A method and system for predicting groundwater environmental quality

Abstract

The present invention discloses a method and system for predicting groundwater environmental quality, and relates to the technical field of prediction data processing. The method comprises: obtaining historical observation data and real-time observation data of a solute to be predicted; obtaining boundary conditions, dividing a prediction area into multiple sub-areas based on the boundary conditions, and generating multiple three-dimensional grid points; obtaining geological data corresponding to the three-dimensional grid points, obtaining the influence factor between the solute to be predicted and the geological data based on a correlation model, judging whether the influence factor is within a preset threshold range, and if not, the three-dimensional grid point is used as a grid point to be processed; obtaining a first boundary value, obtaining a second boundary value; calculating and processing the first boundary value and the second boundary value based on a diffusion calculation model, and obtaining longitudinal diffusion and transverse diffusion; obtaining prediction time data, obtaining grid prediction results, obtaining sub-area prediction results, and obtaining target prediction results. The present invention has the advantages of comprehensive accuracy, high prediction efficiency, and strong adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of prediction data processing, and in particular to a method and system for predicting groundwater environmental quality. Background Art

[0002] As an important natural resource, the environmental quality of groundwater is not only directly related to the sustainable development of human health and ecological environment, but also an important source of water for agricultural irrigation, industrial production and residents' daily life. However, with the rapid advancement of industrialization and urbanization, the problem of groundwater pollution has become increasingly serious and has become one of the environmental problems that need to be solved urgently worldwide. In particular, the excessive levels of harmful solutes such as nitrates and heavy metals occur frequently. These pollutants mainly come from the use of agricultural fertilizers, industrial wastewater discharge, and urban domestic sewage leakage. They enter the groundwater system through infiltration, posing a serious threat to the groundwater environment. Long-term drinking of groundwater containing these pollutants may cause human health problems.

[0003] Therefore, accurate prediction of groundwater environmental quality, especially the changing trend of solute concentration, is crucial for formulating effective environmental protection strategies and management measures. It can help decision makers to timely identify potential pollution sources and assess the risk of pollution spread, so as to take targeted measures to prevent further deterioration of pollution and restore the damaged groundwater environment.

[0004] Traditional groundwater environmental quality prediction methods mostly rely on empirical models or simple statistical analysis. These methods may be effective in dealing with simple problems, but they often ignore the complexity and dynamic characteristics of the groundwater environment, such as the diversity of geological structures, the spatiotemporal variability of hydrological conditions, and the effects of microorganisms. As a result, the prediction results lack sufficient scientificity and accuracy, and are difficult to meet the current needs of environmental protection and management. At the same time, model operations require a lot of computing resources and time, which limits its application efficiency in actual environmental management. Therefore, the development of more accurate and efficient groundwater environmental quality prediction models remains an important direction of current environmental science research. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a method and system for predicting groundwater environmental quality.

[0006] A method for predicting groundwater environmental quality, comprising: S1, obtaining the solute to be predicted in the groundwater environment of the prediction area in the past t 0 The historical observation data at the time and the current t 1S2, obtain the boundary conditions of the prediction area according to the historical observation data, divide the prediction area into multiple sub-areas based on the boundary conditions, grid each sub-area and generate multiple three-dimensional grid points; S3, obtain the geological data corresponding to the three-dimensional grid points, obtain the influence factor between the solute to be predicted and the geological data based on the correlation model, and judge whether the influence factor is within the preset threshold range. If the influence factor of the geological data is not within the preset threshold range, select the three-dimensional grid point corresponding to the geological data as the grid point to be processed, so as to obtain multiple grid points to be processed; S4, obtain the three-dimensional grid points corresponding to the geological data at t according to the historical observation data 0 The first boundary value between adjacent grid points to be processed at time t is obtained according to the real-time observation data. 1 The second boundary value between adjacent grid points to be processed at the time; S5, calculate and process the first boundary value and the second boundary value based on the diffusion calculation model to obtain the longitudinal diffusion and lateral diffusion of the adjacent three-dimensional grid points in the corresponding unit time; S6, obtain the prediction time data, obtain the grid prediction result within the three-dimensional grid point according to the prediction time data, the longitudinal diffusion and the lateral diffusion, integrate multiple grid prediction results and obtain the sub-region prediction result, integrate multiple sub-region prediction results and obtain the target prediction result.

[0007] Preferably, S2 includes: S21, obtaining a given head boundary and a given flow boundary of the prediction area based on historical observation data; S22, dividing the prediction area into multiple sub-areas based on the given head boundary and the given flow boundary; S23, gridding each sub-area and generating multiple three-dimensional grid points.

[0008] Preferably, S3 includes: S31, obtaining geological data corresponding to the three-dimensional grid point; S32, obtaining the influence factor between the solute to be predicted and the geological data based on the correlation model; S33, judging whether the influence factor is within a preset threshold range, if the influence factor of the geological data is not within, then starting S34, the preset threshold range includes the maximum value α max and the minimum value α min ; S34, select the three-dimensional grid points corresponding to the geological data as the grid points to be processed, so as to obtain multiple grid points to be processed.

[0009] Preferably, the correlation model in S32 includes: α=β 0 +β 1 x 1 +β 2 x 2 +…+β n x n ; Among them, α is the impact factor, β 0 is the basic value of the impact factor, (β 1 , β 2 , …, βn ) is the contribution of each geological parameter in the geological data to the influencing factor, (x 1 , x 2 , …, x n ) are the values ​​of various geological parameters in the geological data.

[0010] Preferably, the method further comprises S7, wherein S7 comprises: if the influencing factor α of the geological data is greater than α max , then the three-dimensional grid point corresponding to the geological data is judged to be a diffusion grid point; if the influence factor of the geological data α<α min , then the three-dimensional grid point corresponding to the geological data is judged to be a suppression type grid point; the diffusion degree of the three-dimensional grid point is obtained according to the longitudinal diffusion degree and the transverse diffusion degree, and it is judged whether the diffusion degree matches the diffusion type grid point or the suppression type grid point. If it does not match, a prediction alarm signal is issued.

[0011] Preferably, the diffusion calculation model in S5 includes: Among them, D L is the lateral dispersion, k L is the lateral diffusion proportionality constant of the solute to be predicted, C(x 1 ,y 1 ,z,t 1 ) is the lateral value in the second boundary value, C(x 1 ,y 1 ,z,t 0 ) is the lateral value in the first boundary value.

[0012] Preferably, the diffusion calculation model in S5 further includes: Among them, D H is the longitudinal dispersion, k H is the longitudinal diffusion proportionality constant of the solute to be predicted, C(x 1 ,y 1 ,z,t 1 ) is the vertical value in the second boundary value, C(x 1 ,y 1 ,z,t 0 ) is the vertical value in the first boundary value.

[0013] A groundwater environment quality prediction system is also provided, the system comprising: an observation module for obtaining the solute to be predicted in the groundwater environment of the prediction area at t 0 The historical observation data at time t and the 1The real-time observation data at the time; the segmentation and gridding module is used to obtain the boundary conditions of the prediction area according to the historical observation data, divide the prediction area into multiple sub-areas based on the boundary conditions, grid each sub-area and generate multiple three-dimensional grid points; the judgment and selection module is used to obtain the geological data corresponding to the three-dimensional grid points, obtain the influence factor between the solute to be predicted and the geological data based on the correlation model, and judge whether the influence factor is within the preset threshold range. If the influence factor of the geological data is not within the preset threshold range, the three-dimensional grid point corresponding to the geological data is selected as the grid point to be processed, so as to obtain multiple grid points to be processed; the data acquisition module is used to obtain the three-dimensional grid points at t according to the historical observation data. 0 The first boundary value between adjacent grid points to be processed at time t is obtained according to the real-time observation data. 1 A second boundary value between adjacent grid points to be processed at a certain time; a calculation module, which is used to calculate and process the first boundary value and the second boundary value based on the diffusion calculation model to obtain the longitudinal diffusion and the lateral diffusion of the adjacent three-dimensional grid points in the corresponding unit time; a target prediction module, which is used to obtain the prediction time data, obtain the grid prediction results within the three-dimensional grid points according to the prediction time data, the longitudinal diffusion and the lateral diffusion, integrate multiple grid prediction results and obtain sub-region prediction results, and integrate multiple sub-region prediction results and obtain target prediction results.

[0014] A non-temporary computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned groundwater environmental quality prediction method is implemented.

[0015] An electronic device is also provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned groundwater environmental quality prediction method.

[0016] The beneficial effects of the present invention are embodied in:

[0017] By deeply analyzing historical observation data and real-time observation data, combined with geological data, the technical solution can more comprehensively consider the complexity and dynamic characteristics of the groundwater environment, such as the diversity of geological structures, the spatiotemporal variability of hydrological conditions, and the role of microorganisms, thereby improving the scientific nature of the prediction; the relationship between geological data and solute concentration is quantified using the correlation model, and the longitudinal dispersion and lateral dispersion are calculated through the dispersion calculation model, making the prediction results more accurate and reliable. Further, through regional segmentation and grid design, the complex groundwater environment is divided into multiple relatively simple sub-regions and grid points, making the prediction process more refined, while reducing unnecessary calculations and improving prediction efficiency; by analyzing the influencing factors and screening the grid points to be processed, the calculation amount is further reduced, making the prediction process more efficient. Further, by analyzing the influencing factors and screening the grid points to be processed, the technical solution can more accurately identify areas that have a significant impact on the quality of the groundwater environment. These areas are the focus of subsequent prediction and management, thereby improving the pertinence and effectiveness of the prediction; according to the distribution of the grid points to be processed, monitoring and management resources can be more reasonably configured to improve resource utilization efficiency. Furthermore, the technical solution not only predicts the prediction results within adjacent grid points to be processed, but also derives the prediction results of other three-dimensional grid points to form grid prediction results of all three-dimensional grid points, and then converts the grid prediction results into regional prediction results required in practical applications, and finally forms the target prediction results, forming a complete prediction data set, which is convenient for decision makers and environmental managers to apply the prediction results in actual work, formulate effective environmental protection strategies and management measures, and provide a comprehensive scientific basis and technical support for environmental protection and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0019] Figure 1 It is a schematic diagram of steps S1 to S6 of the groundwater environmental quality prediction method of the present invention;

[0020] Figure 2 Schematic diagram of step S2 in the method for predicting groundwater environmental quality of the present invention;

[0021] Figure 3 Schematic diagram of step S3 in the method for predicting groundwater environmental quality of the present invention;

[0022] Figure 4 It is a schematic diagram of steps S1 to S7 in the method for predicting groundwater environmental quality of the present invention;

[0023] Figure 5 The present invention is a block diagram of an electronic device according to an embodiment of the present invention.

[0024] Reference numerals:

[0025] 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - input / output (I / O) interface, 705 - communication component. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0029] like Figure 1 As shown, a method for predicting groundwater environmental quality is provided, the method comprising:

[0030] S1. Obtain the predicted solute in the groundwater environment of the predicted area in the past t 0 The historical observation data at the time and the current t 1 Real-time observation data at all times;

[0031] S2. Obtain boundary conditions of the prediction area according to historical observation data, divide the prediction area into multiple sub-areas based on the boundary conditions, grid each sub-area and generate multiple three-dimensional grid points;

[0032] S3, obtaining geological data corresponding to the three-dimensional grid point, obtaining the influence factor between the solute to be predicted and the geological data based on the correlation model, judging whether the influence factor is within a preset threshold range, and if the influence factor of the geological data is not within a preset threshold range, selecting the three-dimensional grid point corresponding to the geological data as the grid point to be processed, thereby obtaining multiple grid points to be processed;

[0033] S4. Obtain the data at t based on historical observation data. 0 The first boundary value between adjacent grid points to be processed at time t is obtained according to the real-time observation data. 1 The second boundary value between adjacent grid points to be processed at time instant;

[0034] S5, calculating and processing the first boundary value and the second boundary value based on the diffusion calculation model to obtain the longitudinal dispersivity and the transverse dispersivity of the adjacent three-dimensional grid points in a corresponding unit time;

[0035] S6. Obtain prediction time data, obtain grid prediction results within the three-dimensional grid points according to the prediction time data, longitudinal diffusion and lateral diffusion, integrate multiple grid prediction results and obtain sub-region prediction results, integrate multiple sub-region prediction results and obtain target prediction results.

[0036] In this embodiment, it should be noted that in S1, the historical observation data can be the concentration data of a certain solute (such as nitrate, heavy metal, etc.) in the groundwater in the past period of time (such as last month), which is usually obtained through groundwater monitoring wells or sampling points; the real-time observation data is the concentration data of the solute in the groundwater at the current moment, which is also obtained through monitoring wells or sampling points. For example, suppose we want to predict the future concentration of nitrate in groundwater in a certain area, and we have obtained the concentration data of nitrate in groundwater in the past month (t 0 The nitrate concentration data of the current month (t 1 The nitrate concentration data at the time (time) is used as real-time observation data.

[0037] In S2, first, the historical observation data is deeply analyzed to understand the spatial distribution characteristics of the groundwater environment, water flow direction, solute migration law, etc.; based on the data analysis results, the natural boundaries (such as mountains, rivers, etc.) and artificial boundaries (such as administrative divisions, distribution of monitoring stations, etc.) of the prediction area are determined. At the same time, the key factors that may affect the quality of the groundwater environment, such as the location of the pollution source, the groundwater flow rate, the characteristics of the aquifer, etc., are identified, which will also be used as part of the boundary conditions; according to the determined boundary conditions, the prediction area is reasonably divided into multiple sub-areas. The division of sub-areas should take into account the groundwater flow characteristics, the solute migration law and the distribution of monitoring data to ensure that the groundwater environment in each sub-area is relatively consistent. The division of sub-areas helps to refine the prediction accuracy and adopt different prediction strategies and management measures according to the characteristics of different regions; in each sub-area, a grid design is carried out to generate multiple three-dimensional grid points. The density and shape of the grid should be determined according to the specific situation of the sub-area and the prediction needs. Generally, the denser the grid, the more accurate the prediction result, but the greater the calculation amount. Each three-dimensional grid point represents a sample point of the groundwater environment at that location for subsequent data processing and prediction calculation. Among them, through regional segmentation and gridding, the complex groundwater environment is divided into multiple relatively simple sub-regions and grid points, making the prediction process more refined and helping to improve the accuracy of the prediction results; further, the grid design makes the management of groundwater environmental data more convenient, and each grid point corresponds to a set of data, which is convenient for data storage, query and analysis; further, on the basis of gridding, it is easier to apply various complex mathematical models to predict groundwater environmental quality. These models can fully consider complex processes such as groundwater flow and solute migration, further improving the scientificity and reliability of the prediction.

[0038] In S3, for each three-dimensional grid point, the geological data of its corresponding position are collected. These data may include stratum lithology, permeability, porosity, aquifer thickness, groundwater depth, etc. These data are crucial to understanding the groundwater flow and solute migration process. The geological data may come from geological survey reports, hydrogeological maps, long-term observation records of groundwater monitoring stations, etc.; construct or select a correlation model suitable for the prediction area. The model can quantify the relationship between the solute concentration to be predicted and the geological data, identify the key factors affecting the solute migration and distribution, input the collected geological data into the correlation model, and calculate the impact factor corresponding to each grid point. The impact factor reflects the degree of influence of geological conditions on solute concentration; set a preset threshold range to judge the significance of the impact factor. The preset threshold can be determined based on historical data, expert experience or relevant standards. Compare the calculated impact factor with the preset threshold. If the impact factor of a grid point exceeds the preset threshold range, it means that the geological conditions of the grid point have a significant impact on the solute concentration, so the grid point is marked as a grid point to be processed. Among them, by analyzing the influencing factors and screening the grid points to be processed, the areas that have a significant impact on the groundwater environmental quality can be identified more accurately. These areas are the focus of subsequent prediction and management, which helps to improve the pertinence and effectiveness of the prediction; further, in the entire prediction area, not all geological conditions of the grid points will have a significant impact on the solute concentration. By screening the grid points to be processed, unnecessary calculations can be reduced and the prediction efficiency can be greatly improved; further, according to the distribution of the grid points to be processed, monitoring and management resources can be more reasonably allocated. For areas with significant influencing factors, the monitoring frequency and intensity can be strengthened; for areas with less impact, the monitoring and management investment can be appropriately reduced; further, by analyzing the relationship between geological data and solute concentration through the correlation model, the dynamic change process of the groundwater environment can be understood more scientifically. This data-driven method helps to improve the scientificity and reliability of the prediction.

[0039] In S4, based on the observation data at time t0 (historical time point), the concentration gradient, flux or other related parameters of the solute between adjacent grid points to be processed are calculated. These parameters reflect the migration direction and intensity of the solute between the grid points and can be regarded as the first boundary value; based on the real-time observation data at time t1 (current or recent time point), the solute concentration gradient, flux and other parameters between adjacent grid points to be processed are also calculated. These parameters reflect the actual situation of the current solute migration and serve as the second boundary value; the first boundary value and the second boundary value are compared and analyzed to identify changes in the solute migration trend, which may be affected by many factors, such as changes in groundwater flow velocity, changes in aquifer characteristics, input from external pollution sources, etc. Among them, only adjacent grid points to be processed are processed, which realizes further screening of the prediction area. This screening ensures that only those grid points to be processed that are of practical significance to the prediction of groundwater environmental quality are included in the subsequent calculation and analysis, and those isolated grid points without adjacent grid points to be processed are excluded, thereby reducing unnecessary calculations and improving prediction efficiency; further, data comparison between adjacent grid points is more likely to reveal the migration and distribution patterns of solutes in the groundwater environment, because the geological conditions and hydrological conditions between adjacent grid points are usually more similar; further, the first boundary value and the second boundary value reflect the migration trend and intensity of solutes in the groundwater environment, and are important input parameters in subsequent prediction calculations. Accurate boundary values ​​help to improve the accuracy and reliability of prediction results.

[0040] In S5, the first boundary value (historical observation data) and the second boundary value (real-time observation data) obtained in step S4 are used as input parameters and imported into the diffusion calculation model to calculate the longitudinal diffusion and transverse diffusion respectively. Among them, the first boundary value and the second boundary value are calculated and processed using a special diffusion calculation model. This processing method is more scientific and accurate than simple data comparison or empirical judgment; further, the solute transport degree is further divided into longitudinal diffusion and transverse diffusion. This refinement not only helps to more comprehensively understand the solute transport in the groundwater environment, but also provides more accurate support for the subsequent prediction process.

[0041] In S6, according to actual needs, the specific time of prediction is determined, such as one month later. This step clarifies the time range of prediction and provides a time reference for subsequent calculations and simulations. The longitudinal diffusivity and transverse diffusivity obtained in step S5 are used as the change rate of the solute to be predicted in the groundwater environment. These diffusivity parameters reflect the migration and diffusion rate of the solute in different directions. Based on the diffusivity at the prediction time and the boundary value between the adjacent grid points to be processed, the solute concentration in the adjacent grid points to be processed at the prediction time is calculated. This step obtains the solute concentration prediction results of some grid points in the prediction area. For other three-dimensional grid points outside the adjacent grid points to be processed, according to the boundary value of the solute to be predicted, the solute concentration in the adjacent grid points to be processed is calculated. The general change rate is deduced to obtain the solute concentration prediction results of these grid points. This step expands the prediction range to the entire prediction area; the prediction results of adjacent grid points to be processed and other three-dimensional grid points are integrated to obtain the grid prediction results of all three-dimensional grid points. This step forms a complete prediction data set; finally, through the grid division and regional aggregation process in the inversion S2 step, the prediction results of the sub-region and the final target prediction results are obtained in turn. This step converts the grid prediction results into the regional prediction results required in practical applications. Accurate prediction results help to timely discover potential problems in the groundwater environment and provide scientific basis and technical support for environmental protection and sustainable development.

[0042] It should also be noted that, in summary, by deeply analyzing historical observation data and real-time observation data, combined with geological data, the technical solution can more comprehensively consider the complexity and dynamic characteristics of the groundwater environment, such as the diversity of geological structures, the spatiotemporal variability of hydrological conditions, and the role of microorganisms, thereby improving the scientific nature of the prediction; the relationship between geological data and solute concentration is quantified using the correlation model, and the longitudinal dispersion and lateral dispersion are calculated by the dispersion calculation model, making the prediction results more accurate and reliable. Further, through regional segmentation and grid design, the complex groundwater environment is divided into multiple relatively simple sub-regions and grid points, making the prediction process more refined, while reducing unnecessary calculations and improving prediction efficiency; by analyzing the influencing factors and screening the grid points to be processed, the calculation amount is further reduced, making the prediction process more efficient. Further, by analyzing the influencing factors and screening the grid points to be processed, the technical solution can more accurately identify areas that have a significant impact on the quality of the groundwater environment. These areas are the focus of subsequent prediction and management, thereby improving the pertinence and effectiveness of the prediction; according to the distribution of the grid points to be processed, monitoring and management resources can be more reasonably configured to improve resource utilization efficiency. Furthermore, the technical solution not only predicts the prediction results within adjacent grid points to be processed, but also derives the prediction results of other three-dimensional grid points to form grid prediction results of all three-dimensional grid points, and then converts the grid prediction results into regional prediction results required in practical applications, and finally forms the target prediction results, forming a complete prediction data set, which is convenient for decision makers and environmental managers to apply the prediction results in actual work, formulate effective environmental protection strategies and management measures, and provide a comprehensive scientific basis and technical support for environmental protection and sustainable development.

[0043] In one embodiment, the S2 includes:

[0044] S21, obtaining a given head boundary and a given flow boundary of the prediction area according to historical observation data;

[0045] S22, dividing the prediction area into a plurality of sub-areas based on a given head boundary and a given flow boundary;

[0046] S23, gridding each sub-region and generating a plurality of three-dimensional grid points.

[0047] In this embodiment, it should be noted that given head boundaries refer to those boundaries where the head (i.e., water level height plus gravitational potential energy) is known or fixed; these boundaries may be obtained from groundwater level observation data, or may be determined by topography, geological conditions, or human activities (such as pumping, water injection); given head boundaries are crucial for simulating the flow and distribution of groundwater because they directly affect the flow direction and velocity of groundwater. Given flow boundaries refer to those boundaries where the inflow or outflow of groundwater is known; these boundaries may be the interaction zones between natural water bodies such as rivers, lakes, and oceans and groundwater systems, or they may be artificial pumping wells or injection wells; given flow boundaries are very important for simulating the recharge and discharge processes of groundwater because they determine the water balance of the groundwater system.

[0048] In step S21, the process of obtaining a given head boundary and a given flow boundary requires comprehensive consideration of multiple factors. First, historical observation data, including groundwater level, water quality, precipitation, evaporation, etc., need to be collected. Then, based on these data as well as topographic and geological conditions, the recharge, discharge and flow characteristics of groundwater are analyzed. Finally, combined with professional knowledge and experience, determine which boundaries are head boundaries and which boundaries are flow boundaries, and give the corresponding values.

[0049] In step S22, the process of dividing the prediction area into multiple sub-areas based on a given head boundary and a given flow boundary is a complex and critical task. First, it is necessary to analyze the distribution characteristics of the head boundary and the flow boundary, as well as their impact on groundwater flow and solute migration. Then, based on these characteristics and impacts, the prediction area is divided into multiple relatively independent sub-areas. Each sub-area should have similar groundwater flow and solute migration characteristics to facilitate subsequent simulation and prediction. At the same time, the size and shape of the sub-area also need to be considered to ensure the accuracy and efficiency of the simulation.

[0050] In one embodiment, S3 includes:

[0051] S31, obtaining geological data corresponding to the three-dimensional grid points;

[0052] S32, obtaining the influencing factors between the solute to be predicted and the geological data based on the correlation model;

[0053] S33, judging whether the influencing factor is within a preset threshold range, if the influencing factor of the geological data is not within a preset threshold range, then starting S34, the preset threshold range includes the maximum value α max and the minimum value α min ;

[0054] S34, selecting the three-dimensional grid points corresponding to the geological data as the grid points to be processed, thereby obtaining a plurality of grid points to be processed.

[0055] In this embodiment, it should be noted that in S33, in order to screen out geological data that have an important impact on solute prediction, a preset threshold range needs to be set. The preset threshold range is less than or equal to α max and is greater than or equal to the minimum value α min If the influencing factor of a geological data is not within this range, it means that it has little influence on solute prediction and may need further processing or exclusion.

[0056] In one embodiment, the correlation model in S32 includes: α = β 0 +β 1 x 1 +β 2 x 2 +…+β n x n ; Among them, α is the impact factor, β 0 is the basic value of the impact factor, (β 1 , β 2 , …, β n ) is the contribution of each geological parameter in the geological data to the influencing factor, (x 1 , x 2 , …, x n ) are the values ​​of various geological parameters in the geological data.

[0057] In this embodiment, it should be noted that β 0 is the basic value of the influencing factor, which is a constant term, indicating the basic level of the influencing factor when there is no influence of any geological parameters; (β 1 , β 2 , …, β n ) is the contribution of each geological parameter in the geological data to the influencing factor. Each geological parameter has a corresponding coefficient. The size of this coefficient reflects the importance of the geological parameter to the solute migration and distribution; (x 1 , x 2 , …, x n ) is the value of each geological parameter in the geological data. Each geological parameter has a specific value. This value is multiplied by the corresponding coefficient and then added to the basic value to get the influencing factor. The entire correlation model can quantify the complex relationship between the solute to be predicted and the geological data. Through specific mathematical forms, it can be made clear that each geological parameter acts together on the influencing factor, so as to more accurately understand the impact of geological conditions on solute migration and distribution. At the same time, the model is flexible enough to accommodate multiple geological parameters; further, once the coefficient (β 1 , β 2 , …, β n) is determined through data fitting. We can use this model to predict the migration and distribution of solutes under new geological conditions, which is of great significance for practical applications such as groundwater pollution prediction and water resources management.

[0058] In one embodiment, S7 is further included, and S7 includes:

[0059] If the influence factor of the geological data α>α max , then it is judged that the three-dimensional grid point corresponding to the geological data is a diffusion grid point;

[0060] If the influence factor of the geological data is α<α min , then the three-dimensional grid point corresponding to the geological data is judged to be a suppressed grid point;

[0061] The diffusion degree of the three-dimensional grid point is obtained according to the longitudinal diffusion degree and the transverse diffusion degree, and it is judged whether the diffusion degree matches the diffusion type grid point or the inhibition type grid point. If it does not match, a prediction alarm signal is issued.

[0062] In this embodiment, it should be noted that if the influence factor α of the geological data is greater than α max , then the three-dimensional grid point corresponding to the geological data is judged to be a diffusion grid point, which means that at this grid point, the geological conditions are conducive to the diffusion of solutes; if the influence factor of the geological data α<α min , then the three-dimensional grid point corresponding to the geological data is judged to be an inhibition grid point, which means that at this grid point, the geological conditions are not conducive to the diffusion of solutes and may even inhibit the migration of solutes.

[0063] The diffusion degree of the three-dimensional grid point is obtained based on the longitudinal diffusion degree and the transverse diffusion degree. These two parameters describe the diffusion ability of the solute at the grid point and are important indicators for evaluating the diffusion characteristics of the grid point. It is judged whether the diffusion degree matches the diffusion type grid point or the inhibition type grid point. If the diffusion degree matches the type of the grid point (i.e., the diffusion type grid point has a higher diffusion degree, and the inhibition type grid point has a lower diffusion degree), the prediction is considered reasonable. If the diffusion degree does not match the type of the grid point (for example, the diffusion type grid point has a lower diffusion degree, or the inhibition type grid point has a higher diffusion degree), a prediction alarm signal is issued, which indicates that there may be prediction errors or abnormalities in the geological data, and further inspection and adjustment of the prediction model are required.

[0064] In summary, step S7 improves the accuracy and reliability of groundwater solute prediction by classifying three-dimensional grid points, obtaining diffusion degree and performing matching verification. By issuing prediction alarm signals, potential prediction errors or data anomalies can be discovered and handled in a timely manner, thereby ensuring the accuracy and effectiveness of the prediction results.

[0065] In one embodiment, the diffusion calculation model in S5 includes:

[0066] Among them, D L is the lateral dispersion, k L is the lateral diffusion proportionality constant of the solute to be predicted, C(x 1 ,y 1 ,z,t 1 ) is the lateral value in the second boundary value, C(x 1 ,y 1 ,z,t 0 ) is the lateral value in the first boundary value.

[0067] In this embodiment, it should be noted that, in the prediction of groundwater environmental quality, when the dispersion is decomposed into horizontal and vertical dispersion, it is assumed that the horizontal dispersion mainly occurs on the horizontal plane (ie, the XY plane). 1 ,y 1 ,z,t 1 )-C(x 0 ,y 0 ,z,t 0 ) represents the concentration change of the solute on the horizontal plane (XY plane); k L k is the lateral diffusion proportional constant of the solute to be predicted, which is determined according to the specific conditions of groundwater flow and the solute transport mechanism. In the entire diffusion calculation model, the diffusion capacity of the solute in the lateral direction (or a certain horizontal direction) can be quantified by calculating the difference in solute concentration at different times and locations and combining the lateral diffusion proportional constant k L , a specific lateral diffusion value can be obtained to evaluate the lateral diffusion efficiency of the solute; further, in the prediction of groundwater solutes, accurately considering the lateral diffusion of the solute is crucial to the accuracy of the prediction results. This formula provides a calculation method for the lateral diffusion, so that the prediction model can more accurately consider the lateral diffusion behavior of the solute, thereby improving the accuracy of the prediction; further, the groundwater environment usually has complex geological conditions, such as different lithologies, permeabilities, porosities, etc. This formula introduces the lateral diffusion proportional constant k L , which can adapt to the lateral diffusion characteristics of solutes under different geological conditions, making the prediction model more universal and practical.

[0068] In one embodiment, the diffusion calculation model in S5 further includes:

[0069] Among them, D H is the longitudinal dispersion, k H is the longitudinal diffusion proportionality constant of the solute to be predicted, C(x 1 ,y 1,z,t 1 ) is the vertical value in the second boundary value, C(x 1 ,y 1 ,z,t 0 ) is the vertical value in the first boundary value.

[0070] In this embodiment, it should be noted that, in the prediction of groundwater environmental quality, when the dispersion is decomposed into horizontal and vertical directions, it is assumed that the vertical dispersion mainly occurs in the vertical direction (i.e., the Z-axis direction). 1 ,t 1 )-C(x,y,z 0 ,t 0 ) represents the concentration change of the solute in the vertical direction (i.e., Z-axis direction); k H is the lateral diffusion proportional constant of the solute to be predicted, which is determined according to the specific conditions of groundwater flow and the solute transport mechanism. In the entire diffusion calculation model, the calculation of longitudinal diffusivity is similar to that of lateral diffusivity. The formula of longitudinal diffusivity can quantify the diffusion capacity of solute in the longitudinal direction (or depth direction). By calculating the difference in solute concentration at different depths and times, and combining the longitudinal diffusion proportional constant k H , a specific vertical dispersion value can be obtained; further, in the prediction of groundwater solutes, accurately considering the vertical dispersion of solutes is also crucial to the accuracy of the prediction results. By providing a calculation method for the vertical dispersion, the prediction model can more accurately consider the vertical diffusion behavior of solutes, thereby improving the accuracy of the prediction; further, the groundwater environment usually has complex geological conditions, such as different lithologies, permeability, porosity, etc. This formula introduces the longitudinal dispersion proportional constant k H , which can adapt to the longitudinal diffusion characteristics of solutes under different geological conditions, making the prediction model more universal and practical.

[0071] A groundwater environment quality prediction system is also provided, the system is used to implement the groundwater environment quality prediction method in any one of the above embodiments, and the system includes:

[0072] The observation module is used to obtain the predicted solute in the groundwater environment of the prediction area in the past t 0 The historical observation data at the time and the current t 1 Real-time observation data at all times;

[0073] The segmentation and gridding module is used to obtain the boundary conditions of the prediction area according to the historical observation data, divide the prediction area into multiple sub-areas based on the boundary conditions, grid each sub-area and generate multiple three-dimensional grid points;

[0074] A judgment and selection module is used to obtain geological data corresponding to a three-dimensional grid point, obtain the influence factor between the solute to be predicted and the geological data based on the correlation model, and judge whether the influence factor is within a preset threshold range. If the influence factor of the geological data is not within, the three-dimensional grid point corresponding to the geological data is selected as the grid point to be processed, thereby obtaining multiple grid points to be processed;

[0075] The data acquisition module is used to obtain the data at t based on historical observation data. 0 The first boundary value between adjacent grid points to be processed at time t is obtained according to the real-time observation data. 1 The second boundary value between adjacent grid points to be processed at time instant;

[0076] A calculation module, used to calculate and process the first boundary value and the second boundary value based on a diffusion calculation model to obtain the longitudinal dispersivity and the transverse dispersivity of adjacent three-dimensional grid points in a corresponding unit time;

[0077] The target prediction module is used to obtain the prediction time data, obtain the grid prediction results within the three-dimensional grid point according to the prediction time data, longitudinal diffusion and lateral diffusion, integrate multiple grid prediction results and obtain sub-area prediction results, integrate multiple sub-area prediction results and obtain target prediction results.

[0078] Regarding the groundwater environment quality prediction system in the above-mentioned embodiment, the specific manner of performing the operation has been described in detail in the implementation method of the groundwater environment quality prediction method, and will not be elaborated here.

[0079] Figure 4 is a block diagram of an electronic device for a method for predicting groundwater environmental quality according to an exemplary embodiment. Figure 4 As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0080] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned groundwater environmental quality prediction method. The memory 702 is used to store various types of data to support the operation of the electronic device 700, and these data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0081] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned groundwater environmental quality prediction method.

[0082] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned groundwater environmental quality prediction method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned groundwater environmental quality prediction method.

[0083] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned groundwater environmental quality prediction method when executed by the programmable device.

[0084] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0085] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0086] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for predicting groundwater environmental quality, characterized in that: The method comprises: S1. Obtain the historical observation data of the solute to be predicted in the groundwater environment of the prediction area at the past time t0 and the real-time observation data at the current time t1; S2. Obtain boundary conditions of the prediction area according to historical observation data, divide the prediction area into multiple sub-areas based on the boundary conditions, grid each sub-area and generate multiple three-dimensional grid points; S3, obtaining geological data corresponding to the three-dimensional grid point, obtaining the influence factor between the solute to be predicted and the geological data based on the correlation model, judging whether the influence factor is within a preset threshold range, and if the influence factor of the geological data is not within a preset threshold range, selecting the three-dimensional grid point corresponding to the geological data as the grid point to be processed, thereby obtaining multiple grid points to be processed; S4, obtaining a first boundary value between adjacent grid points to be processed at time t0 according to historical observation data, and obtaining a second boundary value between adjacent grid points to be processed at time t1 according to real-time observation data; S5, calculating and processing the first boundary value and the second boundary value based on the diffusion calculation model to obtain the longitudinal dispersivity and the transverse dispersivity of the adjacent three-dimensional grid points in a corresponding unit time; S6. Obtain prediction time data, obtain grid prediction results within the three-dimensional grid points according to the prediction time data, longitudinal diffusion and lateral diffusion, integrate multiple grid prediction results and obtain sub-region prediction results, integrate multiple sub-region prediction results and obtain target prediction results.

2. The method for predicting groundwater environmental quality according to claim 1, characterized in that: The S2 includes: S21, obtaining a given head boundary and a given flow boundary of the prediction area according to historical observation data; S22, dividing the prediction area into a plurality of sub-areas based on a given head boundary and a given flow boundary; S23, gridding each sub-region and generating a plurality of three-dimensional grid points.

3. The method for predicting groundwater environmental quality according to claim 1, characterized in that: The S3 includes: S31, obtaining geological data corresponding to the three-dimensional grid points; S32, obtaining the influencing factors between the solute to be predicted and the geological data based on the correlation model; S33, judging whether the influencing factor is within a preset threshold range, if the influencing factor of the geological data is not within a preset threshold range, then starting S34, the preset threshold range includes the maximum value α max and the minimum value α min ; S34, selecting the three-dimensional grid points corresponding to the geological data as the grid points to be processed, thereby obtaining a plurality of grid points to be processed.

4. The method for predicting groundwater environmental quality according to claim 3, characterized in that: The correlation model in S32 includes: α=β0+β1x1+β2x2+…+β n x n ; where α is the impact factor, β0 is the basic value of the impact factor, (β1, β2, …, β n ) is the contribution of each geological parameter in the geological data to the influencing factor, (x1, x2, …, x n ) are the values ​​of various geological parameters in the geological data.

5. The method for predicting groundwater environmental quality according to claim 4, characterized in that: Also included is S7, wherein S7 includes: If the influence factor of the geological data α>α max , then it is judged that the three-dimensional grid point corresponding to the geological data is a diffusion grid point; If the influence factor of the geological data is α<α min , then the three-dimensional grid point corresponding to the geological data is judged to be a suppressed grid point; The diffusion degree of the three-dimensional grid point is obtained according to the longitudinal diffusion degree and the transverse diffusion degree, and it is judged whether the diffusion degree matches the diffusion type grid point or the inhibition type grid point. If it does not match, a prediction alarm signal is issued.

6. The method for predicting groundwater environmental quality according to claim 1, characterized in that: The diffusion calculation model in S5 includes: Among them, D L is the lateral dispersion, k L is the lateral diffusion proportional constant of the solute to be predicted, C(x1,y1,z,t1) is the lateral value in the second boundary value, and C(x1,y1,z,t0) is the lateral value in the first boundary value.

7. The method for predicting groundwater environmental quality according to claim 6, characterized in that: The diffusion calculation model in S5 also includes: Among them, D H is the longitudinal dispersion, k H is the longitudinal diffusion proportional constant of the solute to be predicted, C(x1,y1,z,t1) is the longitudinal value in the second boundary value, and C(x1,y1,z,t0) is the longitudinal value in the first boundary value.

8. A groundwater environmental quality prediction system, characterized in that: The system is used to implement the groundwater environmental quality prediction method according to any one of claims 1 to 7, and the system comprises: The observation module is used to obtain the historical observation data of the solute to be predicted in the groundwater environment of the prediction area at time t0 and the real-time observation data at time t1; The segmentation and gridding module is used to obtain the boundary conditions of the prediction area according to the historical observation data, divide the prediction area into multiple sub-areas based on the boundary conditions, grid each sub-area and generate multiple three-dimensional grid points; A judgment and selection module is used to obtain geological data corresponding to a three-dimensional grid point, obtain the influence factor between the solute to be predicted and the geological data based on the correlation model, and judge whether the influence factor is within a preset threshold range. If the influence factor of the geological data is not within, the three-dimensional grid point corresponding to the geological data is selected as the grid point to be processed, thereby obtaining multiple grid points to be processed; A data acquisition module, used to acquire a first boundary value between adjacent grid points to be processed at time t0 according to historical observation data, and to acquire a second boundary value between adjacent grid points to be processed at time t1 according to real-time observation data; A calculation module, used for calculating and processing the first boundary value and the second boundary value based on a diffusion calculation model to obtain the longitudinal dispersivity and the transverse dispersivity of adjacent three-dimensional grid points in a corresponding unit time; The target prediction module is used to obtain the prediction time data, obtain the grid prediction results within the three-dimensional grid point according to the prediction time data, longitudinal diffusion and lateral diffusion, integrate multiple grid prediction results and obtain sub-area prediction results, integrate multiple sub-area prediction results and obtain target prediction results.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the groundwater environmental quality prediction method described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the groundwater environmental quality prediction method described in any one of claims 1 to 7.

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