ is the porosity, Hh is the aquifer thickness, A is the aquifer area; STxs is the permeability coefficient, and is the weight coefficient, and Greater than zero.

[0021] As a further description of the above technical solution: the training method of the machine learning model for predicting the groundwater recharge amount at time P in the future includes:

[0022] Convert the collected historical groundwater recharge training data into a corresponding set of feature vectors;

[0023] Each group of feature vectors is used as the input of the machine learning model, and the machine learning model takes the groundwater recharge amount corresponding to the groundwater level, groundwater loss rate and groundwater storage capacity coefficient of each group of target areas as output, and takes the groundwater recharge amount actually corresponding to the groundwater level, groundwater loss rate and groundwater storage capacity coefficient of each group of target areas as the prediction target, and takes minimizing the loss function value of the machine learning model as the training target; training is stopped when the loss function value of the machine learning model is less than or equal to the preset target loss value.

[0024] As a further description of the above technical solution: it also includes:

[0025] Recharge Optimization Module, which uses a groundwater flow numerical model to simulate groundwater level changes under different recharge rates and optimize the recharge amount and rate;

[0026] The groundwater flow numerical model is a MODFLOW model. MODFLOW simulates the Darcy flow law of groundwater, that is, the water flow rate is proportional to the hydraulic gradient and the permeability of the medium. The MODFLOW model predicts the groundwater level and flow direction by solving the groundwater flow equation.

[0027] As a further description of the above technical solution: the method of using the groundwater flow numerical model to simulate the groundwater level changes under different recharge rates and optimize the recharge amount and rate includes:

[0028] Define the model domain and grid division, select and define the study area, including the groundwater recharge area and its surrounding influence area, and divide the model domain into regular grid cells;

[0029] Setting initial conditions, inputting comprehensive parameters of the groundwater level aquifer in the target area, wherein the comprehensive parameters include thickness, area, porosity, and permeability coefficient of the aquifer;

[0030] Set the compensation conditions and input the real-time rainfall S and real-time evaporation Z of the target area;

[0031] Run a baseline simulation to observe the natural changes in groundwater levels over time without groundwater recharge;

[0032] Set up a plan to run the simulation, that is, for different recharge rates, increase the recharge rate in sequence, run the simulation separately, and record the groundwater level changes in each case;

[0033] Evaluate the change in water level after replenishment to see whether the expected water level recovery has been achieved. Set labels for replenishment plans that have achieved the expected water level recovery and summarize them to establish a replenishment plan set.

[0034] Based on the predicted recharge volume and the preset groundwater recharge rate threshold, the recharge schemes in the recharge scheme set are analyzed and optimized, and based on the recharge schemes in the remaining recharge scheme set and according to the environmental conditions in the target area, a suitable recharge scheme is selected.

[0035] As a further description of the above technical solution: the set of backfill solutions is denoted as YH:

[0036] YH∈{(a1,b1,c1),(a2,b2,c2),...,(an,bn,cn)}, an is the groundwater recharge amount corresponding to the set label n, bn is the groundwater recharge time corresponding to the set label n, and cn is the groundwater recharge rate corresponding to the set label n.

[0037] As a further description of the above technical solution: the method for analyzing and optimizing the recharge schemes in the recharge scheme set based on the predicted recharge amount and the preset groundwater recharge rate threshold includes:

[0038] When an in the replenishment plan is greater than the predicted replenishment amount, the replenishment plan is removed from the replenishment plan set;

[0039] When bn in the backfill scheme is greater than P, the backfill scheme is removed from the backfill scheme set;

[0040] When cn in a recharge scheme is greater than the preset groundwater recharge rate threshold, the recharge scheme is removed from the recharge scheme set.

[0041] As a further description of the above technical solution: the rainfall S in the target area at the future time P and the evaporation Z in the target area are obtained through weather forecast.

[0042] Beneficial effects:

[0043] The groundwater recharge dynamic balance intelligent control device provided by the present invention obtains characteristic data of groundwater in a target area, and converts the characteristic data of historical groundwater into a training set by a sliding window method, so as to train a machine learning model for predicting the groundwater level in the target area at a future time P, predict the groundwater level in the target area at a future time P, obtain the groundwater loss rate in the target area at a future time P according to the groundwater level in the target area at a future time P, and then calculate the groundwater storage capacity coefficient of the target area by obtaining the thickness, area, porosity and permeability of the aquifer in the target area, and then input the obtained groundwater loss rate, groundwater storage capacity coefficient and the real-time collected groundwater level in the target area into the trained machine learning model to predict the groundwater recharge amount, thereby accurately predicting the groundwater recharge amount, ensuring the control accuracy of the groundwater recharge amount, and realizing the dynamic balance of groundwater;

[0044] Furthermore, the present invention also adds a replenishment optimization module, using a groundwater flow numerical model (MODFLOW model) to simulate groundwater level changes under different replenishment rates. Through model simulation, different replenishment strategies can be tested in a virtual environment, avoiding expensive and time-consuming experiments in reality. By simulating different replenishment schemes, the optimal groundwater replenishment amount and rate can be determined to ensure that while meeting demand, over-replenishment or under-replenishment is avoided. The optimized replenishment strategy helps to achieve sustainable management of water resources and ensure the long-term availability of groundwater resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further explained below in conjunction with the accompanying drawings and embodiments:

[0046] Figure 1 This is a module connection diagram of the groundwater recharge dynamic balance intelligent control device according to Example 1 of the present invention;

[0047] Figure 2 This is a module connection diagram of the groundwater replenishment dynamic balance intelligent control device provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0048] In order to make the technical means, creative features, objectives and effects of the present invention easy to understand, the present invention is further described below with reference to specific diagrams. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0049] Example 1

[0050] See also Figure 1 The embodiment of the present invention provides a technical solution: a groundwater recharge dynamic balance intelligent control device, comprising:

[0051] The data acquisition module collects characteristic data of groundwater in the target area through i observation wells in the target area, wherein the characteristic data of groundwater include the thickness Hh of the aquifer, the groundwater level h, and the rainfall S and evaporation Z of the target area at the future time P;

[0052] The rainfall S in the target area and the evaporation Z in the target area at the future time P are obtained through weather forecast.

[0053] It should be noted that the observation well is an observation well drilled in the target area. The depth of the well should reach the groundwater layer and the well pipe should be installed to ensure the representativeness of the water level data.

[0054] The loss prediction module analyzes the characteristic data of groundwater in the target area, predicts the groundwater level in the target area at time P in the future, and obtains the groundwater loss rate in the target area at time P in the future according to the groundwater level in the target area at time P in the future;

[0055] The method of analyzing the characteristic data of groundwater in the target area and predicting the groundwater level in the target area at time P in the future includes:

[0056] The historical groundwater characteristic data are converted into a training set using a sliding window method to train a machine learning model for predicting the groundwater level in the target area at time P in the future;

[0057] A training set generated by a sliding window method is used as the input of a machine learning model. The machine learning model takes the predicted value of the groundwater level in the next P hours as output, the real-time groundwater level as the prediction target, and minimizing the sum of the prediction accuracies of all training data as the training target; wherein the calculation formula for prediction accuracy is: zk=(ak-wk)2, wherein k is the number of all training data, zk is the prediction accuracy, ak is the predicted groundwater level corresponding to the kth group of training data, and wk is the actual groundwater level corresponding to the kth group of training data; the machine learning model is trained until the sum of the prediction accuracies converges and the training is stopped; the machine learning model is a recurrent neural network model.

[0058] According to the groundwater level in the target area at time P in the future, the groundwater loss rate in the target area at time P in the future is:

[0059]

[0060] Where Qp is the groundwater loss rate, h is the groundwater level, hp is the predicted groundwater level at future time P, and p is the time interval.

[0061] The water storage capacity calculation module calculates the groundwater storage capacity coefficient of the target area by obtaining the thickness, area, porosity and permeability coefficient of the aquifer in the target area;

[0062] The expression of groundwater storage capacity coefficient is:

[0063]

[0064] Where DXScs is the groundwater storage capacity coefficient, φ is the porosity, Hh is the aquifer thickness, A is the aquifer area; STxs is the permeability coefficient, and is the weight coefficient, and Greater than zero.

[0065] It should be noted that the size of the weight coefficient is a specific value obtained by quantifying each data to facilitate subsequent comparison. The size of the weight coefficient depends on the number of comprehensive parameters and the preliminary setting of the corresponding weight coefficient for each set of comprehensive parameters by technical personnel in this field.

[0066] It should be noted that porosity refers to the ratio of pore volume in an aquifer to the total rock volume. Porosity is an important indicator of the ability of rocks or soil to store water. The higher the porosity, the stronger the water storage capacity of the aquifer, because high porosity means that there is more space in the aquifer that can be filled with water, and vice versa. Secondly, porosity can be determined by laboratory tests (such as core analysis), logging technology (such as NMR logging) and geological simulation;

[0067] The area and thickness of the aquifer determine the volume space of the aquifer. The larger the thickness and area, the stronger the water storage capacity, and vice versa. The area and thickness of the aquifer can be determined through electromagnetic detection technology, that is, using electromagnetic wave detection technology to identify the differences in electromagnetic properties between the aquifer and other strata, thereby inferring the horizontal distribution area and thickness of the aquifer.

[0068] The permeability coefficient indicates the resistance of a formation or rock to water flow, and is usually used to describe the flow capacity of water in a formation. In formations with high permeability coefficients, water flows more freely. Although this does not directly increase the water storage capacity, it affects the water supply and discharge rate, and thus affects the dynamic water storage capacity of the aquifer. The permeability coefficient is mainly obtained through field experiments, that is, through pumping experiments.

[0069] The specific method of the pumping experiment is:

[0070] Stabilize the water level in the observation well to a certain initial level;

[0071] Start pumping water at a constant rate and record the changes in water levels over time in the pumping well and adjacent observation wells;

[0072] Continue pumping until the water level reaches a relatively stable state;

[0073] The relationship between water level drawdown and time or distance is analyzed using Theis formula, Cooper-Jacob simplified method or Thiem formula to calculate the permeability coefficient;

[0074] The greater the water storage capacity of the aquifer, the higher the allowable recharge volume and rate can be, but at the same time, care must be taken to avoid exceeding the saturation point of the aquifer to prevent excessive groundwater seepage or land subsidence.

[0075] A data collection module collects historical groundwater recharge training data of the target area. The historical groundwater recharge training data is collected when the groundwater reaches a dynamic balance under recharge. The historical groundwater recharge training data includes the groundwater level, groundwater loss rate, groundwater storage capacity coefficient and groundwater recharge volume of the target area.

[0076] The model training module trains a machine learning model to predict the groundwater recharge amount at time P in the future based on historical groundwater recharge training data. It collects groundwater levels, groundwater loss rates, and groundwater storage capacity coefficients in the target area in real time, and predicts the groundwater recharge amount based on the trained machine learning model.

[0077] The training method for the machine learning model to predict groundwater recharge includes:

[0078] Convert the collected historical groundwater recharge training data into a corresponding set of feature vectors;

[0079] Each group of feature vectors is used as the input of the machine learning model, and the machine learning model takes the groundwater recharge amount corresponding to the groundwater level, groundwater loss rate and groundwater storage capacity coefficient of each group of target areas as output, and takes the groundwater recharge amount actually corresponding to the groundwater level, groundwater loss rate and groundwater storage capacity coefficient of each group of target areas as the prediction target, and takes minimizing the loss function value of the machine learning model as the training target; training is stopped when the loss function value of the machine learning model is less than or equal to the preset target loss value.

[0080] The machine learning model may be one of support vector machine regression, random forest regression or neural network regression models.

[0081] The loss function value of the machine learning model is the mean square error.

[0082] By transforming the loss function The model is trained with minimization as the goal, so that the machine learning model can better fit the data, thereby improving the performance and accuracy of the model.

[0083] In the loss function, MSE is the loss function value of the machine learning model, x is the feature vector group number; m is the number of feature vector groups; yx is the groundwater recharge amount corresponding to the xth feature vector group, is the groundwater recharge amount corresponding to the xth group of eigenvectors in real time.

[0084] Other model parameters of the machine learning model, target loss value, optimization algorithm, training set test set validation set ratio, and loss function optimization are all achieved through actual engineering implementation and continuous experimental tuning.

[0085] In this implementation, by acquiring characteristic data of groundwater in the target area, the sliding window method is used to convert the characteristic data of historical groundwater into a training set, so as to train a machine learning model for predicting the groundwater level in the target area at time P in the future, and predict the groundwater level in the target area at time P in the future. The groundwater loss rate of the target area at time P in the future is obtained according to the groundwater level in the target area at time P in the future, and then the groundwater storage capacity coefficient of the target area is calculated by acquiring the thickness, area, porosity and permeability of the aquifer in the target area. Then, the obtained groundwater loss rate, groundwater storage capacity coefficient and real-time groundwater level in the target area are input into the trained machine learning model to predict the groundwater replenishment amount, thereby accurately predicting the groundwater replenishment amount, ensuring the control accuracy of the groundwater replenishment amount and achieving the dynamic balance of groundwater.

[0086] Example 2

[0087] See also Figure 2 This implementation adds a backfill optimization module based on the above implementation:

[0088] The recharge optimization module uses a groundwater flow numerical model to simulate groundwater level changes under different recharge rates and optimize the recharge amount and rate.

[0089] The groundwater flow numerical model is a MODFLOW model. MODFLOW simulates the Darcy flow law of groundwater, that is, the water flow rate is proportional to the hydraulic gradient and the permeability of the medium. The MODFLOW model predicts the groundwater level and flow direction by solving the groundwater flow equation.

[0090] Methods for using groundwater flow numerical models to simulate groundwater level changes under different recharge rates and optimize recharge amount and rate include:

[0091] Define the model domain and grid division, select and define the study area, including the groundwater recharge area and its surrounding influence area, and divide the model domain into regular grid cells;

[0092] Setting initial conditions, inputting comprehensive parameters of the groundwater level aquifer in the target area, wherein the comprehensive parameters include thickness, area, porosity, and permeability coefficient of the aquifer;

[0093] Set the compensation conditions and input the real-time rainfall S and real-time evaporation Z of the target area;

[0094] Run a baseline simulation to observe the natural changes in groundwater levels over time without groundwater recharge;

[0095] Set up multiple scenarios for running simulations, that is, for different recharge rates, increase the recharge rate in sequence, run simulations separately, and record the groundwater level changes in each case;

[0096] Evaluate the change in water level after replenishment to see whether the expected water level recovery has been achieved. Set labels for replenishment plans that have achieved the expected water level recovery and summarize them to establish a replenishment plan set.

[0097] Based on the predicted recharge volume and the preset groundwater recharge rate threshold, the recharge schemes in the recharge scheme set are analyzed and optimized, and based on the recharge schemes in the remaining recharge scheme set and according to the environmental conditions in the target area, a suitable recharge scheme is selected.

[0098] The set of backfill plans is denoted as YH:

[0099] YH∈{(a1,b1,c1),(a2,b2,c2),...,(an,bn,cn)}, an is the groundwater recharge amount corresponding to the set label n, bn is the groundwater recharge time corresponding to the set label n, and cn is the groundwater recharge rate corresponding to the set label n.

[0100] Based on the predicted recharge volume and the preset groundwater recharge rate threshold, the method for analyzing and optimizing the recharge schemes in the recharge scheme set includes:

[0101] When an in the replenishment plan is greater than the predicted replenishment amount, the replenishment plan is removed from the replenishment plan set;

[0102] When bn in the backfill scheme is greater than P, the backfill scheme is removed from the backfill scheme set;

[0103] When cn in a recharge scheme is greater than the preset groundwater recharge rate threshold, the recharge scheme is removed from the recharge scheme set.

[0104] In this embodiment, a groundwater flow numerical model (MODFLOW model) is used to simulate the groundwater level changes under different recharge rates. Through model simulation, different recharge strategies can be tested in a virtual environment, avoiding expensive and time-consuming experiments in reality. By simulating different recharge schemes, the optimal groundwater recharge amount and rate can be determined to ensure that while meeting the demand, over-recharge or under-recharge is avoided. The optimized recharge strategy helps to achieve sustainable management of water resources and ensure the long-term availability of groundwater resources.

[0105] The basic principles, main features and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and the description in the specification are only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The groundwater recharge dynamic balance intelligent control device is characterized by: include: The data acquisition module collects characteristic data of groundwater in the target area through i observation wells in the target area, wherein the characteristic data of groundwater include the thickness Hh of the aquifer, the groundwater level h, and the rainfall S and evaporation Z of the target area at the future time P; The loss prediction module analyzes the characteristic data of groundwater in the target area, predicts the groundwater level in the target area at time P in the future, and obtains the groundwater loss rate in the target area at time P in the future according to the groundwater level in the target area at time P in the future; The water storage capacity calculation module calculates the groundwater storage capacity coefficient of the target area by obtaining the thickness, area, porosity and permeability coefficient of the aquifer in the target area; A data collection module collects historical groundwater recharge training data of the target area. The historical groundwater recharge training data is collected when the groundwater reaches a dynamic balance under recharge. The historical groundwater recharge training data includes the groundwater level, groundwater loss rate, groundwater storage capacity coefficient and groundwater recharge volume of the target area. The model training module trains a machine learning model to predict the groundwater recharge amount at time P in the future based on historical groundwater recharge training data. It collects groundwater levels, groundwater loss rates, and groundwater storage capacity coefficients in the target area in real time, and predicts the groundwater recharge amount based on the trained machine learning model.

2. The groundwater recharge dynamic balance intelligent control device according to claim 1 is characterized in that: The method of analyzing the characteristic data of groundwater in the target area and predicting the groundwater level in the target area at time P in the future includes: The historical groundwater characteristic data are converted into a training set using a sliding window method to train a machine learning model for predicting the groundwater level in the target area at time P in the future; A training set generated by a sliding window method is used as the input of a machine learning model. The machine learning model takes the predicted value of the groundwater level in the next P hours as output, the real-time groundwater level as the prediction target, and minimizing the sum of the prediction accuracies of all training data as the training target; wherein the calculation formula for prediction accuracy is: zk=(ak-wk)2, wherein k is the number of the training data, zk is the prediction accuracy, ak is the predicted groundwater level corresponding to the kth group of training data, and wk is the actual groundwater level corresponding to the kth group of training data; the machine learning model is trained until the sum of the prediction accuracies converges and the training is stopped; the machine learning model is a recurrent neural network model.

3. The groundwater recharge dynamic balance intelligent control device according to claim 1 is characterized in that: According to the groundwater level in the target area at time P in the future, the groundwater loss rate in the target area at time P in the future is: Where Qp is the groundwater loss rate, h is the groundwater level, hp is the predicted groundwater level at future time P, and p is the time interval.

4. The groundwater recharge dynamic balance intelligent control device according to claim 1 is characterized in that: The expression of the groundwater storage capacity coefficient is: Where, DXScs is the groundwater storage capacity coefficient, φ is the porosity, Hh is the aquifer thickness, A is the aquifer area; STxs is the permeability coefficient, and is the weight coefficient, and Greater than zero.

5. The groundwater recharge dynamic balance intelligent control device according to claim 1 is characterized in that: The training method of the machine learning model for predicting the future groundwater recharge volume at time P includes: Convert the collected historical groundwater recharge training data into a corresponding set of feature vectors; Each group of feature vectors is used as the input of the machine learning model, and the machine learning model takes the groundwater recharge amount corresponding to the groundwater level, groundwater loss rate and groundwater storage capacity coefficient of each group of target areas as output, and takes the groundwater recharge amount actually corresponding to the groundwater level, groundwater loss rate and groundwater storage capacity coefficient of each group of target areas as the prediction target, and takes minimizing the loss function value of the machine learning model as the training target; training is stopped when the loss function value of the machine learning model is less than or equal to the preset target loss value.

6. The groundwater recharge dynamic balance intelligent control device according to claim 1 is characterized in that: Also includes: Recharge Optimization Module, which uses a groundwater flow numerical model to simulate groundwater level changes under different recharge rates and optimize the recharge amount and rate; The groundwater flow numerical model is a MODFLOW model. MODFLOW simulates the Darcy flow law of groundwater, that is, the water flow rate is proportional to the hydraulic gradient and the permeability of the medium. The MODFLOW model predicts the groundwater level and flow direction by solving the groundwater flow equation.

7. The groundwater recharge dynamic balance intelligent control device according to claim 6 is characterized in that: The method of using the groundwater flow numerical model to simulate groundwater level changes under different recharge rates and optimize the recharge amount and rate includes: Define the model domain and grid division, select and define the study area, including the groundwater recharge area and its surrounding influence area, and divide the model domain into regular grid cells; Setting initial conditions, inputting comprehensive parameters of the groundwater level aquifer in the target area, wherein the comprehensive parameters include thickness, area, porosity, and permeability coefficient of the aquifer; Set the compensation conditions and input the real-time rainfall S and real-time evaporation Z of the target area; Run a baseline simulation to observe the natural changes in groundwater levels over time without groundwater recharge; Set up a plan to run the simulation, that is, for different recharge rates, increase the recharge rate in sequence, run the simulation separately, and record the groundwater level changes in each case; Evaluate the change in water level after replenishment to see whether the expected water level recovery has been achieved. Set labels for replenishment plans that have achieved the expected water level recovery and summarize them to establish a replenishment plan set. Based on the predicted recharge volume and the preset groundwater recharge rate threshold, the recharge schemes in the recharge scheme set are analyzed and optimized, and based on the recharge schemes in the remaining recharge scheme set and according to the environmental conditions in the target area, a suitable recharge scheme is selected.

8. The groundwater recharge dynamic balance intelligent control device according to claim 7 is characterized in that: The set of backfill plans is denoted as YH: YH∈{(a1,b1,c1),(a2,b2,c2),...,(an,bn,cn)}, an is the groundwater recharge amount corresponding to the set label n, bn is the groundwater recharge time corresponding to the set label n, and cn is the groundwater recharge rate corresponding to the set label n.

9. The groundwater recharge dynamic balance intelligent control device according to claim 8, characterized in that: The method for analyzing and optimizing the recharge schemes in the recharge scheme set based on the predicted recharge amount and the preset groundwater recharge rate threshold includes: When an in the replenishment plan is greater than the predicted replenishment amount, the replenishment plan is removed from the replenishment plan set; When bn in the backfill scheme is greater than P, the backfill scheme is removed from the backfill scheme set; When cn in a recharge scheme is greater than the preset groundwater recharge rate threshold, the recharge scheme is removed from the recharge scheme set.

10. The groundwater recharge dynamic balance intelligent control device according to claim 1, characterized in that: The rainfall S in the target area and the evaporation Z in the target area at the future time P are obtained through weather forecast.

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