Underground water resource comprehensive evaluation method and system based on big data, electronic equipment and storage medium

Abstract

The invention belongs to the field of environmental protection, and discloses an underground water resource comprehensive evaluation method and system based on big data, electronic equipment and a storage medium. The method comprises the following steps: collecting and processing underground water resource data of a to-be-evaluated region to obtain processed data; based on the processed data, extracting long-term trend information related to the groundwater resources; constructing an underground water system model based on the long-term trend information; and completing groundwater resource assessment based on the groundwater system model. According to the method, the multi-source data is collected and processed, the long-term trend information is extracted, the groundwater system model is constructed, and denoising processing is performed, so that high-precision evaluation of groundwater resources is realized. According to the method, the problems of insufficient data coverage, low time resolution and low model precision in a traditional evaluation method can be effectively solved, and the reliability and the accuracy of an evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental protection, and particularly relates to a comprehensive evaluation method, system, electronic device and storage medium for groundwater resources based on big data. Background Art

[0002] The evaluation of groundwater resources is of great significance for water resources management, environmental protection and sustainable development. Traditional methods for evaluating groundwater resources mainly rely on ground monitoring data, such as the observation data of hydrological stations and meteorological stations. However, the coverage of these data in space and time is limited, and it is difficult to comprehensively reflect the dynamic changes of groundwater resources. In addition, although remote sensing technology can provide large-scale observation data, its time resolution is low, and it is easily affected by clouds and sensor noise. In the process of constructing existing groundwater system models, the integration and spatial alignment of multi-source data are often ignored, resulting in insufficient accuracy and reliability of the models. Therefore, it is of great practical significance to develop an evaluation method for groundwater resources that can comprehensively consider multi-source data and improve the evaluation accuracy. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a comprehensive evaluation method for groundwater resources based on big data, and the method includes:

[0004] Collect the groundwater resource data of the area to be evaluated and process it to obtain the processed data;

[0005] Extract the long-term trend information related to groundwater resources based on the processed data;

[0006] Construct a groundwater system model based on the long-term trend information;

[0007] Complete the evaluation of groundwater resources based on the groundwater system model.

[0008] Preferably, obtain the groundwater resource data from meteorological stations and hydrological stations, and perform data cleaning on it; then, in view of the time scale difference between ground monitoring data and remote sensing data, use the linear interpolation algorithm to align the water level data of hydrological stations and precipitation data of meteorological stations in time to generate a unified time series. The linear interpolation formula is as follows:

[0009]

[0010] where t represents the target time point; t prev represents the precursor time point; t next represents the successor time point; y prev and y next respectively represent the data values corresponding to t prev , t next .

[0011] Preferably, the spatial coordinate information of the monitoring points is extracted based on the water level values and precipitation values in the unified time series; a grid structure of the spatial domain is constructed based on the spatial coordinate information of the monitoring points; and the interpolation results on the grid points are calculated for the water level values and precipitation values respectively using the Kriging interpolation algorithm. The Kriging interpolation steps include:

[0012] Let Z(x i ) is a known point x i The observed value at is the estimated value at the unknown point x0, then the estimated value of the Kriging interpolation is given by the following formula:

[0013]

[0014] Among them, λ i represents the Kriging weights, satisfying the following conditions:

[0015]

[0016] Kriging weight λ i By solving the following system of equations:

[0017]

[0018] Among them, γ(x i , x j ) represents the known point x i and x j The semivariogram value between γ(x i , x0) represents the known point x i and the unknown point x0; μ represents the Lagrange multiplier.

[0019] Preferably, the STL time series decomposition algorithm is used to decompose the time series into a trend term and a residual term, and the trend term is extracted from the decomposition result; for the trend term related to the groundwater level change, a grid structure of the spatial domain is constructed.

[0020] Preferably, the STL step includes:

[0021] Perform LOESS smoothing on the original time series Y(t) and preliminarily separate the trend term T(t). The formula is as follows:

[0022] T(t)=LOESS(Y(t), span=k)

[0023] Where k represents the size of the smoothing window;

[0024] Deduct the trend term from the original data to obtain the detrended sequence Y′(t)=Y(t)-T(t);

[0025] Extract the long-term trend directly based on the detrended sequence;

[0026] The final residual term R(t) is the difference between the original data and the trend term, i.e., R(t) = Y(t) - T(t);

[0027] Improve the decomposition stability through multiple inner loops and outer loops.

[0028] Preferably, based on the long-term trend information of the ground monitoring dataset and remote sensing data, use the multi-resolution analysis algorithm to spatially align the DEM elevation data and the geological structure map, and construct the initial framework of the three-dimensional hydrogeological model; use the finite element analysis method to numerically simulate the model to obtain the distribution of the hydrogeological characteristics of the model; based on the numerical simulation results, generate a unified three-dimensional hydrogeological model.

[0029] The present invention also provides a comprehensive evaluation system for groundwater resources based on big data, and the system is used to implement the above method, including a collection module, an extraction module, a construction module, and an evaluation module;

[0030] The collection module is used to collect the groundwater resource data of the area to be evaluated and process it to obtain the processed data;

[0031] The extraction module is used to extract the long-term trend information related to groundwater resources based on the processed data;

[0032] The construction module is used to construct a groundwater system model based on the long-term trend information;

[0033] The evaluation module is used to complete the evaluation of groundwater resources based on the groundwater system model.

[0034] Preferably, the working process of the collection module includes: obtaining the groundwater resource data from the meteorological station and the hydrological station, and cleaning the data; then, aiming at the time scale difference between the ground monitoring data and the remote sensing data, using the linear interpolation algorithm to align the time of the water level data of the hydrological station and the precipitation data of the meteorological station to generate a unified time series, and the linear interpolation formula is as follows:

[0035]

[0036] where t represents the target time point; t prev represents the precursor time point; t next represents the successor time point; y prev and y next respectively represent the data values corresponding to t prev and t next

[0037] ​The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned method is implemented.

[0038] The present invention also provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method when the computer program is executed.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] By collecting and processing multi-source data, extracting long-term trend information, constructing a groundwater system model, and performing denoising processing, the present invention realizes high-precision evaluation of groundwater resources. The present invention can effectively solve the problems of insufficient data coverage, low time resolution, and low model accuracy in traditional evaluation methods, and improve the reliability and accuracy of evaluation results. At the same time, the method of the present invention has strong adaptability and scalability, and can be applied to the evaluation of groundwater resources in different regions and at different scales, providing strong technical support for water resource management, environmental protection, and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention;

[0043] Figure 2 It is a schematic structural diagram of the electronic device according to an embodiment of the present invention.

[0044] Description of the reference numerals:

[0045] 1010, processor; 1020, memory; 1030, input / output interface; 1040, communication interface; 1050, bus. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the ordinary meanings understood by those of ordinary skill in the field to which the present disclosure belongs. The "first", "second" and similar terms used in the embodiments of the present disclosure do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0048] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Embodiment 1

[0050] As can be seen from the background art, in the process of constructing the existing groundwater system model, the integration and spatial alignment of multi-source data are often ignored, resulting in insufficient accuracy and reliability of the model.

[0051] As Figure 1 shown, an embodiment of the present invention provides a comprehensive evaluation method for groundwater resources based on big data. The steps include:

[0052] S1. Collect the groundwater resource data of the area to be evaluated and process it to obtain the processed data.

[0053] Obtain the groundwater resource data from meteorological stations, hydrological stations, etc., and perform data cleaning on it, including removing outliers, filling in missing values, etc., to ensure the integrity and accuracy of the data. Then, in view of the time scale difference between the ground monitoring data and the remote sensing data, a linear interpolation algorithm is used to align the water level data of the hydrological station and the precipitation data of the meteorological station in time to generate a unified time series.

[0054] Specifically, obtain the water level data of the hydrological station and the precipitation data of the meteorological station, and extract their respective original time points (t1, t2,..., t n ) and corresponding data values (y1, y2,..., y n)。Determine the start time point and end time point of the two types of data, and establish a unified time axis. For each time step on the unified time axis, find the two nearest time points before and after this time step for the hydrological data and meteorological data. Calculate the interpolated data values of the hydrological data and meteorological data at the unified time step according to the linear interpolation formula. Arrange the interpolated hydrological data and meteorological data according to the unified time axis. Use the least squares method to fit the correlation between the interpolated hydrological data and meteorological data. Generate a time series dataset (i.e., the processed data) containing the unified time axis, hydrological data, and meteorological data. The linear interpolation formula is as follows:

[0055]

[0056] Among them, t represents the target time point; t prev represents the predecessor time point; t next represents the successor time point; y prev and y next respectively represent the data values corresponding to t prev and t next .

[0057] After that, based on the unified time series, use the Kriging interpolation algorithm to expand the water level data of the hydrological station and the precipitation data of the meteorological station from point data to surface data, and generate a ground monitoring dataset with consistent spatial resolution.

[0058] Specifically, based on the water level values and precipitation values in the above unified time series, extract the spatial coordinate information of the monitoring points. According to the spatial coordinate information of the monitoring points, construct a grid structure in the spatial domain. Use the Kriging interpolation algorithm to calculate the interpolation results at the grid points for the water level values and precipitation values respectively. If there is no monitoring point data around a certain grid point, expand the adjacent area range according to the preset search radius for interpolation calculation. Map the interpolated water level values and precipitation values into the grid structure respectively to generate a spatially continuous distribution map. According to the requirements of the spatial resolution, adjust the density of the grid structure to ensure the consistency of the distribution maps of the water level values and precipitation values. Combine the spatial distribution maps of the water level values and precipitation values to generate a ground monitoring dataset with consistent spatial resolution.

[0059] The formula for Kriging interpolation is as follows:

[0060] Let Z(x i ) be the observed value at the known point x i , and

[0061]

[0062] be the estimated value at the unknown point x0. Then the estimated value of Kriging interpolation is given by the following formula: iDenote the Kriging weights, which satisfy the following conditions:

[0063]

[0064] The Kriging weight λ i is obtained by solving the following system of equations:

[0065]

[0066] where γ(x i , x j ) represents the semivariogram value between the known points x i and x j , and γ(x i , x0) represents the semivariogram value between the known point x i and the unknown point x0; μ represents the Lagrange multiplier.

[0067] S2. Based on the processed data, extract the long-term trend information related to the groundwater resources.

[0068] Aiming at the problem of low temporal resolution of remote sensing data, the STL time series decomposition algorithm is used to decompose the remote sensing data into a trend term and a residual term, and extract the long-term trend information related to the groundwater resources.

[0069] Specifically, the STL time series decomposition algorithm is used to decompose the time series into a trend term and a residual term. Extract the trend term from the decomposition result and determine whether the trend term is related to the groundwater level change. For the trend term related to the groundwater level change, construct a grid structure in the spatial domain. Map the trend term data into the grid structure to generate a spatially continuous distribution map. According to the requirements of the spatial resolution, adjust the density of the grid structure to ensure the consistency of the distribution map of the trend term data.

[0070] The STL (Seasonal-Trend decomposition using LOESS) algorithm decomposes the time series into a trend term (Trend), a seasonal term (Seasonal), and a residual term (Residual) through locally weighted regression (LOESS). In this embodiment, aiming at the problem of low temporal resolution of remote sensing data, STL is simplified to extract the long-term trend term and the residual term. The steps include:

[0071] (1) Perform LOESS smoothing on the original time series Y(t) to initially separate the trend term T(t), and the formula is as follows:

[0072] T(t) = LOESS(Y(t), span = k)

[0073] where k represents the size of the smoothing window.

[0074] (2) Deduct the trend term from the original data to obtain the detrended sequence Y′(t) = Y(t) - T(t).

[0075] (3) Since the seasonal term is weakened in this embodiment, the long-term trend is directly extracted based on the detrended sequence.

[0076] (4) The final residual term R(t) is the difference between the original data and the trend term, i.e., R(t) = Y(t) - T(t).

[0077] (5) Improve the decomposition stability through multiple inner loops (adjusting trends and residuals) and outer loops (updating robust weights).

[0078] S3. Construct a groundwater system model based on the long-term trend information.

[0079] Based on the long-term trend information of the ground monitoring dataset and remote sensing data, use the multi-resolution analysis algorithm to spatially align the DEM elevation data and geological structure map to generate a unified three-dimensional hydrogeological model.

[0080] Extract the information related to topography and geology from the remote sensing data obtained from the meteorological station to obtain the DEM elevation data and geological structure map. Use the multi-resolution analysis algorithm to spatially align the trend term data, DEM elevation data, and geological structure map to generate the spatial alignment result. According to the spatial alignment result, construct the initial framework of the three-dimensional hydrogeological model and determine the boundary conditions and initial parameters of the model. The boundary conditions are determined as follows:

[0081] Based on the topographic data (DEM), geological structure map, and long-term monitoring data of the study area, identify the spatial distribution of hydrogeological units (such as aquitards and aquifer boundaries), and combine the infiltration characteristics of surface water bodies (rivers, lakes) to set the Dirichlet boundary (fixed head) or Neumann boundary (flow constraint) of the model. The initial parameters (such as permeability coefficient and porosity) are obtained through multi-source data fusion, including the historical statistical values of ground monitoring data, core experiment data, and soil moisture information retrieved by remote sensing. Use Kriging interpolation to generate a spatially continuous parameter distribution map, and perform iterative calibration by comparing the trial calculation of the finite element model with the measured data to ensure that the spatial heterogeneity of the parameters is consistent with the actual hydrogeological conditions.

[0082] Use the finite element analysis method to numerically simulate the model to obtain the distribution of the hydrogeological characteristics of the model. Based on the numerical simulation results, generate a unified three-dimensional hydrogeological model. The specific steps are as follows:

[0083] First, discretize the three-dimensional hydrogeological model into a finite number of elements (such as tetrahedrons or hexahedrons), and define the shape function (such as linear shape function N i(x, y, z) approximate head distribution h ≈ ∑N i h i ; Secondly, based on Darcy's law and the principle of mass conservation, establish the control equations:

[0084]

[0085] where K represents the permeability coefficient tensor; S represents the storage coefficient; Q represents the source-sink term, and use the Galerkin method to transform it into an integral form weak equation:

[0086]

[0087] where q n represents the boundary flux; Subsequently, assemble the global stiffness matrix and the load vector, combine the initial head distribution and boundary conditions (such as fixed head or flux constraints), and solve the linear equations Ah = b to obtain the nodal head values; Finally, calculate the velocity field through post-processing:

[0088]

[0089] And iterate to optimize the parameters until the simulation results match the measured data.

[0090] S4. Based on the groundwater system model, complete the assessment of groundwater resources.

[0091] Finally, for the noise interference problem in the three-dimensional hydrogeological model, use the discrete wavelet transform algorithm to denoise the remote sensing image, remove cloud interference and sensor outliers, and generate the denoised remote sensing image. The finally generated denoised remote sensing image can be used to identify key hydrogeological features such as fault strike and lithology distribution, providing a reliable data basis for the construction of the three-dimensional hydrogeological model.

[0092] The technical solution of the present invention, by collecting and processing multi-source data, extracting long-term trend information, constructing a groundwater system model, and performing denoising processing, realizes the high-precision assessment of groundwater resources. This method can effectively solve the problems of insufficient data coverage, low time resolution, and low model accuracy in traditional assessment methods, improving the reliability and accuracy of the assessment results. At the same time, the method of the present invention has strong adaptability and scalability, and can be applied to the assessment of groundwater resources in different regions and at different scales, providing strong technical support for water resource management, environmental protection, and sustainable development.

[0093] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario, and multiple devices cooperate with each other to complete it. In this case of a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0094] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention. The actions or steps recited in the claims can be executed in a different order than in the above embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] Embodiment 2

[0096] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure further provides a comprehensive evaluation system for underground water resources based on big data, including: a collection module, an extraction module, a construction module, and an evaluation module; the collection module is used to collect underground water resources data of the area to be evaluated and process it to obtain processed data; the extraction module is used to extract long-term trend information related to underground water resources based on the processed data; the construction module is used to construct a groundwater system model based on the long-term trend information; the evaluation module is used to complete the evaluation of underground water resources based on the groundwater system model.

[0097] Next, in combination with this embodiment, it will be described in detail how the present invention solves the technical problems in actual work.

[0098] First, use the collection module to collect underground water resources data of the area to be evaluated and process it to obtain processed data.

[0099] Obtain underground water resources data from meteorological stations, hydrological stations, etc., and perform data cleaning on it, including removing outliers, filling in missing values, etc., to ensure data integrity and accuracy. Then, in view of the time scale difference between ground monitoring data and remote sensing data, use a linear interpolation algorithm to align the time of the water level data of the hydrological station and the precipitation data of the meteorological station to generate a unified time series.

[0100] Specifically, obtain the water level data of the hydrological station and the precipitation data of the meteorological station, and extract their respective original time points (t1, t2, ..., t n ) and the corresponding data values (y1, y2, ..., y n ). Determine the start time point and end time point of the two types of data, and establish a unified time axis. For each time step on the unified time axis, find the two nearest time points before and after the hydrological data and meteorological data at this time step. Calculate the interpolated point data values of the hydrological data and meteorological data at the unified time step according to the linear interpolation formula. Arrange the interpolated hydrological data and meteorological data according to the unified time axis. Use the least squares method to fit the correlation between the interpolated hydrological data and meteorological data. Generate a time series data set (i.e., processed data) containing the unified time axis, hydrological data, and meteorological data. The linear interpolation formula is as follows:

[0101]

[0102] where t represents the target time point; t prev represents the precursor time point; t next represents the successor time point; y prev and y next respectively represent the data values corresponding to t prev , t next .

[0103] After that, based on the unified time series, use the Kriging interpolation algorithm to expand the water level data of the hydrological station and the precipitation data of the meteorological station from point data to surface data, and generate a ground monitoring data set with consistent spatial resolution.

[0104] Specifically, based on the water level values and precipitation values in the above unified time series, extract the spatial coordinate information of the monitoring points. According to the spatial coordinate information of the monitoring points, construct a grid structure in the spatial domain. Use the Kriging interpolation algorithm to calculate the interpolation results at the grid points for the water level values and precipitation values respectively. If there is no monitoring point data around a certain grid point, expand the adjacent area range according to the preset search radius for interpolation calculation. Map the interpolated water level values and precipitation values into the grid structure respectively to generate a spatially continuous distribution map. According to the requirements of the spatial resolution, adjust the density of the grid structure to ensure the consistency of the distribution maps of the water level values and precipitation values. Combine the spatial distribution maps of the water level values and precipitation values to generate a ground monitoring data set with consistent spatial resolution.

[0105] The formula for Kriging interpolation is as follows:

[0106] Let Z(x i ) be the observed value at the known point x i , be the estimated value at the unknown point x0, then the estimated value of Kriging interpolation is given by the following formula:

[0107]

[0108] Among them, λ i represents the Kriging weight and satisfies the following conditions:

[0109]

[0110] The Kriging weight λ i is obtained by solving the following system of equations:

[0111]

[0112] Among them, γ(x i , x j ) represents the semivariogram value between the known points x i and x j , and γ(x i , x0) represents the semivariogram value between the known point x i and the unknown point x0; μ represents the Lagrange multiplier.

[0113] After that, the extraction module extracts long-term trend information related to groundwater resources based on the processed data.

[0114] Aiming at the problem of low temporal resolution of remote sensing data, the STL time series decomposition algorithm is used to decompose the remote sensing data into a trend term and a residual term, and long-term trend information related to groundwater resources is extracted.

[0115] Specifically, the STL time series decomposition algorithm is used to decompose the time series into a trend term and a residual term. The trend term is extracted from the decomposition result, and it is judged whether the trend term is related to the groundwater level change. For the trend term related to the groundwater level change, a grid structure in the spatial domain is constructed. The trend term data is mapped into the grid structure to generate a spatially continuous distribution map. According to the requirement of spatial resolution, the density of the grid structure is adjusted to ensure the consistency of the distribution map of the trend term data.

[0116] The STL (Seasonal-Trend decomposition using LOESS) algorithm decomposes the time series into a trend term (Trend), a seasonal term (Seasonal), and a residual term (Residual) through locally weighted regression (LOESS). In this embodiment, aiming at the problem of low temporal resolution of remote sensing data, STL is simplified to extract the long-term trend term and the residual term. The steps include:

[0117] (1) Perform LOESS smoothing on the original time series Y(t) to initially separate the trend term T(t), and the formula is as follows:

[0118] T(t) = LOESS(Y(t), span = k)

[0119] Where k represents the size of the smoothing window.

[0120] (2) Deduct the trend term from the original data to obtain the detrended sequence Y′(t) = Y(t) - T(t).

[0121] (3) Since the seasonal term is weakened in this embodiment, the long-term trend is directly extracted based on the detrended sequence.

[0122] (4) The final residual term R(t) is the difference between the original data and the trend term, i.e., R(t) = Y(t) - T(t).

[0123] (5) Improve the decomposition stability through multiple inner loops (adjusting trends and residuals) and outer loops (updating robust weights).

[0124] The construction module constructs a groundwater system model based on the long-term trend information.

[0125] Based on the long-term trend information of the ground monitoring dataset and remote sensing data, a multi-resolution analysis algorithm is used to spatially align the DEM elevation data and geological structure map to generate a unified three-dimensional hydrogeological model.

[0126] Extract information related to topography and geology from the remote sensing data obtained from the meteorological station to obtain the DEM elevation data and geological structure map. Use a multi-resolution analysis algorithm to spatially align the trend term data, DEM elevation data, and geological structure map to generate a spatial alignment result. According to the spatial alignment result, construct an initial framework of the three-dimensional hydrogeological model and determine the boundary conditions and initial parameters of the model. The boundary conditions are determined as follows:

[0127] Based on the topographic data (DEM), geological structure map, and long-term monitoring data of the study area, identify the spatial distribution of hydrogeological units (such as aquitards and aquifer boundaries), and combine the infiltration characteristics of surface water bodies (rivers, lakes) to set the Dirichlet boundary (fixed head) or Neumann boundary (flow constraint) of the model. The initial parameters (such as hydraulic conductivity and porosity) are obtained through multi-source data fusion, including historical statistical values of ground monitoring data, core experiment data, and soil moisture information retrieved by remote sensing. Use Kriging interpolation to generate a spatially continuous parameter distribution map, and perform iterative calibration by comparing the trial calculation of the finite element model with the measured data to ensure that the spatial heterogeneity of the parameters is consistent with the actual hydrogeological conditions.

[0128] Use the finite element analysis method to numerically simulate the model to obtain the distribution of hydrogeological characteristics of the model. Based on the numerical simulation results, generate a unified three-dimensional hydrogeological model. The specific steps are as follows:

[0129] First, the three-dimensional hydrogeological model is discretized into a finite number of elements (such as tetrahedrons or hexahedrons), and shape functions (such as linear shape function N i (x, y, z) is used to approximate the head distribution h≈∑N i h i ; Second, based on Darcy's law and the principle of mass conservation, a control equation is established:

[0130]

[0131] where K represents the permeability coefficient tensor; S represents the storage coefficient; Q represents the source-sink term, and it is transformed into an integral form weak equation by the Galerkin method:

[0132]

[0133] where q n represents the boundary flux; Subsequently, the global stiffness matrix and the load vector are assembled, combined with the initial head distribution and boundary conditions (such as fixed head or flux constraints), and the linear equation system Ah = b is solved to obtain the nodal head values; Finally, the velocity field is calculated through post-processing:

[0134]

[0135] And the parameters are iteratively optimized until the simulation results match the measured data.

[0136] Finally, the evaluation module completes the assessment of groundwater resources based on the groundwater system model.

[0137] Finally, for the noise interference problem in the three-dimensional hydrogeological model, the discrete wavelet transform algorithm is used to denoise the remote sensing image, remove cloud interference and sensor outliers, and generate a denoised remote sensing image. The finally generated denoised remote sensing image can be used to identify key hydrogeological features such as fault strikes and lithology distributions, providing a reliable data basis for the construction of the three-dimensional hydrogeological model.

[0138] The system of the above embodiments is used to implement the corresponding comprehensive assessment method of groundwater resources based on big data in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0139] It should be noted that the above comprehensive assessment system of groundwater resources based on big data is embodied in the form of functional units. The term "module" here can be implemented in software and / or hardware forms, and no specific limitation is made thereto.

[0140] For example, a "module" may be a software program, a hardware circuit, or a combination of both that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a proprietary processor, or a group of processors, etc.) for executing one or more software or firmware programs, and a memory, a merged logic circuit, and / or other suitable components that support the described functions.

[0141] Embodiment III

[0142] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the comprehensive evaluation method of underground water resources based on big data described in any of the above embodiments.

[0143] Figure 2 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0144] The processor 1010 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0145] The memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0146] The input / output interface 1030 is used to connect to the input / output module to achieve information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input devices can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices can include a display, a speaker, a vibrator, an indicator light, etc.

[0147] The communication interface 1040 is used to connect to the communication module (not shown in the figure) to achieve communication interaction between this device and other devices. The communication module can achieve communication through a wired method (such as USB (Universal Serial Bus), network cable, etc.) or through a wireless method (such as a mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0148] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0149] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0150] The system of the above embodiment is used to implement the corresponding comprehensive assessment method of groundwater resources based on big data in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0151] Embodiment 4

[0152] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the comprehensive assessment method of groundwater resources based on big data as described in any of the above embodiments.

[0153] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0154] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the comprehensive evaluation method of underground water resources based on big data as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0155] Those of ordinary skill in the art should understand that: the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the concept of the present disclosure, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.

[0156] In addition, for the sake of simplicity of description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (i.e., these details should be completely within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0157] Although the present disclosure has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0158] Thus, the units of the examples described in the embodiments of this application can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A person skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0159] Embodiments of the present disclosure are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A comprehensive evaluation method for groundwater resources based on big data, characterized in that, The method includes: Collecting and processing the groundwater resource data of the area to be evaluated to obtain the processed data; Extracting the long-term trend information related to the groundwater resources based on the processed data; Constructing a groundwater system model based on the long-term trend information; Completing the evaluation of the groundwater resources based on the groundwater system model.

2. The comprehensive evaluation method of groundwater resources based on big data according to claim 1, wherein, Obtaining the groundwater resource data from meteorological stations and hydrological stations and cleaning the data; then, aiming at the time scale difference between the ground monitoring data and the remote sensing data, using the linear interpolation algorithm to align the water level data of the hydrological stations and the precipitation data of the meteorological stations in time to generate a unified time series. The linear interpolation formula is as follows: Among them, t represents the target time point; t prev represents the predecessor time point; t next represents the successor time point; y prev and y next respectively represent the data values corresponding to t prev and t next respectively.

3. The comprehensive assessment method of groundwater resources based on big data according to claim 2, characterized in that Based on the water level values and precipitation values in the unified time series, extracting the spatial coordinate information of the monitoring points; constructing a grid structure in the spatial domain according to the spatial coordinate information of the monitoring points; using the Kriging interpolation algorithm to calculate the interpolation results at the grid points for the water level values and precipitation values respectively. The Kriging interpolation steps include: Let \(Z(x\) i ) be the observed value at the known point \(x\) i , and \(\hat{Z}(x_0)\) be the estimated value at the unknown point \(x_0\). Then the estimated value of Kriging interpolation is given by the following formula: where λ i represents the Kriging weight and satisfies the following conditions: Kriging weight λ i Obtained by solving the following system of equations: Among them, γ(x i , x j ) represents the semivariogram value between the known points x i and x j , and γ(x i , x0) represents the semivariogram value between the known point x i and the unknown point x0; μ represents the Lagrange multiplier.

4. The comprehensive evaluation method of groundwater resources based on big data according to claim 1, wherein Using the STL time series decomposition algorithm to decompose the time series into a trend term and a residual term, and extracting the trend term from the decomposition results; constructing a grid structure in the spatial domain for the trend term related to the groundwater level change.

5. The comprehensive evaluation method of groundwater resources based on big data according to claim 4, characterized in that The STL steps include: Performing LOESS smoothing on the original time series Y(t) to preliminarily separate the trend term T(t). The formula is as follows: T(t) = LOESS(Y(t), span = k where k represents the size of the smoothing window; Subtracting the trend term from the original data to obtain the detrended series Y'(t) = Y(t) - T(t); Directly extracting the long-term trend based on the detrended series; The final residual term R(t) is the difference between the original data and the trend term, that is, R(t) = Y(t) - T(t); Improving the decomposition stability through multiple inner loops and outer loops.

6. The comprehensive evaluation method of groundwater resources based on big data according to claim 1, characterized in that Based on the long-term trend information of the ground monitoring data set and the remote sensing data, using the multi-resolution analysis algorithm to spatially align the DEM elevation data and the geological structure map, and constructing the initial framework of the three-dimensional hydrogeological model; Using the finite element analysis method to perform numerical simulation on the model to obtain the distribution of the hydrogeological characteristics of the model; generating a unified three-dimensional hydrogeological model based on the numerical simulation results.

7. An integrated underground water resource assessment system based on big data, the system being used to implement the method according to any one of claims 1-6, characterized in that, Including: A collection module, an extraction module, a construction module, and an evaluation module; The collection module is used to collect and process the groundwater resource data of the area to be evaluated to obtain the processed data; The extraction module is used to extract the long-term trend information related to the groundwater resources based on the processed data; The construction module is used to construct a groundwater system model based on the long-term trend information; The evaluation module is used to complete the evaluation of the groundwater resources based on the groundwater system model.

8. The comprehensive underground water resource assessment system based on big data according to claim 7, characterized in that, The working process of the collection module includes: obtaining the groundwater resource data from meteorological stations and hydrological stations and cleaning the data; then, aiming at the time scale difference between the ground monitoring data and the remote sensing data, using the linear interpolation algorithm to align the water level data of the hydrological stations and the precipitation data of the meteorological stations in time to generate a unified time series. The linear interpolation formula is as follows: Among them, t represents the target time point; t prev represents the predecessor time point; t next represents the successor time point; y prev and y next respectively represent the data values corresponding to t prev and t next respectively.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed, implements the method according to any one of claims 1 to 6.

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